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Engineering, Civil
Landslides are a common and challenging geohazard that may be caused by earthquakes, rainfall, or manmade activity. Various monitoring strategies are used to safeguard populations at risk from landslides. This task frequently depends on the utilization of remote sensing methods, which include the observation of Earth from space, laser scanning, and ground-based interferometry. In recent years, there have been notable advancements in technologies utilized for monitoring landslides (i.e., subsurface systems).
- landslide monitoring
- subsurface monitoring
- investigational monitoring
- wireless monitoring
- early warning monitoring
1. Introduction
The practice of landslide monitoring is the systematic observation and collection of data to enhance the understanding and analysis of this geological event. Any effective monitoring methodology should include the following goals: consistent and systematic data collection, the use of appropriate equipment, accurate timing of measurements, and the use of proper analytic techniques (i.e., how to interpret the collected data). These goals can respond to the following questions: (1) what has to be monitored (such as displacement, stress, and pore water pressure), (2) the number of devices to be utilized, and (3) the frequency and data collection methods. These goals and inquiries may be used to establish the budget, resources, planning, and monitoring system [1].
Geotechnical investigations have been conducted for years to discover the stability conditions of slopes under various geological and environmental circumstances [2,3]. To answer the first aforementioned question, landslide monitoring is used to track and measure slope stability parameters, such as ground movement (surface movement, subsurface movement, heights, and cracks), subsurface water conditions (depth of water table, pore water pressure, soil suction, and soil moisture), and climatic parameters (rainfall, snowfall, temperature, and humidity). These factors can subsequently be used in landslide prediction approaches, which were not the focus of this work [4,5]. The number, type, and location of sensors are determined by the local geology, subsurface conditions, and landslide area in answer to the second question [6]. Concerning the third question, comprehending the geographical and temporal distributions of these factors is critical for realizing landslide dynamics and controlling the associated risk [7].
Determining the most effective monitoring system requires a thorough understanding of the reasons that generate events (initial conditions). For instance, the use of tilt measurement may not be suitable for translational landslides or slow-moving landslides since the occurrence of tilting is improbable under such conditions. Similarly, when deep soil underneath an installation site becomes saturated, it might lead to landslides, which can damage topsoil moisture sensors [8]. Another example is if the effective rainfall value is the cumulative value of one day, then collecting data at 15-minute intervals may not be necessary [1]. Landslides are classified as shallow or deep-seated based on the depth of the slip surface. Both of these types of landslides have distinct features and produce varying degrees of damage. As a result, defining the type of landslide and estimating the potential risk of a prospective landslide by measuring the depth of the sliding surface are both necessary [1]. The monitoring of landslides is divided into phenomena, investigation, and performance categories. The change in the slope over time in a particular geologic location is monitored using phenomena. To ascertain the temporal and physical parameters of an identified landslide, investigation monitoring is performed. A stabilizing system that is already in place can be evaluated for efficacy via performance monitoring [1].
Monitoring systems can be classified into surface and subsurface techniques [9]. The former cannot follow internal changes, but the latter can. Thus, this research focused mostly on subsurface monitoring approaches, where the optimal criteria for a monitoring system, according to the literature, should have the following features: provide real-time data; high sensitivity; high spatiotemporal resolution; cost-effectiveness; low power consumption; reliability; scalability; not affected by signal noise, such as temperature effects; limit the uncertainty caused by missing data; and be suitable for both shallow and deep landslides, as well as harsh environment conditions (i.e., the device should be coated) [10,11,12].
2. Subsurface Landslide Monitoring Techniques
2.1. Surface Displacements
Surface displacement can be measured using various techniques, such as total stations, global positioning system (GPS) [38], robotized total station (RTS) [39], light detection and ranging (LiDAR) [16], synthetic aperture radar (SAR), interferometric synthetic aperture radar (InSAR) [40], persistent scatterer interferometry (PS-InSAR) [41], differential synthetic aperture radar (SAR) interferometry (DInSAR) [42], ground-based InSAR (GB-InSAR) [43], terrestrial laser scanning [44], global navigation satellite system (GNSS) [19], aerial photography [45], and satellite remote sensing techniques [46]. These techniques can only provide information about ground movements and are useful for wide-area surveillance. However, such instruments cannot determine the subsurface physical mechanism of landslides [14,15,16,19,20,47,48].
The GPS technique works on the basis that GPS satellites give navigation positioning signals for space resection measurement, hence calculating the 3D coordinate of the measuring point. However, high-power radio-transmitting stations and high-voltage transmission lines have a significant impact on GPS [9]. Furthermore, one GPS monitoring site costs approximately USD 6000 for a single device [49]. A robotized total station (RTS) is beneficial for distributing information about the present landslide condition and can give near real-time data, such as the ADVICE system [50]. However, false alarms owing to data inconsistencies caused by instrument faults, physical changes at the measurement location, and/or extremely local/shallow reactivations are always possible [50].
Remote sensing techniques (space-borne, aerial, and terrestrial surveys) can monitor broad regions without physical contact with the ground, though these technologies are expensive, have low resolution, and have difficulty collecting real-time data [7]. Although InSAR offers a better spatial resolution than GPS, it is hampered by atmospheric delay. Although PS-InSAR, which is an advanced radar interferometric measurement type that is representative of DInSAR, offers good accuracy, it is impacted by shadows and dense vegetation [9]. In satellite- and airborne-based SAR applications, the technique of differential synthetic aperture radar (SAR) interferometry (DInSAR) has been utilized to monitor vast regions of longer-distance landslides. DInSAR-based systems estimate displacements in millimeters by measuring phase changes between pairs of ground pictures acquired at various time intervals. The drawbacks are that the monitoring time intervals are excessive, ranging from hours (airborne) to weeks (satellite), and that daily or hourly monitoring is costly. Ground-based SAR (GB-InSAR), which is utilized over ranges ranging from a few hundred meters to a few kilometers, was created to alleviate the aforementioned difficulties. However, when a large bandwidth signal (for a high resolution in the range direction) is employed, a costly instrument is required [51,52]. The 3D laser scanning method has the added advantage of rapidly collecting (every 5 min) field deformation topography data with high accuracy and resolution [53]. However, the performance of a laser-light-based device is also influenced by weather conditions, such as severe fog or snow/rain [51]. By employing radio waves to scan a large area of the slope and provide temporal pictures, radar devices can track the movement of the slope. Nevertheless, there are drawbacks to using radar systems, such as the inability to monitor the slope in the event of snowfall or rain or when the line of sight (LoS) between the scanning device and the target slope is blocked. The technology is also useless for providing real-time warnings of sudden movements (i.e., seconds) since it takes several minutes to hours to scan the slope and interpret the photos to detect changes in the slope state [28].
A global navigation satellite system (GNSS) has been suggested to eliminate the requirement for line of sight (LOS) and to offer high-precision 3D monitoring. However, this technology has a significant maintenance cost and time requirements, as well as the presence of a single point of failure [54]. LiDAR-derived digital elevation models (DEMs) can quantify minor displacements across broad regions. Nevertheless, choosing an appropriate DEM resolution (i.e., pixel size, grid resolution, grid size) for constructing susceptibility maps is sometimes difficult since the scale of observation influences the evaluation, results, and interpretation [55].
The gradual degradation of slope stability generates landslides, and the sliding surface plays a vital role in landslide evolution. To illustrate, the landslide initiation is generated from the subsurface deep layers: only when the slope mass changes sufficiently can the slope surface deform macroscopically [56]. Surface monitoring systems may detect millimeter-scale deformation and can monitor wide regions with good spatial resolution and 3D capabilities, which is appropriate for landslide susceptibility, vulnerability, and risk maps for planning. The considerable time these systems need to spend returning to the same location, however, prevents these systems from offering real-time monitoring [57] and is not adequate for rapid landslides [29,58]. Therefore, there is a need for improvements in the small-scale subsurface monitoring of landslides [59,60].
2.2. Subsurface Monitoring
Landslide deep displacement monitoring, where landslide initiation begins, is important for early warning forecasting and stability assessment [4,5,9]. In addition to displacement monitoring, subsurface monitoring techniques provide the added benefit of tracking internal forces, stress, moisture content, and temperature changes. Furthermore, such methods can provide early signs for emergencies.
2.2.1. Movement-Monitoring Devices
Extensometer Device
A conventional wire extensometer can provide a continual check of surface movement that may lead to a landslide. During emergencies, data can be obtained at regular intervals of 1–3 h, yet during routine situations, measurement intervals are 6 h. However, to obtain meaningful readings, the wire must be continually tensioned [2]. The quantity and rate of movement are measured and calculated manually within a centimeter range. However, key events might be missed if measurements are not obtained on time. To overcome the aforementioned issues, potentiometric extensometers detect displacement using a variable resistance mechanism, where a movable arm makes electrical contact along a fixed resistance strip. This type has the advantage that the wiring can be buried [61]. Crawford et al. [62] used a cable-extension transducer, which is a stainless-steel cable connected to a potentiometer housed in a protective casing, where the voltage output is then transformed to a linear absolute displacement. Fibreglass extensometers were initially placed (drilled horizontally in boreholes) in the S landslide to provide more precise data [2]. This type of extensometer is suited for rock slide applications since it can detect movement in the millimeter range [2].
Nevertheless, the wire extensometer has the drawback of collecting data at the landslide surface, making it hard to analyze the deep displacement distribution, and being overly expensive, costing approximately EUR 1000 for a single monitoring site [64]. Moreover, this technique is a single-point measuring technique and cannot provide distributed monitoring [8,65]. With technological advancement, wire extensometers may now deliver real-time and high-resolution measurements. Wire extensometers can be linked to particular data logging units and can be combined with other sensors for landslide dynamic analysis. While this technique is more suited for translational landslides, it can additionally be used in roto-translational landslides and has been validated in field experiments with land shifts ranging from 12 mm to 150 mm [63].
Inclinometers
Compared with extensometers, inclinometers have the benefit of measuring deep displacement with a spatial vertical resolution of 0.5–1 m [66,67]. Measurements are collected regularly by installing a single inclinometer into grooved vertical pipes installed in deep boreholes to analyze their deformation. Later investigations employed numerous analog inclinometers or a series of digital in-place inclinometers positioned at different depths inside these pipes for continuous measurement. Inclinometers, however, are difficult to install, laborious, lack sensitivity, and are vulnerable to environmental dangers [10,61]. Using an inclinometer to determine the precise location of sliding surfaces is limited by the spatial vertical resolution [68,69], especially when the shear band thickness is small [35]. Automating inclinometers is impractical because the wiring restricts the number of inclinometers that may be installed in a region, resulting in limited area spatial resolution [70]. This approach is impracticable for measuring significant lateral deflections for two reasons: the limited displacement range [9] and the high expense of guide casing (approximately 30 USD/m) [47] (600 USD/inclinometer) [71]. Electric-powered inclinometers are the most often used equipment for measuring subsurface displacements. However, in real applications, this technique (i.e., electric-powered inclinometers) suffers from limited stability and durability, poor resistance to electromagnetic interference, high gravity dependency, and significant signal loss for long-distance transmission [72].
Thus, inclinometers are appropriate for landslides that move very slowly to slowly [73] and have a thick shear bandwidth (refer to Figure 11), for which a lengthy monitoring interval and low spatial resolution would be sufficient. Intelligent monitoring for landslides has been widely studied [74]. Recently, numerous research studies have been conducted to overcome the inclination drawbacks by improving the spatiotemporal resolution, lowering the cost, giving real-time data, and enabling wireless data transmission.
Time Domain Reflectometry (TDR)
TDR is a relatively new method that, similar to radar, employs a coaxial cable and a cable tester [70,71]. A TDR device is made up of a TDR step pulse generator, an oscilloscope (or receiver), and a transmission line coupled to a multiplexer for multipoint and multifunction usage through various types of sensing waveguides [69,75]. An electrical pulse is sent down a coaxial cable that has been grouted into a borehole by the cable tester. The pulse is reflected when it encounters a crack or distortion in the cable. The reflection is represented by a “spike” in the cable signature. The relative magnitude and rate of displacement, as well as the position of the deformation zone, can be measured instantly and precisely [69].
This approach, however, faces challenges when quantifying the amount of displacement [68,78]. This is because numerous factors influence displacement, including (1) cable resistance, (2) soil–grout–cable contact, and (3) interaction and shear bandwidth. As a result, each cable has unique calibration measurement features. TDR is not suitable for multi-landslide failure zones [9]. TDR is not recommended for fast-flowing landslides or difficult-to-access steep slopes [29]. This method works best on rock slopes, and it is less effective on soft soils [70]. Reflection interference from closely spaced sliding surfaces requires additional investigation.
For the following reasons, this system is preferable over inclinometers: (1) low cost (in the United States, high-quality coaxial cable costs 13.5 USD/m, and the connection for installing each monitor hole costs USD 100.35) [47], (2) automated real-time data-collecting capability [9], (3) high spatial resolution to detect the exact location of the sliding surface (0.05 m), (4) TDR is capable of capturing the dynamics of shear deformation due to its unique characteristic of high temporal resolution (minute range) [69], and (5) the capability of measuring small displacements (0.5 mm) [77].
Acoustic Emission (AE)
The majority of AE monitoring studies are qualitative, determining the status of a slope based on the level of AE. A passive waveguide (i.e., grouted waveguide) is typically used for rock slope monitoring, whereas an active waveguide is used in soil slope monitoring by employing a steel pipe and granular backfill. The ringdown count (RDC), which is a frequent AE characteristic, is the number of times the AE signal amplitude surpasses the preset voltage threshold throughout a period. A certain frequency band of 20–30 kHz, which is the dominant frequency range produced from an active waveguide, is where AE signals are solely gathered to remove external noise [56,79,80]. Previous research employed metal tubes, which are problematic for large deformations because they are prone to failure from shear or bending.
AE technology is characterized by its dependability, low cost, great precision, and ability to be performed in real time. AE is sensitive to minor changes in displacement and velocity, allowing it to detect extremely slow-moving landslides with a high measuring range, outperforming both TDR and inclinometers. To illustrate, because of the hardness and brittleness of the inclinometer body, it can be bent excessively when the local shear displacement reaches approximately 50 mm, resulting in device failure.
Optical Fiber System
Optical fiber technology has become more important, supplying a significant amount of the world’s internet, television, and telephone networks. Because of the sensitivity of the propagating light signal to disturbances, such as strain and temperature change, optical fiber cables have been effectively employed as sensing devices that can transport high-quality data across large distances at remarkable speeds. Fibre-optic (FO) sensors can be inserted directly into the ground; linked to a stabilizing structure or reinforcement; or coupled to traditional monitoring equipment, such as an inclinometer [82].
First, Brillouin optical time domain reflectometry (BOTDR) was developed. However, BOTDR cannot detect strain and temperature at the same time [47]. A few years later, optical time domain reflectometry (OTDR) was developed as a distributed sensing technology [65] and considered a viable alternative to address the aforementioned limitation. Figure 13 depicts the essential components of the OTDR. A laser transmitter releases a short signal into the fiber, the timing of which is set by an electronic delay generator. The light is reflected to the source, and the delay generator measures the time delay relative to the start time of the pulse. Each time delay value is associated with a specific position along the fiber. Thus, in principle, backscattering and back reflections may be calculated in terms of their magnitude and pinpointed in terms of the distance along the connection [61]. Figure 14 is an example of a return signal obtained by using an OTDR system. Extrinsic and intrinsic sensors are the two types of OTDR displacement sensors. Extrinsic sensors that employ optical fiber as a transmission medium include reflexive, transmission, and interferometric sensors. Intrinsic sensors are commonly bend-loss-type sensors in which the optical fiber bends and creates macro bending loss, which is not favorable for long-distance optical data transmission. Fiber-optic displacement sensors based on the macro bending loss concept are intensity-based fiber-optical sensors, meaning that light transmission loss increases abruptly with large curvatures [83]. During light transmission, Rayleigh, Raman, and Brillouin scatterings occur and cause the light intensity to be attenuated. Rayleigh backscattering is the most powerful of the three [59,60], and OTDR can detect its light intensity as a function of time [47,58].
Figure 13. Basic elements of OTDR (modified after Aulakh et al. [61]).
Figure 14. OTDR system sample return signal trace (modified after Aulakh et al. [61]).
The first single optical fiber can detect deformation with a high beginning accuracy of 0.3 mm; nevertheless, it has a limited sliding distance of 3.6 mm and a dynamic range of 3.3 mm [47]. The first generation of optical fiber has an unsatisfactory spatial distribution of twenty meters (one optical fiber was used to pass through a whole capillary steel pipe, with a suitable length of fiber left outside the pipe); it used a base material of PVC with no filler material within. To increase the spatial resolution, Aulakh et al. [61] developed a micro bend resolution-enhancer method that can improve the OTDR resolution up to 10 times. To increase the measuring range, Zhu et al. [47] created the second generation “combined optic fibre transducer” (COFT), with the base material being expansile polyester ethylene (EPS).
However, COFT finds it challenging to locate potential sliding surfaces and collect dispersed measurements of complex landslides, particularly the arrangement and interaction between multiple sliding surfaces. Therefore, a quasi-distributed measuring system and prospective sliding surfaces, especially on rock slopes, can be achieved using a parallel-series connected fiber-optic displacement sensor (PSCFODS) with bowknot bending modulation that makes it more bendable and sensitive [59,60,83]. The greatest value was 34 mm, and the initial measurement was 0.98 mm. Different lengths of capillary steel pipes were arranged to determine the sliding surface location with a spatial resolution of 250 mm.
OTDR, BOTDR, and C-OTDR have limited spatial distributions, which limit their usage. The spatial resolution has risen from 1 m for the Brillouin optical time domain reflectometry (BOTDR) technique to 0.1 m for the Brillouin optical time domain analysis (BOTDA) approach due to the rapid growth of fiber optic technology. However, their BOTDA installation is difficult, as BOTDA requires an incident laser from both ends of the optical fiber [78].
Previous studies, however, were based on the micro bending theory or the beam theory, which does not consider mass movement kinematics. Zhang et al. [58] investigated the mechanism of distributed optical strain sensing (DFOSS) via a kinematic method through a parametric study on the sliding directions, shear zone width, and shear displacement. This approach simplifies the deformed sensing optical fiber (SOF) to be an arc or straight line, but the deformed shape might be rather complex since it is determined using the shearing angle, soil profile, grouting quality, etc. In contrast to simplistic techniques that assume the deformed shape to be rectangular or an arc, a more generic shear displacement calculation method (accumulative integral method (AIM)) is presented herein that does not presuppose the shape of the DSS cable [35].
To improve the stress transmission between the sensing cable and the surrounding soil, Ivanov et al. [82] concluded that the position of the sensors perpendicular to the sliding direction is preferable where better soil cable coupling is achieved. However, in such cases, these fibers are subjected to high shearing stresses, which limit their usage to shallow, slow-moving landslides.
A fiber Bragg grating (FBG)-based inclinometer can monitor quasi-distributed deformation at various depths (i.e., spatial vertical resolution) [72]. The FBG is a wavelength-selective filter. An FBG sensor will reflect light with a center wavelength matching the Bragg condition. Strain modifies the Bragg wavelength by causing the grating periodicity to expand or contract. Wang et al. [72] adopted a prototype monitoring system consisting of nine FBG-based inclinometers. This system has a spatial resolution of one meter and can detect horizontal displacement with high accuracy in the millimeter range.
While FBG-based sensors offer discrete strain and temperature readings at predetermined places and are capable of providing dynamic measurements, this technology is unable to offer monitoring over a wide area. While BOTDR/A monitors strain and temperature change throughout the entire cable length, they are only capable of static monitoring, which can be over many kilometers (i.e., the distributed fiber length) [82].
While numerous authors highlight the low cost and long lifespan of the sensor itself (i.e., optical fiber cables), the truth is quite contrary: costs may reach tens of thousands of euros and are often built to function in a controlled environment, such as a laboratory [82]. Optical fiber technology has not been used extensively for a long period in challenging outdoor environments.
Electromechanical Tilt Sensors
Fiber optics are widely used to improve performance, whereas electromechanical sensors appear to be a viable way to obtain both precision and a wide range of data [92]. However, because microelectromechanical systems (MEMS) are electronic devices, they must be charged, and their output signals must be transmitted outside by electric cables or wireless networks, which cannot be too lengthy, or the signals will be compromised by noise. Optical fiber sensors may be an alternative to electrically powered devices since they may be operated remotely and are powered by optical fiber cables, such as FBGs, without the requirement for electricity [48].
Extensometers can only detect surface displacements, while inclinometers can only give subsurface displacement in one direction [8,65]. Both approaches require expert labor to install and maintain such instruments. Moreover, determining the landslide direction with both inclinometers and extensometers is challenging. Tilt measurements can indirectly detect two-dimensional shear deformation and determine the rotational direction in terms of tilt angle and sign convention [95]. When combined with MEMS and WSNS techniques, tilt sensors can provide the following benefits: (1) minimal cost, (2) simple installation (no deep boreholes required), and (3) real-time data [96].
Nevertheless, the inclination measurement accuracy is influenced by a variety of error causes, including noise, drift, and offset. Similar to Ruzza et al. [64] and to overcome the aforementioned accuracy limitation, Wielandt et al. [100] developed a low-cost, long-term wireless sensor that consists of three-axis accelerometers (MEMS) and a temperature sensor to monitor the change in sensor inclination, surrounding soil deformation, and subsurface temperature to reduce the draft error. The equipment achieved a resolution of 0.39 mm, a 95% confidence interval of ±0.73 mm per meter of probe length, a depth spatial resolution of 100 mm, and an acceleration range of ±2 g.
Strain Gauge Sensors
Strain gauges can achieve cost-effective conditions compared with inclinometers. The strain gauge measures the strain experienced by the soil layer during slope instability and can be connected to a WSN to provide real-time data [102]. A strain gauge translates force, pressure, tension, weight, and other variables into a change in electrical resistance that can be measured. Before a landslide, strain gauges are used to quantify the micro-movements within the unstable soil slope [103].
Acceleration Sensors
The majority of monitoring system components involve sensors for assessing soil tilting or displacement; however, acceleration sensors have yet to be commonly utilized. Independent of the trigger, acceleration sensors can be utilized to detect any movement (Giri et al., 2018). These sensors can be manufactured based on the technology of optical fibers [90], inertial measuring units (IMUs), and MEMS [8,28], in which sensor reading data can be transferred via wiring or wireless networks. By supplying a significant voltage differential Vout, the accelerometer reads a biaxial acceleration change.
4.2.2. Force and Stress Monitoring
The majority of widely available monitoring and warning systems rely on displacement, which is affected by a variety of variables, such as rainfall, temperature, and soil moisture. Landslides, on the other hand, can be predicted in advance by monitoring the earth pressure and the sliding force in near real time, as the best metric for identifying the kinematic characteristics of landslides is the sliding force [9].
Earth pressure cells (EPCs) and seismic vibrators can be buried in the soil layer to measure the variation in earth pressure. Ma et al. [53] utilized an EPC to measure the earth pressure using experimental tests, where this device has a capacity of 500 kPa. Similarly, Askarinejad et al. [73] employed EPCs with an accuracy of 1 kPa, a range of 0–500 kPa, and a frequency of 100 Hz. Yunus et al. [107] developed a new smart wireless sensor to measure seismic vibrations. A set of weights placed on the cone transforms the loudspeaker (Visaton FR8 8-ohm) into a vibration sensor. When the loudspeaker detects seismic waves, the weights remain in place and apply stress on the cone, changing the distance between the coil and the base of the center pole. As a result, an output voltage is created at the loudspeaker’s output terminal.
4.2.3. Water and Temperature Monitoring
There are three types of near-surface water monitoring: surface water monitoring, groundwater monitoring, and precipitation monitoring. Precipitation monitoring is primarily concerned with rainfall, whereas surface water monitoring covers near-surface soil moisture. Groundwater monitoring includes measures such as the groundwater level, pore water pressure, water temperature, water quality, and soil water content.
Precipitation Monitoring
Rain gauges are classified into mechanical, optical, electrical, visual, and radar types, with the mechanical type, such as the traditional tipping bucket rain gauge (TBR), being the most extensively used and accurate. The mechanical type has the benefit of directly measuring the amount of rainfall, whereas the other methods adopt indirect measurements [112].
Near-Surface Water Monitoring
Near-surface technologies, such as gamma-ray attenuation [113], soil heat flux [114], and ground penetration radar (GPR) [32], are costly, susceptible to noise, and incapable of providing deep moisture and temperature information [115]. Soil moisture may be monitored at the regional scale using remote sensing techniques, such as satellite retrievals, which are restricted to near-surface soil moisture. Although satellite-based soil moisture estimations [34] have been found to be beneficial for identifying landslide-prone situations, their application in landslide early warning systems is restricted by the coarse spatial resolution and the lower temporal resolution [116]. It should be highlighted that this study focused on subsurface monitoring techniques, which exclude the aforementioned investigations.
Subsurface Water Monitoring
Subsurface water monitoring approaches include site investigation and laboratory sampling, optical fiber and acoustic emission methods, electrical permittivity tools, geophysical techniques, and MEMS and IoT technology applications. Subsurface water monitoring includes soil moisture content (volumetric water content), pore water pressure (suction pressures), and groundwater level variation. It should be emphasized that soil moisture is a critical parameter for assessing and monitoring natural hazards, such as landslides. The volumetric water content response to rainfall events is more immediate than that of pore water pressure and retains its maximum value for some time before slope failure [115].
Temperature Monitoring
For deep-seated landslides, where thermal sensitivity plays a crucial role in the stability of the slide, Seguí and Veveakis [30,31] created a theoretical equation to quantify and decrease the uncertainty of the model parameters and use the temperature in the shear band. The feasibility of this study was confirmed using field tests, where a thermometer was employed to determine the potential thermal sensitivity of the material located in one of the most crucial regions of a landslide (the shear band). However, this system requires prior investigation to determine the location of the shear band.
4.2.4. Warning Techniques
Previous monitoring systems primarily focused on the accuracy of acquired data for improved prediction based on geological parameter monitoring; nevertheless, these approaches lack scene information and deal with emergency scenarios [127]. Thus, regardless of the precision and quantification of the monitored parameter, warning monitoring systems can offer an early warning indication. Sensors for moisture or slope deformation are point sensors that are exclusively sensitive to changes in physical characteristics in their immediate surroundings. As a result, several sensors are necessary to cover a large possible landslide region. This might drastically raise project costs, but limiting the number of sensors would reduce the landslide forecast efficiency, making the system itself doubtful. A promising technique where geological engineering uses damage-free studies of geotechnical parameters based on data delivered by elastic waves was developed [97].
Chen et al. [97,98,99] constructed two physical models (small and large) to study the behavior of the elastic wave velocity in rainfall-induced landslides. The elastic wave velocity dropped continually in response to moisture content and deformation, and there was a clear increase in the rate of wave velocity decline when failure commenced.
These studies (elastic wave), however, lacked a cost estimate for field deployment, and the location (i.e., near the toe, middle, or close to the crest) of the elastic wave transmitter/receiver in the field is unknown. According to experimental and laboratory studies by Chen et al. [98], the toe is best for monitoring waves. Exciting device selection is a complex problem (for example, powerful waves can harm slope stability, while weak waves may be influenced by noise) [97].
Previous research on acoustic emission (AE) was restricted to high-frequency signals in which AE is generated when a disordered material is subjected to stress, shear, or failure [129]. Low-frequency AE signals, such as infrasonic signals, have received less attention. In contrast to traditional monitoring systems (i.e., point systems, such as deformation systems or subsurface water systems), infrared signals can monitor several landslides within a local region with high penetration capacity and low attenuation.
Motakabber and Ibrahimy [131] developed a wireless (almost 100 m sensor node distance) differential capacitor-type sensor using mathematical models and simulations. This sensor overcomes the limitations of capacitor-type sensors, which are noise and complex thermal adjustments, and has the advantages of being simple, robust, reliable, and cheaper. This system consists of an underground pretension cable with a capacitor gauge sensor attached at one end. When soil starts to deform, the formation of a force-on-force plate, as well as the pretension wire, results in a change in the differential capacitor.
Through numerical (ANSYS) and experimental indoor experiments for soil deformation monitoring, Kuang [133] investigated a unique chemiluminescence-based approach. Chemiluminescence devices have reactants that are kept in distinct compartments and produce light instantly when distorted, making them easily detectable by inexpensive optoelectronics (i.e., light-dependent resistors (LDRs)). No power is needed for chemiluminescence to operate, as it is entirely passive. This device costs 1 USD/unit, where the dimensions of one unit are 400 mm in length and 15 mm in diameter. However, this system is sensitive (i.e., vulnerable) to small deformations ranging between 0.43 mm and 24.99 mm. Thus, the position of the system can be changed based on the expected soil movement to overcome this issue. However, it should be emphasized that the effectiveness of most warning techniques for predicting landslides is still being researched.
4.3. Wireless Sensing Network (WSN)
Wired-based systems have apparent disadvantages, such as difficulty in wiring and construction in danger zones, human-caused destruction, and natural catastrophe damage [134]. This significantly increases the effort necessary for installation and operation, both financially and in terms of time. Furthermore, data are often conveyed without any preprocessing, necessitating the storage and delivery of massive packages of redundant data linked to a given node of observation before it can be processed and correlated [7]. Thus, wireless sensor networks have several benefits over traditional techniques, including the following: (1) the ability to gather and analyze multipoint distributed data, (2) the ability to cover a large area with little wiring expenses, (3) they are energy efficient since they can run for months, (4) incorporation with existing equipment [7,28,92,134], (5) installation without preexisting infrastructure, and (6) low vulnerability to environmental impacts [54]. Other appealing characteristics of WSNs include self-organizing and self-healing capabilities, high fault tolerance, and ease of interaction with web-based technologies [6].
The term “wireless sensor network” (WSN) refers to a wireless network that employs a linked sensor to track the state of physical or environmental factors [111]. The terms “wireless sensor” and “smart transducer” refer to sensors that are outfitted with microcontrollers to give intelligence and network capabilities [107]. It should be noted that WSNs can collect data and move information in real time; however, the precision and accuracy of the measurements are mostly dependent on the monitoring mechanism used [54,134].
The sensor nodes, gateway, and monitoring center comprise the landslide wireless monitoring system. Sensor nodes provide data from the field to the administration of the landslide monitoring center. The gateway is responsible for connecting the node to the internet. The monitoring center is in charge of data storage, processing, and analysis. WSNs are primarily composed of hardware and software systems. The wireless communication modules included in the sensors are commonly long-term evolution (LTE), Bluetooth, ZigBee, Wi-Fi, LoRa, etc. Among these, LoRa modulation technology is an appropriate technological solution for node communication [136]. In a WSN, several sensor nodes structure the linked networks into a certain architecture.
However, wireless sensing networks have some challenging issues, such as energy consumption, memory size, and communication issues [54,137]. To illustrate, the monitoring activity is more accurate if sensor nodes are regularly awakened to sample data, but it has a significant impact on the sensor node lifetime. As a result, it is necessary to develop a flexible system that considers the detection performance, cost, and energy savings [127].
4.3.1. Energy Consumption Issues
The energy consumption issues are directly related to the amount of transmitted data, sampling rate, and number of sensors, and they are indirectly related to the adopted threshold and prediction accuracy [102]. There are three approaches to preserving the system’s energy: (1) lowering the frequency at which data are collected, (2) limiting the number of active sensors [103], and (3) improving the self-rechargeability of the power system. As a result, it is critical to comprehend, assess, and construct a threshold that minimizes the sampling rate while maintaining high accuracy. For example, WSNs are capable of making decisions themselves, and data transmission can be minimized during dry seasons [6]. During the rainy season, solar power tends to decline rapidly owing to the increase in the data frequency rate. As a result, limiting energy consumption becomes an overriding concern for the network’s long-term operation, particularly when landslides are imminent (for example, heavy-rainfall-induced landslides [4,5,102].
Regarding innovative thresholds that are responsible for data frequency lowering, Rosi et al. [7] adopted a threshold that consisted of four stages (quiet stage, quiet-to-motion stage, motion stage, and motion-to-quiet stage), where the sensor starts to collect, store, and send data in the second and third stages only. In the first and last stages, the system shuts down the connection to save energy and maintain accuracy. Ramesh and Rangan [102] established a threshold system with four levels: rainfall (mm), moisture (%), and pore pressure (kPa). The lowest level was (20, 0, 0), while (0, 100, 60) was the highest. At the lowest and highest thresholds, the threshold increased the battery lifespan to 43 days and 63 days, respectively. Another approach was used to reduce further energy use, in which data collection for moisture and pore pressure began after the threshold for precipitation was reached. For the lower and higher thresholds, this threshold could prolong life to 150 days and 400 days, respectively. Additional thresholds can be used, wherein only the sensor with the highest value continues to function while the others go offline.
For electromechanical low-power usage sensors, Yang et al. [49] developed a MEMS system that adopted a temporal resolution of 10 min on rainy days and 1 h in dry seasons using four Standard Power 7 Alkaline batteries that can power a single sensor device for more than a year. Abraham et al. [93,94] used a MEMS system with four C-size alkaline batteries and a sensor that sleeps for 10 min after transmitting a signal, extending the battery life in the field.
Solar cell systems have been widely used in power-monitoring systems [63,126]. The sensor unit may run semi-permanently without changing the batteries by installing an optional solar battery, which costs approximately USD 5 [49,52,101]. Lin et al. [132] used a unique self-powered wireless sensing method called a zigzag-structured triboelectric nanogenerator (Z-TENG), which has an open-circuit voltage of 2058 V and a short-circuit current of 154 µA. This system has the benefit of using the energy from moving vehicles to power the TTEFBS system. Wireless power transfer (WPT), as a breakthrough method for charging electronic devices, has drawn a significant deal of attention since Tesla’s initial WPT experiment at the beginning of the twentieth century to eliminate constant battery changes and charging using plugs. Magnetic resonance wireless power transfer (MR-WPT) offers several benefits, including long coupling distances, high output power, high transfer efficiency, minimal influence from nonferromagnetic barriers, and minimal impact on the human body [138].
4.3.2. Communication Issues
The system precision is affected by the distance between nodes; the shorter the distance is, the higher the precision. To clarify, the precisions for 110 m, 60 m, and 10 m internode distances were 0.2 m, 0.03 m, and 0.009 m, respectively [54]. Latupapua et al. [111] concluded that the response time rises with the increase in the distance between nodes and station, where the response times were 1.91, 2.98, 3.09, and 4.47 s for distances of 20, 40, 60, and 80 m, respectively, while the monitoring center did not gather any data for distances of 100 m. Rosi et al. [7] adopted a new antenna capable of connecting nodes up to 80 m apart. Mucchi et al. [54] developed a WI-GIM wireless MEMS system with an internode distance between 60 and 90 m. Yang et al. [49] developed a wireless device that can transmit data up to 300 m. Jeong et al. [92] implemented a self-organizing mesh network topology and a time-synchronized mesh protocol (TSMP) to overcome the communication environment of a hilly region; it was found to be more dependable and adaptable than the star network design. Wireless underground sensor networks (WUSNs) cannot be implemented using the current electromagnetic (EM)-based wireless communication technology because it does not match the application requirements of the underground environmen.
4.3.3. Data Loss and Size Issues
A large amount of environmental and geophysical data collected by a variety of sensors and systems suffers from high levels of ambiguity, noise, and missing data. To illustrate, the nature of the observed monitoring data fluctuates according to external triggering (rainfall, earthquakes, etc.), and missing records are highly expected to occur throughout the monitoring. Data loss may indicate that the program’s goals were not achieved, which is more than just a negative situation. Blahůt et al. [142] revealed that while measuring displacement, missing measurements accounted for approximately 24.6%. To address the aforementioned shortcomings, time-series analysis was used, which included various statistical approaches, such as regression models, to comprehend the underlying context of data points or to make predictions based on prior behavior. A second-order polynomial can be used to approximate trend data representing creep behavior: it should be noted that the displacement can be divided into creep displacement (simple trend) and periodic displacement (complex trend) [4]. Paired adaptive regressors for cumulative sum (PARCS) were utilized for periodic data. Sumathi and Anitha [143] designed a lossless landslide-monitoring (LLM) system. During the data collection and processing phases, two algorithms were used. A modified gray wolf optimization method was employed in the first phase, and an iterative dichotomize-3 (ID-3)-based decision-making strategy was applied in the second phase. This method boosted the delivery ratio by 30%. de Souza and Ebecken [144] adopted artificial intelligence models to predict missing data using principal component analysis (PCA) combined with artificial neural networks (ANNs).
4.4. Physical and Prototype Systems
It is challenging to see any purpose for implementing a monitoring program if the devices being used cannot record data with the required frequency, accuracy, or precision stated by its objectives. To illustrate, the difference between precision and accuracy is visualized (the bull’s eye targets) in Figure 30 [149]. In the following sections, both experimental and prototype modeling are discussed for the better simulation and investigation of landslide monitoring systems.
Figure 30. Bull’s eye visualization for accuracy and precision conditions.
4.4.1. Experimental Models
Laboratory model testing is a powerful technique that plays a vital part in landslide engineering studies. Although time-consuming, scale-model testing has helped to advance our understanding of landslide causes and processes. The most accurate way to analyze landslides is via laboratory model studies. This is due to the possibility of continuous monitoring of the water content of the soil slope, as well as subsequent deformation, which allows for the management of the soil characteristics and boundary conditions [97,99].
4.4.2. Prototype Working Process
The installation of the subsurface monitoring system was illustrated by Chuan et al. [9]. The process consists of (1) hole drilling, (2) monitoring system installation, (3) drilling pipe installation, (4) sensor checkup, (5) powering the system, (6) data analysis, and (7) data processing. First, the depth of the borehole (i.e., sensor tip) is determined using drilling machinery based on the depth of the sliding mass and geological soil profile. The drill pipe is then removed, and the sensors are mounted in accordance with the design objectives. An initial examination is required to ensure that the sensor is linked to the subsurface soil. The next step is to turn on the system and begin data collection and storage. The data are then processed and displayed before being examined. Adopting a probable prediction model based on the processed data is the last stage, as illustrated in Figure 31.
Figure 31. Subsurface monitoring system flowchart.
4.4.3. Field Systems
Before implementing any monitoring system, it should be noted that field investigation and laboratory testing are required [92,153]. A site study can offer basic information regarding landslide classification, soil profile and features, sliding surface location, etc. The field investigation program includes a (1) surface geological survey, (2) borehole survey, (3) test pit, (4) standard penetration test (SPT), (5) field density test, (6) field permeability test, (7) surface permeability test, (8) cone penetration test (CPT), (9) refraction seismic survey, and (10) multichannel analysis of surface waves (MASW). Furthermore, laboratory tests for assessing soil attributes include (1) soil classification, (2) water content, (3) Atterberg limits, (4) grain-size distribution, and (5) soil water characteristic curves (SWCC) [92]. Because the previous research strategy is time consuming, subsurface studies employing the geoelectrical resistivity method may be a feasible option. Geoelectrical resistivity is calculated by passing an electric current through a current electrode into the ground and measuring the differential potential of a region.
The monitoring locations can be selected using four spatial distribution methods: random, matrix, vulnerable, and hybrid. The monitoring locations in a random method are installed at nonspecific random places on a landslide-prone slope. The whole area of deployment is split into a matrix of NxN cells in the matrix method, and one monitoring probe is placed in each cell of the matrix. Monitoring stations are placed in vulnerable (i.e., critical zones) zones identified during the site investigation, topography mapping, and soil testing in the vulnerability technique. In the hybrid technique, both the matrix and vulnerable approaches are used, i.e., start with the matrix and then adjust the locations of the monitoring devices based on the most critical (i.e., vulnerable) locations [10].
Most of the preceding methods can offer a single measurement (displacement, soil moisture, etc.); however, the possibility of high false alarms limits its usage. Thus, such data should be correlated with other monitoring data, such as rainfall or soil moisture, to reduce such effects. As a result, multimonitoring systems are strongly advised. Multimonitoring systems can be produced by combining the individual systems shown above or by designing a single sensor node with many functionalities. Multifunctional sensor devices that make use of MEMS sensors and WSNs are now commercially available.
5. Conclusions
Surface-monitoring techniques can offer information regarding near-surface movement, moisture content, and other physical information. Such strategies offer the following benefits: (1) they can offer millimeter-level 3D coordinates, and (2) they can provide distributed monitoring data with high spatial resolution across large regions. These studies, however, have the disadvantages of (1) obtaining real-time data is difficult and expensive; (2) they have a coarse resolution; and (3) they are impacted by severe fog, snow/rain, atmospheric delay, dense vegetation, and shadow. As a result, these methodologies are appropriate for creating landslide susceptibility, risk, and vulnerability maps [4,5]. However, such maps cannot provide early warning indications or predict disasters.
These objectives can only be met by a knowledge of the inner mechanism and monitoring of subsurface conditions. Extensometers have a high temporal resolution (36 mm/s) and precision (0.011 ± 0.0083 mm). Nonetheless, this is a single-point surface-movement-monitoring system. These characteristics are appropriate for translational landslides. By detecting subsurface displacement, conventional inclinometers outperform extensometers. The limited spatial vertical resolution (0.5–1 m) restricts its use, particularly for thin shear bandwidth. Unlike traditional inclinometers, TDR can enable exact monitoring of the sliding surface’s position (spatial resolution of 0.05 m). When compared with the inclinometer guide enclosure, the coaxial cable costs approximately 55% less. However, measuring the displacement is difficult. The moderate rigidity of inclinometers restricts their use in monitoring minor movements. AE techniques are sensitive to minor deformation and are best suited for slow-moving landslides. Optical-fiber-based inclinometers have recently gained much interest. This technology combines all of the previously mentioned benefits, including high initial measurement (0.98 mm), measuring range (36 mm), low cost (0.45 USD/m), and high spatial resolution of 10 mm. FBG may be coupled with BOTDA to monitor both the strain and temperature across a large region. Because of the restricted monitoring range, this method is best suited for rock landslides. This method is limited in its application since it is based on wire connections.
Tilt sensors have the benefit of being able to determine the direction of a landslide with two-dimensional deformation with an accuracy of 0.0025° and a measurement range of −30° to +30°. The depth of the sensor rod must be carefully calculated: small and long rods are suited for circular slip surfaces, while long rods should penetrate the rock layer for shallow landslides, as short rods are not effective. Many biaxial tilt sensors may be combined to form a multimodule system (inclinometer) with a spatial resolution of 100 mm, an accuracy of 0.73 mm, and a cost of 70 EUR/m. Tilt sensors, on the other hand, are point sensors and cannot extract deformations in areas where there is no inclination (i.e., translational landslides). Inclinometers based on strain gauges can detect micro-displacement. Soil deformation sensors are excellent for quick landslides since they have a low stiffness when compared with other approaches. SDS can detect micro-displacement (1 mm) throughout a range of 0 to 25 mm. The Strain Gauge Deep Earth Probe (SG-DEP sensor) can give 360-degree directional measurements and is ideal for both shallow and deep landslides, as well as harsh conditions. Acceleration sensors can detect slope movement independently of external triggers. This approach is appropriate for translational quick landslides without tilting, where linear acceleration is the most influential characteristic.
In addition to subsurface monitoring, the best technique to assess the kinematic characteristics of landslides is to monitor the sliding force; however, its installation is complicated. Rainfall monitoring is critical since it is regarded as the primary triggering factor. Based on multiple triboelectric nanogenerator (TENG) units, a self-powered wireless sensor with a high measurement range (0 to 288 mm/d) and resolution (5.5 mm) was recently created. The subsurface moisture state illuminates the antecedent effect of rainfall. The drying technique for determining soil moisture in a laboratory has great accuracy; nonetheless, it is a labor-intensive procedure necessitating massive investigation work for a wide area. It is challenging for AE techniques to link soil moisture with acoustic waves. FBG can detect up to 37% volumetric water content. UHF radio-frequency identification (RFID) sensors can detect soil moisture levels as high as 16%. The smart aggregate (SAs) approach can monitor soil moisture up to 30%. Geophysical methods, such as electrical resistivity tomography (ERT), can offer information about wide areas rather than single spots that provide plot-scale soil moisture variation. The spatial resolution of a region might range from meters to decimeters. This technology can detect soil moisture up to 2 m deep.
MEMS and IoT sensors that can be linked to WSNs can be used to overcome wiring and installation problems. MEMS can be used as an inclinometer, tiltmeter, volumetric water content sensors, etc., with the primary goal of low cost and simple installation and maintenance. These sensors are more suited for shallow landslides. The SitkaNet sensor may represent a realistic solution to construct a deep spatially distributed moisture content sensor for approximately 1000 USD per node. In the shear band, temperature sensitivity is critical for slope stability. Likewise, for shallow strata, the surface temperature can offer an early warning when moving landslides have greater temperatures than stable zones. Multifunction nodes offer a feasible alternative to single-function nodes in terms of cost and false alarm rate.
Regardless of the quantification of subsurface characteristics, warning signs can offer indicators to cope with emergency circumstances. Elastic waves and low-frequency infrasonic signals can provide warning indications when internal mechanisms (such as soil moisture, deformation, matric suction, and effective stresses) change. However, implementing such a strategy is rather difficult. Other warning systems, such as differential capacitors, triboelectric force and bend sensors (TTEFBS), and chemiluminescence-based approaches are currently under development.
Data may be obtained manually; however, critical events may be missed. Natural disasters can cause damage to wire- or cable-based systems. Wireless networks can address the aforementioned limitations by linking several sensors for broad monitoring areas. However, WSNs are limited by power consumption issues, communications issues, and data loss and size issues. For power consumption issues, building a sleep threshold, reducing the number of sensors, and using rechargeable techniques can overcome this dilemma. Regarding communication issues, the communication distance between sensor nodes can affect the precision and the response time for the transmitted data. Available techniques can provide an inter-distance between 90 and 300 m, while the magnetic induction communication transceiver can be buried up to 5.28 m into the ground. Missing data can be obtained using a variety of mathematical methods. Laboratory-scale testing provides an appropriate approach to understanding the mechanism of landslides in a safe and low-cost setting. Prior to the field installation of the monitoring system, a thorough site study is needed. The monitoring system is placed under four conditions: random, matrix, vulnerable, or hybrid. The vulnerable placement allows for reasonable monitoring where the monitoring points are placed in critical locations.
This entry is adapted from the peer-reviewed paper 10.3390/rs16020385
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