Optimized Deep Learning Model for Flood Detection: History
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Contributor:
Andrzej Stateczny
,
Hirald Dwaraka Praveena
,
Ravikiran Hassan Krishnappa
,
Kanegonda Ravi Chythanya
,
Beenarani Balakrishnan Babysarojam

The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control.

  • combined Harris hawks shuffled shepherd optimization algorithm
  • convolutional neural network
  • flood prediction
  • median filter
  • satellite images

1. Introduction

In general, flooding poses a serious risk to automobiles and disrupts traffic, leading to swept-away automobiles, injuries, and fatalities among passengers [1][2]. Cities have to create flood maps to lessen the risk throughout weather events with remote monitoring of flooding. However, urban environments are very complicated, with streams at submeter resolutions, narrow and short-lived floods, and ponding [3], which cause the flooding degree to be discontinuous. These three factors make mapping urban flood events difficult. Applying strategies to hydrologic models used in flood forecasting [4][5] is difficult due to these factors. More conventional approaches are used to map urban flooding as well as flood risk. High-resolution hydrologic modeling is efficient at small scales, but with current technology, it is difficult to obtain the computational power. Extremely accurate inputs are needed to precisely estimate the urban flooding at community level [6]. The necessity for less computation-intensive mapping or forecasting techniques for flooding is demonstrated by these constraining variables.
In order to increase control over flood risks, remote sensing offers the advantage of [7][8] large-scale flooding without the requirement of extremely precise inputs as well as computation-intensive methods. Investigations on flood prediction have been conducted in areas like aerial imagery or satellite imagery. The radar is an active sensor that monitors the Earth’s surface regardless of the level of cloud cover [9]. SAR data are determined to be unsuitable for mapping floods in urban areas because of its shadow and stopover in a complex urban context [10]. Rapid flood mapping is usually accomplished using unsupervised detection approaches because there is less ground truth data available in real-world applications. However, through additional information and improved procedures, some accomplishments have been seen in employing SAR for applications related to urban floods, with the integration of improved data and new image processing methods.

2. Optimized Deep Learning Model for Flood Detection Using Satellite Images

In 2019, Huynh et al. [11] developed a novel processing technique-based time series analysis in order to estimate the floodable regions in the Mekong Delta by means of modern satellite images. These were important concerns in which experts were interested, and in order to map flood zones and control flood risks, as well as observe and detect changes in floodable regions, researchers used image LiDAR/image RADAR.
In 2020, Goldberg et al. [12] used operational weather satellites to discuss mapping, assessing, and forecasting floods generated by snowmelt and ice jams. It was anticipated that satellite-based flood forecasts would enable more quantitative forecasts on the breakdown timing and areas of floods caused by ice jams and snowmelt when combined with temperature readings. With the help of this study’s attempts and results, the flood products from VIIRS and GOES-R offered wide-end users’ dynamic detection and forecasting of floods caused by snowmelt and ice jams.
In 2020, Moumtzidou et al. [13] expanded an approach to recognize floods in a time series and examined the prediction of flood events by comparing two successive Sentinel-2 images. DCNN, which was fine-tuned and pre-trained, was utilized to detect floods by testing various input series of three water-sensitive satellite bands. The proposed strategy was measured against various remote sensing-based baseline CD methodologies. The suggested approach helped the crisis management authority determine and assess the impact of the floods more accurately.
In 2021, Du et al. [14] created a ML-based method with the help of Google Earth Engine for mapping and predicting the daily downscaling of 30-m flooding. Utilizing retrievals from SMAP and Landsat along with rainfall predictions from the NOAA global prediction model, the CART approach was developed and trained. Independent verification revealed a strong correlation (R = 0.87) between FW forecasts over randomly chosen dates and Landsat readings.
In 2021, Mateo-Garcia et al. [15] developed a flood segmentation method that ran effectively on the accelerator on the PhiSat-1 and generated flood masks to be transferred rather than the raw images. The current PhiSat-1 mission from the ESA attempted to make this notion easier to demonstrate by offering hardware capabilities to carry out onboard processing and incorporating a power-constrained ML accelerator along with the software to execute customized applications.
In 2021, Paul and Ganju [16] suggested a pseudo-labeling method for semi-supervised learning that gained steadily better accuracy by obtaining trust estimates via U-Net ensembles. Specifically, a cyclical method was used, consisting of three stages:
(1) 
Training an ensemble method of multiple U-Net frameworks through a high-confidence hand-labeled dataset given;
(2)
 Filtering out poorly generated labels;
(3)
 Combining the generated labels with an early-obtainable, strong-confidence hand-labeled dataset.
In 2021, Roland Lowe et al. [17] showed how topographic deep learning can be used to estimate the depth of an urban pluvial flood. This study looks into how deep learning was set up to predict 2D supreme depth maps during urban flood events as accurately as possible. This was accomplished by adapting the U-NET neural network design, which is frequently used for image segmentation. The results reveal reduced CSI to 0.5 and RMSE scores (0.08 m) for screening procedures. However, the double-peak event’s increased forecast inaccuracy highlighted a weakness in accurately catching the dynamics of flood occurrences.
In 2021, Marcel Motta et al. [18] showed how to anticipate urban floods utilising both machine learning and geographic information systems. In order to create a flood prediction system that can be utilised as a useful tool for urban management, this project will combine machine learning classifiers with GIS methodologies. This method created reasonable risk indices and factors for the occurrence of floods and was useful for developing a long-term smart city plan. Random forest was the most effective machine learning model, with an accuracy of 0.96 and a Matthew’s correlation coefficient of 0.77. However, the higher sensitivity resulted in a larger false-positive rate, indicating that the system threshold needs to be further adjusted.
In 2021, Mahdi Panahi et al. [19] proposed two deep learning neural network architectures, CNN and RNN, for the spatial prediction and mapping of flood possibilities. A geospatial database with information for previous flood disasters and the environmental parameters of the Golestan Province in northern Iran was built in order to design and validate the predictive models. The SWARA weights were used to train the CNN and RNN models, and the characteristic method was employed to validate them. According to the findings, the CNN model performed marginally better at forecasting future floods than RNN (AUC = 0.814, RMSE = 0.181). However, they frequently produce incorrect results and have a tendency to shorten the complicated form of flood disasters.
In 2021, Susanna Dazzi et al. [20] proposed the concept of predicting the flood stage by means of ML models. Using mostly upstream stage observations, this work evaluated the ML models’ propensity to forecast flood stages at a crucial gauge station. All models offered adequately accurate predictions up to 6 h in advance (e.g., root mean square error (RMSE) 15 cm and Nash–Sutcliffe efficiency (NSE) coefficient > 0.99). Additionally, the outcomes imply that the LSTM model should be used because, while taking the most training time, it was reliable and accurate at forecasting peak standards.
In 2021, Xinxiang Lei et al. [21] presented convolutional neural network (NNETC) and recurrent neural network (NNETR) models for flood prediction. After that, flooded areas were spatially divided at random in a 70:30 ratio for the construction and validation of flood models. By means of area under the curve (AUC) and RMSE, the models’ prediction accuracy was verified. The validation findings showed that the NNETC model’s prediction performance (AUC = 84%, RMSE = 0.163) was marginally superior to the NNETR model’s (AUC = 82%, RMSE = 0.186). To create a flood danger map for metropolitan areas even though the output contains a relative error of up to 20% (based on AUC).
In 2022, Georgios I. Drakonakis et al. [22] presented supervised flood mapping via CNN by employing multitemporal Sentinel-1 and Sentinel-2. In this paper, OmbriaNet was presented, which completely depends on CNN. It uses the temporal variations between flood episodes that are retrieved by various sensors to detect variations among permanent and flooded water areas. This paper demonstrated how to build a supervised dataset on new platforms, assisting in the management of flood disasters. But, CNNs have been applied to supervised classification with limited spatial scale and delivering acceptable outcomes.
In 2022, Kamza et al. [23] developed remote sensing and geographic information system (GIS) technology to examine the changes in the northeastern Caspian Sea coastline and forecast the severity of flooding with rising water levels. The proposed method (remote sensing and GIS) for making dynamic maps was being used to track the coastline and predict how much flooding would occur in a certain area. As a result, it was possible to forecast the flooding of the northeast coast using a single map. But this research contains a variation in sea level and continuous ecosystem deterioration current, which reduces the prediction quality.
In 2022, Tanim et al. [24] suggested a novel unsupervised machine learning (ML) method for detecting urban floods that included the Otsu method, fuzzy rules, ISO-clustering techniques, and was focused on change detection (CD) methodology. In order to create and train ML algorithms for flood detection, this research integrated data from remote sensing satellite imagery with information from ground-based observations generated from police department reports about road closures. By utilizing satellite images and lowering the risk of flooding in transport design and urban infrastructure development, this systematic technique is ideally helpful for other cities in danger of urban flooding as well as for identifying nuisance floods.
In 2022, Peifeng Li et al. [25] presented a deep learning algorithm (CNN–LSTM) to directly compute runoff in two-dimensional rainfall radar maps. The NSE results from the research mentioned above are lower or on par with those from this study’s CNN–LSTM model. If the training data are carefully chosen, the model’s Nash–Sutcliffe efficiency (NSE) value for runoff simulation throughout the periods could exceed 0.85. When the extreme values were missed in the one-fold training dataset, the CNN–LSTM miscalculated the extreme flows.
According to the aforementioned research, the information that was retrieved from the data typically exceeds the limitations of the measurements, even if the satellite data have a high degree of uncertainty. However, the data have not been properly analysed in order to determine how various remote sensing techniques and analyses were used to locate the flooded area.
Until now, many approaches have been implemented to evaluate remote sensing-based systems. Unsupervised deep learning algorithms are more reliable because they are faster, use less training data, take less time to run, and also provide superior computing efficiency. Therefore, a hybrid deep learning model named DHMFP–CHHSSO is proposed for flood detection, which provides greater performance with a lower data count and processing time for improved fast flood mapping. The suggested technique helps other towns in danger of urban flooding by using satellite data to reduce the flood risk of transportation projects and urban structure development in addition to flood detection.

This entry is adapted from the peer-reviewed paper 10.3390/rs15205037

References

  1. Luis, C.; Alvarez, M.; Puertas, J. Estimation of flood-exposed population in data-scarce regions combining satellite imagery and high resolution hydrological-hydraulic modelling: A case study in the Licungo basin (Mozambique). J. Hydrol. Reg. Stud. 2022, 44, 101247.
  2. Mohammed Sarfaraz, G.A.; Siam, Z.S.; Kabir, I.; Kabir, Z.; Ahmed, M.R.; Hassan, Q.K.; Rahman, R.M.; Dewan, A. A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction. J. Environ. Manag. 2023, 326, 116813.
  3. Roberto, B.; Isufi, E.; NicolaasJonkman, S.; Taormina, R. Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions. Hydrol. Earth Syst. Sci. 2022, 26, 4345–4378.
  4. Kim, H.I.; Han, K.Y. Data-Driven Approach for the Rapid Simulation of Urban Flood Prediction. KSCE J. Civ. Eng. 2020, 24, 1932–1943.
  5. Kim, H.I.; Kim, B.H. Flood Hazard Rating Prediction for Urban Areas Using Random Forest and LSTM. KSCE J. Civ. Eng. 2020, 24, 3884–3896.
  6. Keum, H.J.; Han, K.Y.; Kim, H.I. Real-Time Flood Disaster Prediction System by Applying Machine Learning Technique. KSCE J. Civ. Eng. 2020, 24, 2835–2848.
  7. Thiagarajan, K.; Manapakkam Anandan, M.; Stateczny, A.; Bidare Divakarachari, P.; Kivudujogappa Lingappa, H. Satellite image classification using a hierarchical ensemble learning and correlation coefficient-based gravitational search algorithm. Remote Sens. 2021, 13, 4351.
  8. Jagannathan, P.; Rajkumar, S.; Frnda, J.; Divakarachari, P.B.; Subramani, P. Moving vehicle detection and classifi-cation using gaussian mixture model and ensemble deep learning technique. Wirel. Commun. Mob. Comput. 2021, 2021, 5590894.
  9. Simeon, A.I.; Edim, E.A.; Eteng, I.E. Design of a flood magnitude prediction model using algorithmic and mathematical approaches. Int. J. Inf. Tecnol. 2021, 13, 1569–1579.
  10. Aarthi, C.; Ramya, V.J.; Falkowski-Gilski, P.; Divakarachari, P.B. Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction. Sustainability 2023, 15, 1637.
  11. Huynh, H.X.; Loi, T.T.T.; Huynh, T.P.; Van Tran, S.; Nguyen, T.N.T.; Niculescu, S. Predicting of Flooding in the Mekong Delta Using Satellite Images. In Context-Aware Systems and Applications, and Nature of Computation and Communication; Vinh, P., Rakib, A., Eds.; Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; Springer: Cham, Switzerland, 2019; p. 298.
  12. Mitchell, D.G.; Li, S.; Lindsey, D.T.; Sjoberg, W.; Zhou, L.; Sun, D. Mapping, Monitoring, and Prediction of Floods Due to Ice Jam and Snowmelt with Operational Weather Satellites. Remote Sens. 2020, 12, 1865.
  13. Anastasia, M.; Bakratsas, M.; Andreadis, S.; Karakostas, A.; Gialampoukidis, I.; Vrochidis, S.; Kompatsiaris, I. Flood detection with Sentinel-2 satellite images in crisis management systems. In Proceedings of the 17th ISCRAM Conference, Blacksburg, VA, USA, 24–27 May 2020.
  14. Du, J. Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 6707–6715.
  15. Mateo-Garcia, G.; Veitch-Michaelis, J.; Smith, L.; Oprea, S.V.; Schumann, G.; Gal, Y.; Baydin, A.G.; Backes, D. Towards global flood mapping onboard low-cost satellites with machine learning. Sci. Rep. 2021, 11, 1–12.
  16. Sayak, P.; Ganju, S. Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning. arXiv 2021, arXiv:2107.08369.
  17. Löwe, R.; Böhm, J.; Jensen, D.G.; Leandro, J.; Rasmussen, S.H. U-FLOOD—Topographic deep learning for predicting urban pluvial flood water depth. J. Hydrol. 2021, 603, 126898.
  18. Motta, M.; de Castro Neto, M.; Sarmento, P. A mixed approach for urban flood prediction using Machine Learning and GIS. Int. J. Disaster Risk Reduct. 2021, 56, 102154.
  19. Panahi, M.; Jaafari, A.; Shirzadi, A.; Shahabi, H.; Rahmati, O.; Omidvar, E.; Lee, S.; Bui, D.T. Deep learning neural networks for spatially explicit prediction of flash flood probability. Geosci. Front. 2021, 12, 101076.
  20. Dazzi, S.; Vacondio, R.; Mignosa, P. Flood stage forecasting using machine-learning methods: A case study on the Parma River (Italy). Water 2021, 13, 1612.
  21. Lei, X.; Chen, W.; Panahi, M.; Falah, F.; Rahmati, O.; Uuemaa, E.; Kalantari, Z.; Ferreira, C.S.; Rezaie, F.; Tiefenbacher, J.P.; et al. Urban flood modeling using deep-learning approaches in Seoul, South Korea. J. Hydrol. 2021, 601, 126684.
  22. Drakonakis, G.I.; Tsagkatakis, G.; Fotiadou, K.; Tsakalides, P. OmbriaNet—Supervised flood mapping via convolutional neural networks using multitemporal sentinel-1 and sentinel-2 data fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2022, 15, 2341–2356.
  23. Anzhelika, T.K.; Kuznetsova, I.A.; Levin, E.L. Prediction of the flooding area of the northeastern Caspian Sea from satellite images. Geod. Geodyn. 2022, 14, 191–200.
  24. Ahad Hasan, T.; McRae, C.B.; Tavakol-Davani, H.; Goharian, E. Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning. Water 2022, 14, 1140.
  25. Li, P.; Zhang, J.; Krebs, P. Prediction of flow based on a CNN-LSTM combined deep learning approach. Water 2022, 14, 993.
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