Flood is one of most devastating natural disasters, resulting in significant economic losses including human deaths [1,2]. This damages both rural and urban infrastructure like bridge and drainage systems [3,4]. Flood generally leaves undesirable sediments and debris in the affected lands [5,6], which can disrupt transportation networks [7], clog drainage infrastructure and sewers [8,9] and may make lands unproductive. The cleaning up of flood debris is usually costly, not to mention the disruption to the daily lives of the community involved [10,11]. Due to climate change, the frequency and magnitude of floods are increasing [12].

Flood forecasting requires significant efforts, and it is usually the responsibility of a large government organisation. Governments spend a significant amount on various projects to identify flood-safe areas, which are used to build cities. Researchers have developed numerous methods to estimate design floods, which are used to build flood-safe infrastructure [13,14]. Design flood is defined as a flood level or discharge associated with a return period or annual exceedance probability such as a 100-year flood.

In addition to traditional techniques, like the rational method, physical and numerical [15,16] models have been proposed for design flood estimation. Most of the physical models require in-depth knowledge of flood processes [17,18], making them difficult to use in practice. Van den Honert and McAneney [19] pointed out the common limitations associated with these physical models [20,21], which include model inaccuracies resulting in systematic errors (over or underestimation of design floods) [22,23]. On the other hand, data-driven models have been quite popular for flood estimation in recent years [24]. Examples include a quantile regression technique and a probabilistic rational method [25]. This is because they usually consider climate factors and catchment characteristics in developing models, which are easier to apply [26,27]. A flood frequency analysis (FFA) is the most popular method to estimate design floods, which uses observed peak discharge data disregarding catchment characteristics [28,29]. A normal distribution [30,31], log-normal distribution [32,33], Gumbel distribution [34,35], generalised extreme value distribution and log-Pearson type III distribution [36,37] are some of the most commonly used flood frequency distributions in FFA. One of the major limitations of FFA is the lack of long and good quality recorded flood data at the location of interest. To overcome data limitations, hydrologists have proposed a regional flood frequency analysis (RFFA), which attempts to estimate design floods at an ungauged catchment based on the concept of a homogeneous region, which pools observed flood data from a group of similar catchments to estimate design floods at the ungauged catchment [38,39]. This method became more popular among researchers than physical models because it saves time and resources [40]. Probabilistic rational method (PRM) [41], multiple linear regression (MLR) [42,43], quantile regression techniques (QRT) [44,45], and index flood method (IFM) [46,47] are some of the most commonly used RFFA techniques. However, some of the early RFFA techniques (e.g., rational method) have lost their popularity due to their inconsistency and inappropriate model assumptions.

In the past two decades, scientists suggested hybrid or mixed methods to increase the relative accuracy of RFFA models [48,49]. Although some early linear models have been improved, they may not be accurate under some circumstances as flood generation is basically a non-linear process [50]. Hydrologists attempted to apply non-linear methods in RFFA such as a non-linear regression analysis (where log-transformation of the variables is considered). Artificial intelligence (AI)-based methods are also non-linear, but more powerful than simple non-linear models like log-log ones as they can consider many different combinations of variables and complex non-linear processes in model building. Given that the majority of flood estimation methods are data driven, they require a great deal of simplification and assumptions to be practical, accessible, and implementable [51,52]. They require relatively fewer input data and minimal knowledge of fundamental physical processes involved. Over the last two decades, non-linear AI-based RFFA methods have grown in popularity over physical models as these provide more accurate results and are easier to apply [53,54]. Artificial neural networks (ANNs) [55,56], support vector regression (SVR) [57,58,59,60], adaptive neuro-fuzzy inference system (ANFIS) [61,62], genetic algorithms (GA) [63,64] and hybrid, mixed and combined approaches [65,66] are some of the most popular AI-based flood estimation methods. As AI-based models are relatively new in flood estimation, it is not easy to decide which one is to be applied for a given problem [67,68].

There are several important aspects to consider when building models based on AI. Firstly, these models like all other data-driven models need enough data to develop and test the model [67]. If adequate data exist, it is often possible to build, test, and evaluate an AI-based model (similar to many other RFFA models) by dividing the data into training, test, and evaluation data sub-sets [69,70]. Cross-validation is also often used in building RFFA models when less data samples are available [71]. The more data used in the modeling, the less generalization error occurs, meaning that the final model can be used on different sites with limited or no data available. Other benefits of having adequate data include the simplicity of using different distribution methods, the ability to account for lost data or missing variables, and, most crucially, the ability to train and validate the model multiple times to develop the best possible model [72,73]. However, it should be noted that data quality is of significant importance in developing and testing accurate models.

2.1. ANN-Based RFFA Models