One ML method that is used to capture the underlying relationship between independent and dependent variables in atmospheric processes is Artificial Neural Networks (ANNs). ANNs are interconnected networks comprising an input layer, some number of hidden layers, and an output layer. Each layer contains several processors, or nodes, referred to as artificial neurons. The neurons in each layer are connected to the neurons in the previous and next layers, and they transfer information from one layer to the next. Synaptic weights and biases, along with activation functions applied to the input layer, modulate the input signals sent from one layer to the next. The processed information is then sent as output to the connected neurons in the output layer (Zounemat-Kermani et al., 2020). The power of ANNs is their ability to learn functional relationships, with minimal empirical error, between these variables. Additionally, the use of activation functions with ANNs allows them to handle non-linear data effectively (Zaidi et al., 2018). In fact, many water related studies (e.g., Sahoo et al., 2017) using ANNs have shown that complex, reproducible, non-linear relationships exist among, for example, precipitation, temperature, streamflow, climate indices, irrigation demand, and groundwater levels.

Another ML method that has been used for predicting average rainfall is a classification algorithm known as Support Vector Machines (SVM) (e.g., Mohanty and Mohapatra, 2018). This method, developed by Vapnik (1995), is based on Structural Risk Minimization, which, rather than minimizing empirical error, as ANNs do, minimizes an upper bound of the generalization error ε. Dynamic Support Vector Machines (DSVMs), a modified version of the SVM, can be used to accommodate the structural changes in non-stationary rainfall data because it uses, instead of a static ε and static regularization constants, an exponentially decreasing ε, and exponentially increasing regularization constants (Cao and Gu, 2002) to allow room for analysis of changing patterns in the data.

The probabilities of hydrological extreme events such as floods and drought are modeled using different distributions from those that predict future average values. Traditionally, these events and their return periods are estimated with distributions associated with Extreme Value Theory (e.g., Kao and Ganguly, 2011). However, ML techniques for anomaly detection have begun to be applied to hydrological extremes problems. Anomaly detection is the identification of outliers in the data, or items that differ significantly from the overall trend of the data. Typically, anomalous data is related to issues such as measurement equipment failure or an extreme hydrological event. For example, Das and Parthasarathy (2009) used unsupervised spatio-temporal distance-based and neighborhood-based anomaly detection method with global climate data to identify extreme drought and heavy rainfall at specific locations. Characterization of short-term and long-term future extreme events have also been made with anomaly detection using trends found in historical time series. For these analyses, techniques such as kernel-based (rule-based classification), window-based (examination of the data in smaller “windows” in space or time), predictive, and segmentation (partitioning data into even smaller, possibly unequal, segments) algorithms are employed along with anomaly detection for locating extremely low and extremely high temperature and precipitation events (Chandola et al., 2009b). In the case of the research by Sun et al. (2017), a density-based method was applied to anomaly detection in a hydrological time series. That is, the data were transformed to a piecewise linear representation through the important feature points of the data before mapping their slope, length, and mean to three-dimensional space for examination.

Flood models at the catchment level analyze mainly issues of runoff generation and concentration leading to flood discharge. Because flood flow predictions are complex, non-linear, and not well-understood, ML may be required to evolve algorithms to derive characteristics of a particular flow. One way of evolving these algorithms is with the use of genetic programming, or genetic algorithms (GA), which produce, using routines imitating Darwin's “natural selection,” algorithms directed to perform tasks defined by a set of training examples. Whigham and Crapper (2001) applied a type of genetic programming system to discover rainfall-runoff relationships for two meteorologically and topographically different catchments, one in Wales and one in Australia, and compared the results to those obtained with a traditional deterministic lumped parameter model. While both models did well when rainfall and runoff were correlated, the genetically programmed model performed better on the more poorly correlated data because it was allowed not to assume any underlying relationships, only to demonstrate its “fitness” to solve the problem.

Guidolin et al. (2016) used a two-dimensional cellular-automata-based model employing simple transition rules and a weight-based system to model catchment-level runoff. This diffusive-like method is designed to work with various general grids (rectangular, hexagonal, triangular) and with different neighborhood types (e.g., Moore or von Neumann). It also allows for model parallelization to increase its efficiency in large compute environments. To propagate a flood using this method, ratios of water to be transferred from a central cell to downstream neighbor cells are calculated using a weight-based system, with water volume transferred limited by Manning's formula (Manning et al., 1890), and the critical flow equation. Water velocity and an adaptive time step are evaluated within a larger updated timestep. The results of the emergent behavior of this process shows good agreement with much more computationally intensive physical methods.

Flood hazard in rivers can be characterized by the probability and intensity of large river flows and their consequent inundations, and it depends on the atmospheric and catchment processes preceding river flood generation (Merz et al., 2010). In fact, river floods are generally defined in hydrological terms by their water level or amount of discharge. Thus, Shamseldin (2010) explore the use of ANN for forecasting discharge from the Blue Nile river in Sudan. The type of neural network they chose was that of a multi-layer perceptron (MLP) feedforward network, a non-linear input–output model consisting of a network of interconnected neurons, or computational units, linked together by connection pathways. The input layer is essentially a set vectors of independent variable values, whereas the output layer is a set of possible dependent variable vectors of values. Between these two layers is a hidden layer containing an unknown number of neurons which are usually estimated by a trial-and-error procedure based on a mathematical non-linear transfer function (Shamseldin, 2010). Input variables in this case were weighted historical rainfall estimates, weighted seasonal rainfall estimates, and seasonal expectation of discharge; and the output variables were the river discharge values. Results showed strong correlation with observations for the river.

In addition to the multilayer perceptron ANN approach, other types of ANNs have been used to analyze river floods. For example, Tayyab et al. (2016) applied and compared three different types of ANNs to predict stream discharge for the Jinsha River Basin in China. The methods included feedforward back propagation neural networks (FFBPNN), generalized regression neural networks (GRNN), and radial basis function neural networks (RBFNN). The differences among these approaches lies in the hidden layer functions and activation functions that are applied to the problem. Badrzadeh et al. (2013) expanded on these ANN approaches by coupling wavelet (transforms that identify trends in the data normally not revealed by signal analysis approaches and also help to de-noise a dataset) multi-resolution analysis and adaptive neuro-fuzzy interface system (ANFIS) techniques (integration of neural networks and fuzzy logic) as preprocessing techniques to the ANN and show improved daily river flow forecasting over the use of ANNs alone, especially for long lead times. Mosavi et al. (2018) demonstrated the application of ANNs, neuro-fuzzy, SVM, and support vector regression (SVR) (SVM with regression only), in forecasting river floods and predicting the runoff hydrograph. The robustness of these techniques was evaluated and was found to be in good agreement with the observations.

Building resilience to natural disasters is one of the most pressing challenges for achieving sustainable urban development in flood-prone regions (Chang et al., 2019). River flooding in urban areas can cause high levels of damage, and while a relationship between hydrological characteristics and damaging floods may exist, knowing about an area's hydrological characteristics does not always indicate understanding of its vulnerability to damaging floods (Pielke, 2000). This understanding is imperative for hazard-mitigation planning for urban areas because these areas' responses to rainfall extremes tend to be faster than those for natural surfaces (Rodriguez et al., 2003). Thus, strategies for flood mitigation in these areas such as detention ponds, soakaways, permeable concrete, and green spaces, or upstream solutions such as river training and construction of dams and levees (Shamseldin, 2010) should be evaluated and implemented based on a thorough understanding of flood risks and responses of the area. For example, for predicting urban floods for the city of Pattani south of Thailand, Noymanee et al. (2017) examined the entire Pattani basin, which includes two dams for water management: a diversion-type, Pattani Dam, and a hydropower plant, Bang Lang Dam. It is known that the most frequent floods are a result of overflow from flash flooding of the Pattani Dam rushing toward the city. The researchers acknowledge that a comprehensive approach to controlling floods in the area must include both structural and non-structural measures such as the development of improved technology for data management of the drainage network, and an increase in the sensors' frequency and extent of coverage. Thus, Noymanee et al. (2017) tested five different ML methods using open data pertaining to the area hydrology, the dam structures, the drainage network, and the technological components of the dams to explain the occurrence of extreme floods estimating dam water levels and cumulative precipitation amounts to forecast flood peaks in the urban area. The five methods tested included an ANN, Bayesian linear regression (statistical inference using Bayes' theorem), boosted decision tree regression and decision forest regression (both similar to random forest analysis discussed in section 2.2.1) and linear regression. Results showed the lowest error and highest correlation with the observations in the urban area from the Bayesian linear regression. This favorable result for that method may have occurred because it was informed by probability distributions drawn from prior data.

Often, in order to understand and manage risks of urban flooding beyond purely hydrological considerations, integration of decision support tools with predictive models is instructive. For example, one study (Rozos, 2019), combined a hydrological model, a demand management model called a network flow programming model (NFP), and an Feed Forward Neural Network (FFNN) to simulate a water supply system in Athens, Greece. The NFP optimizes and simulates the operation of a water supply system given hydrological inputs. FFNNs are the simplest type of ANN, whereby information moves in a forward direction from input nodes to the hidden layer to the output nodes (Mosavi et al., 2018) and they lend themselves to multi-model coupling. In this case, the NFP used synthetic data of a length capable of capturing the risk of each policy. Then the penalty functions of the NFP were selected to reflect the operating policies with different levels of risk acceptance. This process provided a large set of training data over a long period of time that was then used as input to the FFNN. This process allowed optimal decisions to be identified and made for the Athens system.

Indirect flood effects are those that cause damage to assets outside the flooded area. These assets can be physical, economic, social, or ecological in nature with impacts lasting for days, months, or even years after a large flooding event (Costello et al., 2019). In order to evaluate the extent of these effects, multi-agent-based simulations have been applied. Agent-based models simulate actions and interactions of autonomous agents, which can be individual actors or groups of actors, to assess the effects of these individual actions on the system as a whole. In one study (Yang S. et al., 2019), reinforcement learning, which rewards software agents for actions taken to maximize their cumulative reward, was used with the agent-based simulation for the optimization of post-disaster recovery for both individual companies and supply chains for Tokyo, Japan. That study showed improved indirect damage estimation accuracy and mitigation potential over statistical methods and rough empirical models.

Drought is a complex weather hazard (Van Loon, 2015); therefore, a comprehensive understanding of the physical mechanisms that drive drought is essential to improving drought prediction (Huang et al., 2016). Numerous studies have been conducted to understand the intricate physical processes that lead to the extreme low moisture conditions of drought. Scientists have employed dynamical methods that involve climate and hydrological model simulations, statistical models using a suite of predictors and drought indices, as well as hybrid models for drought prediction (Fernández et al., 2009; Dutra et al., 2014; AghaKouchak, 2015; Mo and Lyon, 2015; Wood et al., 2015; Hao et al., 2017, 2018).

During the last decade, there has been an increase in the use of ML techniques to improve drought predictability (Hao et al., 2018). For instance, random forest ML algorithms have been increasingly used in drought prediction studies (Park et al., 2016; Kuswanto and Naufal, 2019; Rahmati et al., 2020). Random forests are extensions of decision tree analysis that start with classification trees–types of decision trees that can be grown together as a “forest” in a computational system. They provide highly accurate classification and characterization of complex predictor variable interactions while maintaining flexible analytical technique selection (Allen et al., 2018). Random forests also provide the capability to deal with the issue of overfitting and multicollinearity as compared to the traditional linear regression models (Konapala and Mishra, 2020). Park et al. (2016) employed random forests, boosted regression tree, and Cubist ML algorithms (rule-based model trees on which the terminal leaves contain linear regression models) for meteorological and agricultural drought monitoring using 16 remote sensing based drought factors over arid and humid regions in the United States. Their findings suggest that among the three approaches, random forests provide the best performance for Standardized Precipitation Index (SPI) prediction. Similarly, Kuswanto and Naufal (2019) found the performance of random forests to be optimal when using SPI derived from Modern-Era Retrospective analysis for Research and Applications (MERRA-2) for drought prediction over the East Nusa Tenggara Province in Indonesia. A more recent study, Rahmati et al. (2020) compared the performance of six different ML techniques [classification and regression trees (CART), boosted regression trees (BRT), random forests, multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA), and SVM] for mapping agricultural drought hazard in the southeast region of Queensland, Australia. Similar to Park et al. (2016) and Kuswanto and Naufal (2019), they found that random forests had the best goodness-of-fit and predictive performance among the six models. Zaniolo et al. (2018) contributed to the FRIDA (FRamework for Index-based Drought Analysis) for the automatic design of basin-customized drought indexes across different types of basins by applying a ML-powered variable selection algorithm. The algorithm is based on a Wrapper for Quasi-Equally Informative Subset Selection (W-QEISS), which applies a multi-objective evolutionary algorithm to identify Pareto-efficient subsets of variables. This technique is able to maximize the wrapper accuracy, minimize the number of selected variables, and optimize relevance and redundancy of the subset. As a result, the framework is able to build an index that represents a surrogate of the drought conditions in a basin through the computation and combination of all the relevant available information regarding the water cycle in the system identified using the feature selection algorithm.

ANN ML techniques (see section 2.1.1) have also been used for drought forecasting (Mishra et al., 2007; Morid et al., 2007; Belayneh and Adamowski, 2012; Belayneh et al., 2014). Belayneh et al. (2016) coupled a wavelet transform data processing technique (see section 2.1.3), bootstrapping and boosting ensemble approaches with ANN and Support Vector Regression (SVR) (see section 2.1.1) for drought prediction in the Awash river basin of Ethiopia. Bootstrapping is a resampling technique with replacement that was used to create bootstrap ANN and SVR ensemble models to reduce model prediction uncertainty. Boosting techniques improve the performance of an algorithm by producing a series of models focusing on training cases that were not well predicted previously. The researchers found that the coupled models showed an improved performance and provided more robust SPI predictions as compared to either of ANN or SVR alone.

ANN models can be limited by model interpretability, local minima traps, and computational efficiency issues. Thus, alternatively, XGBoost has been gaining popularity due to its high execution speed and improved model performance as compared to other ML techniques such as SVM, ANN, and random forests (Fan et al., 2018; Shimoda et al., 2018; Zhang R. et al., 2019). XGBoost is an ensemble technique that implements a gradient boost decision tree algorithm to produce an ensemble of weak prediction models. Models are subsequently added to improve errors until an optimum performance is achieved. Zhang R. et al. (2019) compared the performance of XGBoost with a traditional statistical model and an ANN model for Standardized Precipitation Evapotranspiration Index (SPEI) prediction with a lead time of 1–6 months for 32 weather stations in the Shaanxi Province of China. In their study, the XGBoost model showed the best performance for SPEI prediction, achieved highest user's and producer's accuracies and was much faster than the ANN model.

To tackle sediment-related problems, the predictions of sediment production and transport are required to inform urban planning and watershed management communities of the major source of sediment and erosion-prone areas. Conventionally, these predictions are addressed through a wide variety of erosion and sediment transport models (Merritt et al., 2003; Nearing et al., 2005). Despite the usefulness and maturity of these traditional approaches, the prediction of sediment-related parameters (e.g., soil losses, in-stream sediment load, and sediment delivery ratio) is still challenging because of the following model limitations: (a) running many physically-based erosion and sediment transport models are time- and resource-intensive, and requires the consideration of more physical processes in addition to the hydrological process making models are less applicable to sediment-related predictions in large watersheds and areas (Abaci and Papanicolaou, 2009); (b) most models are designed to simulate a specific type of erosion (e.g., rill, gully, and stream bank erosion) and sediment transport (e.g., suspended load and bed load) (Wischmeier and Smith, 1978; Ganasri and Gowda, 2015), while sediment-related problems in urban areas and urban waterways often entail multiple types of erosions and sediment transport therefore requiring the integration of a variety of models; and (c) most erosion and sediment transport models do not cover sediment transport and deposition at man-made structures (Rowley, 2014) in urban areas. A comparative study conducted by Liang et al. (2019) showed that data-driven models can effectively inform and complement the simulations conducted with physics based models. Currently, there are many studies that utilize various ML methods to address various issues in sediment research. We summarize a list of example studies by their application areas and their applied ML methods:

In general, erosion and sediment research is a broad subject that provides numerous opportunities for ML applications. By reviewing the above-mentioned example studies, we have summarized that (a) compared with traditional erosion and sedimentation transport models, ML methods are easier and cheaper (Cigizoglu, 2002; Tayfur and Guldal, 2006; Yadav et al., 2019a), and can be readily applied to solve complex sediment problems that entail human factors and multiple erosion and sediment transport processes (Xu et al., 2019a), (b) ML models that rely on field data generally produce better and more reliable results than those obtained from experimental models (Kitsikoudis et al., 2014).

In addition to its application to previously described hydrological studies, hybrid modeling has also been applied to sediment research (Merritt et al., 2003; Hajigholizadeh et al., 2018). Through the fusion of inductive data-driven models and deductive process-based models (Goldstein and Coco, 2015), hybrid models inherit the strengths of both the ML methods and physics-based models in a single model that has an increased performance in terms of speed (Babovic et al., 2001; Hall, 2004), accuracy (Krasnopolsky and Fox-Rabinovitz, 2005; Goldstein and Coco, 2015), and the capability of addressing soil-water problems with complex and multi-scale physical processes (Hajigholizadeh et al., 2018). An additional benefit of hybrid modeling is that ML models and data can be directly coupled to improve the calibration of process-based models (Knaapen and Hulscher, 2003; Ruessink, 2005; Mekonnen et al., 2012). Hajigholizadeh et al. (2018) summarized a table of hybrid modeling applications that integrate statistical models with process-based models in sediment research including:

Over the last two decades, RS data have successfully contributed to various stages of flood management (Rahman and Di, 2017) such as flood risk assessment and flood emergency planning and management. Flood risk assessment requires the performance of flood hazard assessment, exposure risk assessment, and vulnerability assessment. As a part of the flood hazard assessment, RS data have been analyzed for flood forecasting and evaluation of flood inundation. As a part of flood emergency planning and management, RS data have been widely used in flood early warning systems, rescue and relief operations, post-flood damage assessment and policy making. Various recent approaches have used advanced ML techniques and RS during various stages of flood management.

Flood forecasting requires accurate estimation of rainfall. Although satellite RS has limited direct applicability to flood forecasting, it has been widely used for precipitation estimation, which is an important input for flood forecasting models. In the late 90s, Tsintikidis et al. (1997) used a shallow neural network with one hidden layer to estimate rainfall from a passive microwave radiometer SSM/I data. The network considered brightness temperature and associated polarization information as inputs and it output the rainfall rates. A random forest based ML algorithm was used to estimate the precipitation which used satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) (Kühnlein et al., 2014). Recently, Shi et al. (2015) proposed a spatio-temporal sequence forecasting approach using Convolutional Long-Short Term Memory (ConvLSTM) with RADAR echo data in 2D from a ground-based RADAR for precipitation nowcasting by forecasting the RADAR echo data. Pan et al. (2019) proposed a Convolutional Neural Network (CNN) based approach to improve the precipitation estimates from numerical weather prediction (NWP) models. The authors stated that the method outperformed reanalysis precipitation products as well as statistical downscaling (SD) products using linear regression, nearest neighbors, random forests, or fully connected deep neural networks. In an another recent work, Hayatbini et al. (2019) proposed a precipitation estimation framework using a fully convolutional neural network and the advanced baseline imager data from GOES-16, a multispectral geostationary satellite. Specifically, they proposed that the U-net CNN architecture could perform rain/no-rain classification using satellite imagery. The study was based on the earlier work of Hong et al. (2004) on precipitation estimation using remote sensing data and an ANN.

Flash flood susceptibility mapping is another important process in flood risk assessment. Recently, Costache et al. (2019) used a Digital Elevation Model (DEM) with 30 m spatial resolution obtained from Shuttle Radar Topography Mission (SRTM), and which was developed using the technique called SAR interferometry, to derive seven flash-related conditioning factors such as slope angle, aspect, profile curvature, and other factors. In addition, the authors used aerial imagery from Google Earth to delineate the torrential areas along with the land use/cover data, CORINE, which was derived from Sentinel-2 and Landsat-8 RS images. K-nearest neighbors (kNN), K-Start (KS), and Anlytical Hierarchy Process (AHP) algorithms were then applied to obtain the flash-flood susceptibility mapping. Thus, RS techniques played a crucial role in obtaining eight out of 10 flash-flood conditioning factors. In a similar work, Shahabi et al. (2020) used a ML ensemble method with four different k-nearest neighbor (kNN) algorithms for flood detection and susceptibility mapping. Authors used Sentinel-1 images to generate the flood inventory and SRTM DEM to obtain various flood-related conditioning factors. These two works show that ML ensemble methods are gaining traction in flood susceptibility mapping.

Mapping of flooded areas is important to performing damage assessment, deploying rescue and relief operations and developing policies. An example of applying RS and ML to this undertaking is Feng et al. (2015), who developed a random forest based approach to map accurately a flooded area using high-resolution (0.2 m) imagery obtained from Unmanned Areal Vehicle (UAV) imagery. The data were obtained for Yuyao City of Zhejiang Province in Eastern China during the flooding that occurred due to the extreme rainfall event on October 7, 2013. Additionally, Jain et al. (2020) developed a hybrid approach to combine the strength of the traditional water indices from RS imagery and generalization capability of Convolutional Neural Networks (CNN). The authors proposed a new water index which minimized cloud interference in the RS image and used it with a pre-trained VGG-16 model (Simonyan and Zisserman, 2014) and a transfer learning based approach to re-train the model for a new task of flood water detection. In a similar work, Potnis et al. (2019) used an Encoder-Decoder Neural Network based on the Efficient Residual Factorized Convnet (ERFNet) architecture for multi-class segmentation of urban floods satellite imagery from WorldView-2 of floods in Srinagar, India during September 2014. Recently, Jiang et al. (2020) proposed an approach to obtain waterlogging depth from video images using CNN. The approach generated synthetic images from the set of images of reference objects and flood surface, which was further used to train the CNN model to obtain the waterlogging depth. This method can also be employed to obtain waterlogging depth from the images taken of the flooded area using recent drone-based video surveillance. Cervone et al. (2017) added to these techniques a methodology to fuse social media data with the RS data during a flood situation to improve the flood mapping capability.

Recently, a few approaches to model the semantics in RS images were proposed for flood detection and mapping. Kurte et al. (2017) proposed a semantics enabled framework to model the spatial relationships among various regions in the RS images to enable spatial-relationships-based queries such as Retrieve all images in the ALI repository having Built Up region externally connected to the Stagnated Flood Water. Later this work was extended to accommodate the temporal aspect to enable the spatio-temporal semantic queries such as Show road segments which were completely submerged during 9th September 2014 to 22nd September 2014 (Kurte et al., 2019). In a similar semantics based approach, Potnis et al. (2018) developed a flood scene ontology (FSO) which formally defines complex classes such as Flooded_Residential_Buildings, Accessible_Residential_Buildings, Operational_Roads. After detecting various objects in the RS imagery using any supervised classification approach, the ontology can be used to infer complex classes which are very important for flood mapping.

RS data has been used over the past 50 years to monitor water quality. For instance, RS data can be used to measure water turbidity, or lack of transparency, which is a good measure of the water quality. Clear water shows high absorptivity in the infra-red and near-infrared wavelength regions. It also shows some reflectivity in the visible regions. Reflectivity in this application can reveal variations in water quality due to salinity, temperature, and turbidity. In the past decade, much research has been published in which remote sensing and ML approaches are used to estimate additional water quality parameters. For example, Dogan et al. (2009) explored the non-linear capability of ANN to improve the accuracy of biological oxygen demand (BOD) estimation. Wu et al. (2014) compared multiple regression (MR) with ANN for total suspended solid (TSS) turbidity estimations using data measured with a hyperspectral spectroradiometer and found that the non-linear transformation function of ANN performed better than MR. Wang et al. (2011) used the support vector regression (SVR) method to retrieve various water quality estimators from SPOT-5 satellite data. SVRs showed potential in solving problems with small sample size, non-linearity, or high dimension (Vapnik, 1995). Huo et al. (2014) stated that the lakes near urban areas or inside urban areas are becoming eutrophied or even hypereutrophied due to excessive urbanization and a fast growing economy. The authors used genetic algorithms combined with support vector machines (GA-SVM) to build an inversion model for eutrophic indicators such as Chl-a from Landsat ETM imagery. They showed that the GA-SVM based method had better prediction accuracy than the traditional statistical regression methods and ANN based approaches. According to Sharaf El Din et al. (2017), modeling water quality using satellite data is a complex problem, and conventional regression-based approaches can not perform well while modeling such complex relationships between water quality and RS data. The authors claimed that the proposed Landsat8-based-BPNN—back propagation neural network—to estimate water quality (both optical and non-optical) worked better than SVM-based methods. Moreover, the authors mentioned that, compared to the BPNN-based methods, the SVM-based methods could produce very different results due to differences in parameter selections, kernel-selection, high algorithmic complexity, and extensive memory requirement. The developed model showed R² > 0.9 for the water quality indicators such turbidity, total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), and dissolved oxygen (DO). Recently, Hafeez et al. (2019) compared several ML techniques including artificial neural networks, random forests, cubist regression, and support vector regression for estimating the concentrations of suspended solids (SS), Chl-a, and turbidity using Landsat data. The results showed that the ANN-based model achieved the highest accuracy in estimating the above mentioned water quality indicators. In an another recent study, Govedarica and Jakovljević (2019) used 4-years of time-series data of in-situ monitoring of surface water bodies for the calibration and validation of a water quality estimation based on SVM and ANN algorithms using Landsat 8 data. The work also compared the estimations based on Landsat 8 with the Sentinel-2 data and found that, due to higher spatial and spectral resolution, Sentinel-2 data is a better alternative for water quality monitoring. Interestingly, the results showed that SVM produced more accurate results than ANN when used with Landsat data, whereas ANN provided better estimation accuracy for turbidity and TSS than SVM, and lower accuracy for TN and TP than SVM when used with Sentinel-2 data. Finally, Wang et al. (2017) conducted a study that combined a ML algorithm and remote sensing spectral indices [difference index (DI), ratio index (RI), and normalized difference index (NDI)] through fractional derivatives methods and in turn establishes a model for estimating and assessing the water quality index (WQI) (2.3). For this study, the WQI was calculated using sensitive wave bands and a spectral index of hyperspectral data, and particle swarm optimization (Kennedy and Eberhart, 1995; Shi and Eberhart, 1998)—support vector regression models (PSO-SVR), which deploy a population of candidate solutions over the SVR search space. Through comparisons of the predictive effects of the 22 water quality index estimations determined by the PSO-SVR, Wang et al. (2017) demonstrated that the model based on RI, DI, and NDI values of the 1.6 order was better performing than the others for predicting the water quality index of the semi-arid area of central Asia [R2 (0.92), RMSE = 58.4, RPD (2.81) and a slope of curve fitting of 0.97].

Urban impervious surfaces such as roads, driveways, sidewalks, and parking lots prevent water from infiltrating into soil, which has impacts on urban hydrology, groundwater, and water quality. Impervious surfaces facilitate pollutant's movements to nearby water bodies during heavy rain and urban flooding (Hall and Hossain, 2020). In the context of ML, identifying impervious surfaces from RS data is fundamentally a classification approach. However, many index-based approaches for sighting impervious surfaces using RS (e.g., Weng, 2012) focus on the developments in this area that use ML algorithms. Recently, Yao et al. (2017) adopted a one-class classification approach to detect impervious surfaces using high-resolution GF-1 satellite images, and found that Presence and Background Learning (PBL) and Positive Unlabeled Learning (PUL) outperformed SVM models in detecting impervious surfaces. Miao et al. (2019) also used a one class classification technique and Landsat-8 imagery for impervious surface classification. In a similar study, Bian et al. (2019) used a random forest algorithm and time-series data from multiple satellites HJ-1A/B and GF-1/2 to estimate the changes in the impervious surface percentage over the years 2009–2017. Lin et al. (2019) addressed the challenges in detecting impervious surfaces due to the diversity of land use and shadow effects in high-resolution satellite imagery using a dictionary sparse representation classification and data fusion approach with WV-2, GeoEye-1, TerraSAR-X, and LiDAR. Zhang H. et al. (2019) addressed similar issues by using a deep CNN approach with data fusion from optical and SAR satellites WV-3, Sentinel-2, and Radarsat-2. Similar other works, Sun et al. (2019) (used 3D CNN with WV-3 and LiDAR), McGlinchy et al. (2019) (used UNet with WV-2), show increasing trends of using deep learning based approaches with multi-satellite data fusion.