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In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques—Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R2 = 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers. [ABSTRACT FROM AUTHOR]

Accurate forecasting of lake water level (WL) fluctuations is essential for effective development and management of water resource systems. This study applies the Random Tree (RT) algorithm and the Iterative Classifier Optimizer (ICO), which is based on the Alternating Model Tree (AMT) as an iterative regressor, to forecast WL up to three months ahead for Lake Superior and Lake Michigan. To enhance the accuracy of these machine learning (ML) algorithms, their forecasts are combined using ensemble algorithms such as Bagging (BA) or Additive Regression (AR), resulting in BA-RT, BA-ICO, AR-RT, and AR-ICO models. The most effective inputs for WL forecasting are determined using a nonlinear input variable selection method called partial mutual information selection (PMIS), considering lagged WL values up to 24 months. Forecasting models for each lake are developed using a training subset spanning from 1918 to 1988. The models' parameters are tuned using a validation subset covering 1989 to 2003. Finally, model performance is evaluated using a testing subset from 2004 to 2018. Statistical metrics and visual analysis with testing data are used to validate the performance of the developed algorithms. Additionally, results obtained from Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models serve as benchmarks for comparison with ML results. The findings demonstrate that ML models outperform SARIMA models in terms of error values: RMSPE ranges between 3.9% and 11.3% for Lake Michigan and between 2.3% and 9.2% for Lake Superior respectively. Furthermore, both hybrid ensemble algorithms improve individual ML algorithm performance; however, the BA algorithm achieves better overall performance compared to the AR algorithm. As a novel approach in forecasting problems, ICO algorithm based on AMT shows great potential in generating accurate multistep forecasts of lake WL. It demonstrates high generalization and low variance compared to the RT model. [ABSTRACT FROM AUTHOR]

To accurately predict dam break peak outflow (Qp), two standalone [random tree (RT), instance-based k-nearest neighbors learning (IBK)] and four new ensemble machine learning algorithms, that couple the standalone models with two ensemble methods [bootstrap aggregating (BA), disjoint aggregating (DA)], are proposed. The machine learning methods (RT, IBK, BA-RT, BA-IBK, DA-RT, and DA-IBK) and several popular empirical equations are applied to predict Qp using dam reservoir volume above the breach invert (Vw) and height of water in the dam reservoir above the breach invert (Hw) collected from 122 historical dam break events. Three different input scenarios (Vw, Hw, Vw and Hw) are considered to find the most effective combination of input variables. The proposed models are evaluated visually (using scatter and violin plots as well as Taylor diagrams) and quantitatively (using Nash–Sutcliffe Efficiency (NSE), Willmott's index of agreement (WI), and Legates and McCabe coefficient of efficiency (LM) metrics). It is found that DA-IBK provides the best performance (NSE = 0.866, WI = 0.960 and LM = 0.687), leading to a ~ 28% improvement in NSE compared to the best empirical equation. However, all machine learning models (particularly, the ensemble models), provide substantially better performance than the empirical equations, especially at the highest outflows. [ABSTRACT FROM AUTHOR]

Snow Water Equivalent (SWE) is one of the most critical variables in mountainous watersheds and needs to be considered in water resources management plans. As direct measurement of SWE is difficult and empirical equations are highly uncertain, the present study aimed to obtain accurate predictions of SWE using machine learning methods. Five standalone algorithms of tree-based [M5P and random tree (RT)], rule-based [M5Rules (M5R)] and lazy-based learner (IBK and Kstar) and five novel hybrid bagging-based algorithms (BA) with standalone models (i.e., BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) were developed. A total of 2550 snow measurements were collected from 62 snow and rain-gauge stations located in 13 mountainous provinces in Iran. Data including ice beneath the snow (IBS), fresh snow depth (FSD), length of snow sample (LSS), snow density (SDN), snow depth (SD) and time of falling (TS) were measured. Based on the Pearson correlation between inputs (IBS, FSD, LSS, SDN, SD and TS) and output (SWE), six different input combinations were constructed. The dataset was separated into two groups (70% and 30% of the data) by a cross-validation technique for model construction (training dataset) and model evaluation (testing dataset), respectively. Different visual and quantitative metrics (e.g., Nash–Sutcliffe efficiency (NSE)) were used for evaluating model accuracy. It was found that SD had the highest correlation with SWE in Iran (r = 0.73). In general, the bootstrap aggregation (i.e., bagging) hybrid machine learning methods (BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) increased prediction accuracy when compared to each standalone method. While BA-M5R had the highest prediction accuracy (NSE = 0.83) (considering all six input variables), BA-IBK could predict SWE with high accuracy (NSE = 0.71) using only two input variables (SD and LSS). Our findings demonstrate that SWE can be accurately predicted through a variety of machine learning methods using easily measurable variables and may be useful for applications in other mountainous regions across the globe. [ABSTRACT FROM AUTHOR]

Labyrinth weirs are utilized to transport a greater discharge during floods in contrast to conventional weirs due to their increased weir crest length. Nevertheless, due to the increased geometric complexity of labyrinth weirs, determination of accurate discharge coefficients and accordingly, head-discharge ratings are quite essential issues in practical application. Hence, as a first step the present study proposes the following eight standalone algorithms: decision table (DT), Kstar, least median square (LMS), M5 prime (M5P), M5 rule (M5R), pace regression (PR), random forest (RF) and sequential minimal optimization (SMO). Then, applying the stacking (ST) algorithm, these standalone models were hybridized to predict the discharge coefficient (Cd) for sharp-crested labyrinth weirs. Potential/effective variables were constructed in the form of several independent dimensionless parameters (i.e., θ, h/W, L/B, L/h, Froude number (Fr), B/W and L/W) to predict Cd as an output. The accuracy of the developed models was examined in terms of different statistical visually based and quantitative-based error measurement criteria. The results illustrate that h/W and B/W parameters have the highest and lowest effect on the Cd prediction, respectively. According to NSE, all developed algorithms provided accurate performances, while ST-Kstar had the highest prediction power. [ABSTRACT FROM AUTHOR]

Suspended sediment load modeling through advanced computational algorithms is of major importance and a challenging topic for developing highly accurate hydrological models. To model the suspended sediment load in the Rampur watershed station in the Mahanadi River Basin, Chhattisgarh State, India, unique integrated computational intelligence regression models with an optimizer are proposed in this study. For the first time in the literature, the isotonic regression (ISO) and sequential minimal optimization regression (SMOR) models and their hybrid versions with an iterative classifier optimizer (ICO) are applied for suspended sediment load modeling. The research is based on daily discharge and suspended sediment data collected over a 38-year period (1976–2014). Root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) were employed to evaluate the performance of the standalone ISO and SMOR, as well as the proposed ICO–ISO and ICO–SMOR hybrid models. Ten different scenarios were considered for modeling to investigate the performance of the models using different input combinations. The proposed new models were found to be more reliable than standalone ISO and SMOR models. Results revealed that the performance of the hybrid model was mostly attributable to the basic algorithm for the model development, where both SMOR and ICO–SMOR models were superior to their ISO and ICO–ISO counterparts in terms of accurate computation. Overall, the ICO–SMOR models outperformed the other models in terms of accuracy, with RMSE, RRMSE, R2, and NSE of 5495.1 tons/day, 2.77, 0.90, and 0.86, respectively. The current study's findings support the applicability of the proposed methodology for modeling of suspended sediment load and encourage the use of these methods in alternative hydrological modeling. [ABSTRACT FROM AUTHOR]

Infiltration is the process by which water enters the soil, and it plays a significant role in the hydrologic cycle. Direct measurement of infiltration is time consuming; however, empirical and physical models are inaccurate. In this study, we compared the results of a deep learning-based convolutional neural network (CNN) algorithm with those of models based on standalone support vector regression (SVR) and group method of data handling (GMDH) algorithms. We also tested a hybridized SVR and GMDH-based model enhanced by three metaheuristic algorithms: gray wolf optimization (GWO), bat algorithm (BA), and particle swarm optimization (PSO). Measured variables including measurement time; sand, clay and silt contents; bulk density; and soil moisture content were used as model inputs to predict cumulative infiltration as an output. Finally, models were evaluated using the Pearson correlation coefficient, root mean square error, mean absolute error, Nash–Sutcliffe efficiency, percentage of bias, and relative error. Weighting of the model input parameters/variables demonstrated that time was the most effective variable for predicting cumulative infiltration, whereas the most powerful parameter input combination required all seven variables. The prediction model evaluation results showed that the hybridized models improved the predictive capability of the standalone models. [ABSTRACT FROM AUTHOR]

The free overfall is a simple and widely used device for measuring discharge in open irrigation channels and agricultural research projects. However, the direct measurement of discharge can be difficult and time-consuming with care needed to minimize potential inaccuracies of empirical equations applied to site-specific conditions. Thus, in the present study four standalone algorithms of Isotonic Regression (ISO), Least Median of Square Regression (LMS), M5Prime (M5P) and REPT and four novel hybrid algorithms of rotation forest (ROF) combined with those four standalone models (i.e., ROF-ISO, ROF-LMS, ROF-M5P and ROF-REPT) were applied for the intelligent prediction of discharge per unit width for the free overfall condition in rectangular channels. This was accomplished via six data sets (355 data) collected from the published literature including end depth, Manning's roughness coefficient, channel width, bed slope and unit discharge. The dataset was partitioned in a 70:30 ratio randomly, 70% (248 data) of data used for model development while 30% (107 data) applied for model validation. Also, four different input combinations were constructed to identify the most effective prediction method. Furthermore, results were validated using several visually based (line graph, scatter plot, violin plot and Taylor diagram) and quantitative-based [root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Willmott's index of agreement, Legates and McCabe coefficient of efficiency (LM)] approaches. Results of the sensitivity analysis revealed that end depth had the highest effect on the results, while channel width was least influential. Results also showed that the best input combination incorporated all four input parameters. According to the results, ROF-REPT had the best performance with RMSE of 0.0035 (m3/s/m), NSE of 0.990, WI of 0.997% and LM of 0.905% followed by ROF-M5P REPT, M5P, ROF-LMS, ISO and LMS. [ABSTRACT FROM AUTHOR]

The objective of this study was to improve the predictability of the GALDIT (G: groundwater occurrence, A: aquifer hydraulic conductivity, L: level of groundwater above sea level, D: distance from the shoreline, I: impact of the seawater intrusion, and T: thickness of the aquifer) groundwater vulnerability model using machine leaning methods. This study evaluated eight state-of-the-art machine learning methods, including the naïve Bayes tree (NBT) and logistic model tree (LMT) methods, and their combinations with the dagging (DA), bagging (BA), and random subspace (RS) methods. The results of the machine leaning methods were compared against the benchmark GALDIT model. The coastal Gharesoo-Gorgan Rood aquifer, North Iran, was used as a case study for the proposed methodology. Two sets of total dissolved solids (TDS) samples from 53 wells were collected in 2017 and 2018 and used for the GALDIT modeling and validation purposes, respectively. Correlation coefficient (r) values were calculated for model validation and prediction accuracy by comparison with the TDS data. All eight machine learning models performed well in assessing the coastal aquifer vulnerability with respect to the GALDIT model. The best result was obtained by the BA-LMT model (r = 0.931), followed by the DA-LMT model (r = 0.911), the BA-NBT model (r = 0.904), the DA-NBT model (r = 0.896), the RS-NBT model (r = 0.882), the RS-LMT (r = 0.873), the LMT (r = 0.863), the NBT (r = 0.850), and GALDIT model (r = 0.480). [ABSTRACT FROM AUTHOR]

From a watershed management perspective, streamflow need to be predicted accurately using simple, reliable, and cost-effective tools. Present study demonstrates the first applications of a novel optimized deep-learning algorithm of a convolutional neural network (CNN) using BAT metaheuristic algorithm (i.e., CNN-BAT). Using the prediction powers of 4 well-known algorithms as benchmarks – multilayer perceptron (MLP-BAT), adaptive neuro-fuzzy inference system (ANFIS-BAT), support vector regression (SVR-BAT) and random forest (RF-BAT), the CNN-BAT model is tested for daily streamflow (Qt) prediction in the Korkorsar catchment in northern Iran. Fifteen years of daily rainfall (Rt) and streamflow data from 1997 to 2012 were collected and used for model development and evaluation. The dataset was divided into two groups for building and testing models. The correlation coefficient (r) between rainfall and streamflow with and without antecedent events (i.e., Rt-1, Rt-2, etc.) (as the input variables) and Qt (as the output variable) served as the basis for constructing different input scenarios. Several quantitative and visually-based evaluation metrics were used to validate and compare the model's performance. The results indicate that Rt was the most effective input variable on Qt prediction and the integration of Rt, Rt-1, and Qt-1 was the optimal input combination. The evaluation metrics show that the CNN-BAT algorithm outperforms the other algorithms. The Friedman and Wilcoxon signed-rank test indicates that the prediction power of CNN-BAT algorithm is significantly/statistically different from the other developed algorithms. [ABSTRACT FROM AUTHOR]

The objective of the current study is groundwater vulnerability assessment using DRASTIC, modified DRASTIC, and three statistical bivariate models (frequency ratio (FR), evidential belief function (EBF), and weights-of-evidence (WOE)) for Sari-Behshahr plain, Iran. A total of 218 wells were sampled for nitrate concentration measurement in 2015. Datasets were generated using results from 109 wells having nitrate concentrations greater than 50 mg/L. The nitrate data were divided into two groups of 70% (76 locations as training dataset) for modeling and 30% (33 locations as a testing dataset) for model validation. Finally, five groundwater potential pollution (GPP) maps were produced by the training dataset and then evaluated using the testing dataset and receiver operating characteristic (ROC) method. Results of the ROC method showed that the WOE model had the highest predictive power, followed by EBF, FR, modified DRASTIC, and DRASTIC models. Results of the maps obtained revealed that high and very high pollution potential covered the southern part of the study areas, where big cities are located. Results of the present study can be replicated in other locations for identifying groundwater contaminant prone areas. [ABSTRACT FROM AUTHOR]

Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period (1980–2015). The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R2). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers. [ABSTRACT FROM AUTHOR]

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models. [ABSTRACT FROM AUTHOR]

Advance knowledge of solar radiation is highly essential for multiple energy devotions such as sustainability in energy production and development of solar energy system. The current research investigates the capability of four data mining computation models, namely random forest (RF), random tree, reduced error pruning trees and hybrid model of random committee with random tree reduce (RC) for predicting daily measured solar radiation at four locations of Burkina Faso, i.e., Bur Dedougou, Bobo-Dioulasso, Fada-Ngourma and Ouahigouya. Daily data of seven climatic variables, namely maximum and minimum air temperature, maximum and minimum relative humidity, wind speed, evaporation and vapor pressure deficit, for the period 1998–2012 are used for solar radiation prediction. Different combinations of input variables are used according to correlation coefficient between the predictors and predictand, and the best input combination is selected based on the sensitivity of model output measured in terms of statistical indices. The obtained results are found consistence for all the meteorological stations. The highest accuracy in prediction is found when all the climate variables are used as input. The RC and RF showed the minimal absolute error in prediction at all the stations. The RMSE and NSE are found in the range of 0.03–0.05 and 0.77–0.91 for RC and 0.03–0.05 and 0.78–0.92 for RF at different stations. The results indicate that the proposed data mining models can be used for accurate prediction of solar radiation over the Burkina Faso. [ABSTRACT FROM AUTHOR]

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This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey. Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies. Twelve predisposing factors were then prepared using this available dataset. To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters. For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation. The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors. The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0.848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0.837) model. Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area. This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir. The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation. [ABSTRACT FROM AUTHOR]

Flood is one of the most prevalent natural disasters that frequently occur in the northern part of Iran reported in hot spots of flood occurrences. The main aim of the current study was to prepare flood susceptibility maps using four models, namely frequency ratio (FR), weights-of-evidence (WofE), analytical hierarchy process (AHP), and ensemble of frequency ratio with AHP (FR-AHP), and to compare them at Haraz Watershed in Mazandaran Province, Iran. A total of 211 flood locations were prepared in GIS environment, of which 151 locations were randomly selected for modeling and the remaining 60 locations were used for validation aims. In the next step, 10 flood-conditioning factors were prepared including slope angle, plan curvature, elevation, topographic wetness index, stream power index, rainfall, distance from river, geology, landuse, and normalized difference vegetation index. The receiver operating characteristic curve and the area under the curve (AUC) were created for different flood susceptibility maps. Validation of results showed that AUC values for success rate in training data set, for FR, WofE, AHP, and FR-AHP, were 97.07, 98.96, 95.91, and 86.19 % with prediction rates of 0.9657 (96.57 %), 0.9596 (95.96 %), 0.9492 (94.92 %), and 0.8469 (84.69 %), respectively. Moreover, the results showed that the frequency ratio model had the highest AUC in comparison with other models. Generally, the four models show a reasonable accuracy in flood-susceptible areas. The results of this study can be useful for managers, researchers, and planners to manage the susceptible areas to flood and reduce damages. [ABSTRACT FROM AUTHOR]

This study presented the prediction capability of three methods including the frequency ratio (FR), fuzzy gamma (FG) and landslide index method (LIM) to produce landslide-prone areas in the Sari-Kiasar watershed, Mazandaran Province of Iran. In the first step, 105 landslide locations were selected and were randomly divided into two groups of 75% (78 locations) and 25% (27 locations) as training and testing datasets. Then the 17 landslide conditioning factors including land use/land cover, Differential Vegetation Index (DVI), lithology and distance from faults, elevation, slope aspect, slope angle, tangential curvature, profile curvature and plane curvature, distance from drainage, rainfall, Stream Power Index, Sediment Transport Index and temperature, and distance from road, density of settlement were considered for the proposed modelling approach. Finally, by applying the training dataset, three landslide susceptibility maps were constructed by using the FR, FG and LIM methods. The prediction capability of the performed model was evaluated by the area under the receiver operating curve or AUC for both training (success rate) and testing (prediction rate) datasets. The results showed that the AUC for success rate of FR, FG and LIM models was 82.04%, 81.08% and 73.61% and for prediction rate was 82.72%, 79.09% and 65.45%, respectively. The results showed that the FR model has a higher prediction accuracy than the FG and LIM methods. This study revealed that the most important factors in landslide occurrence are rainfall, slope and vegetation. The result of the present study can be possibly useful for land use planning and watershed management. [ABSTRACT FROM AUTHOR]

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas. [ABSTRACT FROM AUTHOR]

Flooding is a very common worldwide natural hazard causing large-scale casualties every year; Iran is not immune to this thread as well. Comprehensive flood susceptibility mapping is very important to reduce losses of lives and properties. Thus, the aim of this study is to map susceptibility to flooding by different bivariate statistical methods including Shannon's entropy (SE), statistical index (SI), and weighting factor (Wf). In this regard, model performance evaluation is also carried out in Haraz Watershed, Mazandaran Province, Iran. In the first step, 211 flood locations were identified by the documentary sources and field inventories, of which 70% (151 positions) were used for flood susceptibility modeling and 30% (60 positions) for evaluation and verification of the model. In the second step, ten influential factors in flooding were chosen, namely slope angle, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, rainfall, geology, land use, and normalized difference vegetation index (NDVI). In the next step, flood susceptibility maps were prepared by these four methods in ArcGIS. As the last step, receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated for quantitative assessment of each model. The results showed that the best model to estimate the susceptibility to flooding in Haraz Watershed was SI model with the prediction and success rates of 99.71 and 98.72%, respectively, followed by Wf and SE models with the AUC values of 98.1 and 96.57% for the success rate, and 97.6 and 92.42% for the prediction rate, respectively. In the SI and Wf models, the highest and lowest important parameters were the distance from river and geology. Flood susceptibility maps are informative for managers and decision makers in Haraz Watershed in order to contemplate measures to reduce human and financial losses. [ABSTRACT FROM AUTHOR]