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Floods are both complex and destructive, and in most parts of the world cause injury, death, loss of agricultural land, and social disruption. Flood susceptibility (FS) maps are used by land‐use managers and land owners to identify areas that are at risk from flooding and to plan accordingly. This study uses machine learning ensembles to produce objective and reliable FS maps for the Haraz watershed in northern Iran. Specifically, we test the ability of the support vector regression (SVR), together with linear kernel (LK), base classifier (BC), and hyper‐parameter optimization (HPO), to identify flood‐prone areas in this watershed. We prepared a map of 201 past floods to predict future floods. Of the 201 flood events, 151 (75%) were used for modeling and 50 (25%) were used for validation. Based on the relevant literature and our field survey of the study area, 10 effective factors were selected and prepared for flood zoning. The results show that three of the 10 factors are most important for predicting flood‐sensitive areas, specifically and in order of importance, slope, distance to the river and river. Additionally, the SVR‐HPO model, with area under the curve values of 0.986 and 0.951 for the training and testing phases, outperformed the other two tested models. [ABSTRACT FROM AUTHOR]

The Muskingum model is one of the most widely used hydrological methods in flood routing, and calibrating its parameters is an ongoing research challenge. We optimized Muskingum model parameters to accurately simulate hourly output hydrographs of three flood-prone rivers in the Karun watershed, Iran. We evaluated model performance using the correlation coefficient (CC), the ratio of the root-mean-square error to the standard deviation of measured data (PSR), Nash–Sutcliffe efficiency (NSE), and index of agreement (d). The results show that the gray wolf optimization (GWO) algorithm, with CC = 0.99455, PSR = 0.155, NSE = 0.9757, and d = 0.9945, performed better in simulating the flood in the first study area. The Kalman filter (KF) improved these measures by + 0.00516, − 0.1246, + 0.02328, and + 0.00527, respectively. Our findings for the second flood show that the gravitational search algorithm (GSA), with CC = 0.9941, PSR = 0.1669, NSE = 0.9721, and d = 0.9921, performed better than all other algorithms. The Kalman filter enhanced each of the measures by + 0.00178, − 0.0175, + 0.0055 and + 0.0021, respectively. The gravitational search algorithm also performed best in the third flood, with CC = 0.9786, PSR = 0.2604, NSE = 0.9321, and d = 0.9848, and with improvements in accuracy using the Kalman filter of + 0.01081, − 0.0971, + 0.394, and + 0.0078, respectively. We recommend the use of GWO-KF for flood routing studies with flood events of high volumes and hydrograph base times, and use of GSA-KF for studies with flood events of high volumes and hydrograph base times. [ABSTRACT FROM AUTHOR]

Protection against natural hazards (i.e., floods, landslides, forest fires, and earthquakes) is vital in land-use planning, especially in high-risk areas. Multi-hazard susceptibility maps can be used by land-use manager to guide urban development, to minimize the risk of natural disasters. The objective of the present study was to use four machine learning models to produce multi-hazard susceptibility maps in Khuzestan Province, Iran. In this work, four different natural hazards (flood, landslides, forest fire, and earthquake) using support vector machine (SVM), boosted regression tree (BRT), random forest (RF), and maximum entropy (MaxEnt) techniques were created. Effective factors used in the study include elevation, slope degree, slope aspect, rainfall, temperature, lithology, land use, normalized difference vegetation index (NDVI), wind exposition index (WEI), topographic wetness index (TWI), plan curvature, drainage density, distance from roads, distance from rivers, and distance from villages. The spatial earthquake hazard in the study area was derived from a peak ground acceleration (PGA) susceptibility map. The second step in the study was to combine the model-generated maps of the four hazards in a reliable multi-hazard map. The mean decrease Gini (MDG) method was used to determine the level of importance of each effective factor on the occurrence of landslides, floods, and forest fires. Finally, "area under the curve" (AUC) values were calculated to validate the forest fire, flood, and landslide susceptibility maps and to compare the predictive capability of the machine learning models. The RF model yielded the highest AUC values for the forest fire, flood, and landslide susceptibility maps, specifically, 0.81, 0.85, and 0.94, respectively. [ABSTRACT FROM AUTHOR]

Soil erosion is a major cause of damage to agricultural lands in many parts of the world and is of particular concern in semiarid parts of Iran. We use five machine learning techniques—Random Forest (RF), M5P, Reduced Error Pruning Tree (REPTree), Gaussian Processes (GP), and Pace Regression (PR)—under two scenarios to predict soil erodibility in the Dehgolan region, Kurdistan Province, Iran. Our models are based on a variety of soil properties, including soil texture, structure, permeability, bulk density, aggregates, organic matter, and chemical constituents. We checked the validity of the models with statistical metrics, including the coefficient of determination (R2 ), mean absolute error (MAE), root mean squared error (RMSE), T-tests, Taylor diagrams, and box plots. All five algorithms show a positive correlation between the soil erodibility factor (K) and silt, sand, fine sand, bulk density, and infiltration. The GP model has the highest prediction accuracy (R² ¼ 0.843, MAE ¼ 0.0044, RMSE ¼ 0.0050). It outperformed the RF (R² ¼ 0.812, MAE ¼ 0.0050, RMSE ¼ 0.0061), PR, (R² ¼ 0.794, MAE ¼ 0.0037, RMSE ¼ 0.0052), M5P (R² ¼ 0.781, MAE ¼ 0.0043, RMSE ¼ 0.0053), and REPTree (R² ¼ 0.752, MAE ¼ 0.0045, RMSE ¼ 0.0056) algorithms and thus is a useful complement to studies aimed at predicting soil erodibility in areas with similar climate and soil characteristics. [ABSTRACT FROM AUTHOR]

This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and profile curvatures, normalized difference vegetation index (NDVI), land use, lithology, distance to river, groundwater drawdown, and elevation. The Boruta algorithm was applied to determine the importance of the possible causative factors. NDVI, groundwater drawdown, land use, and lithology had the strongest relationships with land subsidence. Finally, we generated land subsidence maps using different machine learning and statistical models. The accuracy of these models was assessed using the AUC value and the true skill statistic (TSS) metrics. The SVM model had the highest prediction accuracy (AUC = 0.967, TSS = 0.91), followed by RF (AUC = 0.936, TSS = 0.87), EBF (AUC = 0.907, TSS = 0.83), and IoE (AUC= 0.88, TSS = 0.8). [ABSTRACT FROM AUTHOR]

We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively. [ABSTRACT FROM AUTHOR]

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran. [ABSTRACT FROM AUTHOR]

We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas. [ABSTRACT FROM AUTHOR]

We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but "distance to road" was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas. [ABSTRACT FROM AUTHOR]

Documenting habitats of rangeland plant species is required to properly manage rangelands and to understand ecosystem processes. A reliable rangeland species potential map can help managers and policy makers design a sustainable grazing system on rangelands. The aim of this study is to map the plant species in the Qurveh City rangelands, Kurdistan Province, Iran, using state-of-the-art machine learning algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes (NB), Bayes Net (BN) and Classification and Regression Tree (CART). A total of 185 rangeland species were used in the study, together with 20 conditioning factors, to build and validate models. The One-R feature section technique and multicollinearity test were used, respectively, to determine the most important factors and correlations between them. Model validation was performed using sensitivity, specificity, accuracy, F1-measure, Matthews correlation coefficient (MCC), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). Results showed that topographic wetness index (TWI), slope angle, elevation, soil phosphorus and soil potassium were the five most important factors to increase the rangeland plants habitat suitability. The Naïve Bayes algorithm (AUC = 0.782) had the highest performance and prediction accuracy and best consistency across the species in the investigated rangeland, followed by the SVM (AUC = 0.763), ANN (AUC = 0.762), CART (AUC = 0.627), and BN (AUC = 0.617) models. [Display omitted] • The habitats of important ecological rangeland plants were modeled and mapped. • Machine learning algorithms are robust tools in rangeland rehabilitation and management. • Topographic, phosphorus and potassium were the main factors to increase habitat suitability. • NB and CART had the highest and lowest prediction for studied species of investigated rangeland. • High potential rangeland habitats can help decision makers in better rangeland management. [ABSTRACT FROM AUTHOR]

Reliable flash flood susceptibility maps are a vital tool for land planners and emergency management officials for early flood warning and mitigation. We have developed a new ensemble learning model that predicts flash flood susceptibility at Haraz, Iran. The new model couples a Bayesian Belief Network (BBN) model with an extreme learning machine (ELM) and backpropagation (BP) structure optimized by a genetic algorithm (GA) named GA-BN-NN model. We applied the support vector machine (SVM) technique to a database of 194 flood locations with ten conditioning factors. An artificial neural network (ANN) algorithm with a multi-layer perceptron function, MLP-BP, optimized by a genetic algorithm, GA-MLP, and a shuffled frog-leaping algorithm, SFLA-MLP, were used as benchmark models for assessing the power prediction of the proposed model. Statistical measures, including sensitivity, specificity, accuracy, F1-measure and Jaccard coefficient, and root mean square error, were used to evaluate the goodness-of-fit and prediction accuracy, respectively, of the training and testing datasets. We found that all ten factors are positively correlated with flood occurrence, but slope angle has the highest average merit (AM = 9.7) and thus contributes most to the occurrence of flooding. Results indicate that the GA-BN-NN model has the highest goodness-of-fit and prediction accuracy (AUC = 0.966) and hence outperforms other ensemble learning models that we tested — the SFLA-MLP, MLP-BP, and GA-MLP models. We thus conclude that the proposed model is a promising technique for managing risk in flood-prone areas around the world. • A new deep learning model, E(T)-ELM-DBN-BP, was explored for flash flood susceptibility mapping. • The LSSVM technique was applied to determine the most important flash flood conditioning factors. • The MLP was optimized by BP, GA and SFLA algorithms as benchmark models to validity of the new model. • The E(T)-ELM-DBN-BP model outperformed the E(T)-MLP-BP, E(W)-MLP-GA and E(W)-MLP-SFLA models. • The new proposed model was offered as a promising and trustworthy tool for flash flood mitigation. [ABSTRACT FROM AUTHOR]

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.