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Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs. To solve this problem, this study develops the Absence Point Generation (APG) toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies. The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude, slope degree, topographic wetness index, and distance from rivers, and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are generated. To test the APG toolbox, we applied two benchmark algorithms of random forest (RF) and boosted regression trees (BRT) in a case study to investigate groundwater potential using three absence datasets i.e., the APG, random, and selection of absence samples (SAS) toolbox. The BRT-APG and RF-APG had the area under receiver operating curve (AUC) values of 0.947 and 0.942, while BRT and RF had weaker performances with the SAS and Random datasets. This effect resulted in AUC improvements for BRT and RF by 7.2, and 9.7% from the Random dataset, and AUC improvements for BRT and RF by 6.1, and 5.4% from the SAS dataset, respectively. The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps, which proves the importance of absence points in environmental binary issues. The proposed APG toolbox could be easily applied in other environmental hazards such as landslides, floods, and gully erosion, and land subsidence.

Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP conditioning factors such as topographical (i.e. slope, plan curvature, and profile curvature), hydrogeological (i.e. transmissivity, aquifer thickness, and electrical conductivity), hydrological (i.e. rainfall, distance from rivers, river density, and permeability), lithology, and land use were considered as input to the models. For the FR modelling, classified layers of the aforementioned variables were used, while their continuous layers were implemented in the KNN and RF algorithms. At the last step, receiver operating characteristic (ROC) curve was used to assess the ability and accuracy of the applied algorithms. Based on the findings, the area under the curve of ROC for the RF, KNN, and FR models was 97.1, 94.6, and 89.2%, respectively. Furthermore, transmissivity, slope, aquifer thickness, distance from rivers, rainfall, and electrical conductivity were recognized as the most influencing factors in the modelling procedure. The findings of this study indicated that the application of RF, KNN, and FR can be suggested for identification of suitable areas for FSP establishment in other regions.

Nitrate pollution of groundwater has increased dramatically worldwide due to increase of population and agricultural productivity. The resulting nitrate concentration in groundwater is usually a combination of various types of point and non-point pollutant sources. It is often difficult to distinguish between these sources since groundwater is formed in large and complex catchments with various natural processes and anthropogenic influence that contribute to a certain downstream nitrate concentration. For such conditions, this paper uses a methodology that can be used to inversely determine type and location of main nitrate pollutant source. The methodology builds on two state-of-the-art data mining techniques, boosted regression tree (BRT) and k-nearest neighbor (KNN). These techniques are used to produce a nitrate pollution vulnerability map. The methodology can mitigate effects of subjective judgement on determining importance of different sources and mechanisms for nitrate transport. The investigated mechanisms are hydrogeological, hydrological, anthropogenic, topography, and soil conditioning factors. Thus, the proposed methodology is used to separate between natural processes and anthropogenic effects on nitrate pollution. To calculate the groundwater vulnerability maps, a groundwater nitrate concentration of 40 mg/L (suggested by WHO with a 20% risk margin) was selected as a general threshold for identifying polluted areas that resulted in 96 polluted wells. Non-polluted locations were selected from well data with nitrate concentration less than 15 mg/L (96 non-polluted). The models were trained on 70% polluted and 70% non-polluted site data. The remaining data, 30% polluted and 30% non-polluted sites, were used to validate the simulation results. Results showed that the BRT produced outputs with higher performance than the KNN algorithm. The final ranking results based on the BRT model showed the higher importance of hydraulic conductivity, river density, soil, slope percent, net recharge, and distance from villages, in order, relative to other factors.

In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.

In recent years, assessment of sediment contamination by heavy metals, i.e., arsenic, has attracted the interest of scientists worldwide. The present study provides a new methodology to better understand the factors influencing surface water vulnerability to arsenic pollution by two advanced machine learning algorithms including boosted regression trees (BRT) and random forest (RF). Based on the sediment quality guidelines (Effects range low) polluted and non-polluted arsenic sediment samples were defined with concentrations8 ppm and8 ppm, respectively. Different conditioning factors such as topographical, lithology, erosion, hydrological, and anthropogenic factors were acquired to model surface waters' vulnerability to arsenic. We trained and validated the models using 70 and 30% of both polluted and non-polluted samples, respectively, and generated surface vulnerability maps. To verify the maps to arsenic pollution, the receiver operating characteristics (ROC) curve was implemented. The results approved the acceptable performance of the RF and BRT algorithms with an area under ROC values of 85% and 75.6%, respectively. Further, the findings showed higher importance of precipitation, slope aspect, distance from residential areas, and slope length in arsenic pollution in the modeling process. Erosion, lithology, and land use maps were introduced as the least important factors. The introduced methodology can be used to define the most vulnerable areas to arsenic pollution in advance and implement proper remediation actions to reduce the damages.

Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

Sustainable water resources management in arid and semi-arid areas needs robust models, which allow accurate and reliable predictive modeling. This issue has motivated the researchers to develop hybrid models that offer solutions on modelling problems and accurate predictions of groundwater potential zonation. For this purpose, this research aims to investigate the capability and robustness of a novel hybrid model, namely the logistic model tree (LMT) and compares it with state-of-the-art models such as the support vector machine and C4.5 models that locate potential zones for groundwater springs. A spring location dataset consisting of 359 springs was provided by field surveys and national reports and from which three different sample data sets (S1–S3) were randomly prepared (70% for training and 30% for validation). Additionally, 16 spring-related factors were analyzed using regression logistic analysis to find which factors play a significant role in spring occurrence. Twelve significant geo-environmental and morphometric factors were identified and applied in all models. The accuracy of models was evaluated by three different threshold-dependent and –Independent methods including efficiency (E), true skill statistic (TSS), and area under the receiver operating characteristics curve (AUC-ROC) methods. Results showed that the LMT model had the highest accuracy performance for all three validation datasets (Emean = 0.860, TSSmean = 0.718, AUC-ROCmean = 0.904); although a slight sensitivity to change in input data was sometimes observed for this model. Furthermore, the findings showed that relative slope position (RSP) was the most important factor followed by distance from faults and lithology.

Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model’s output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. All rights reserved. Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. However, few studies have been published on the training data selection effects on the LSM. Thus, this study assesses the training landslides random selection effects on support vector machine (SVM) accuracy, logistic regression (LR) and artificial neural networks (ANN) models for LSM in a catchment at the Dodangeh watershed, Mazandaran province, Iran. A 160 landslide locations inventory was collected by Geological Survey of Iran for this investigation. Different methods were implemented to define the landslide locations, such as inventory reports, satellite images and field survey. Moreover, 14 landslide conditioning factors were considered in the analysis of landslide susceptibility. These factors include curvature, plan curvature, profile curvature, altitude, slope angle, slope aspect, distance to faults, distance to stream, topographic wetness index, stream power index, terrain roughness index, sediment transport index, lithology and land use. The results show that the random landslide training data selection affected the parameter estimations of the SVM, LR and ANN algorithms. The results also show that the training samples selection had an effect on the accuracy of the susceptibility model because landslide conditioning factors vary according to the geographic locations in the study area. The LR model was found to be less sensitive than the SVM and ANN models to the training samples selection. Validation results showed that SVM and LR models outperformed the ANN model for all scenarios. The average overall accuracy of LR, SVM and ANN models are 81.42%, 79.82% and 70.2%, respectively.

In recent years, application of ensemble models has been increased tremendously in various types of natural hazard assessment such as landslides and floods. However, application of this kind of robust models in groundwater potential mapping is relatively new. This study applied four data mining algorithms including AdaBoost, Bagging, generalized additive model (GAM), and Naive Bayes (NB) models to map groundwater potential. Then, a novel frequency ratio data mining ensemble model (FREM) was introduced and evaluated. For this purpose, eleven groundwater conditioning factors (GCFs), including altitude, slope aspect, slope angle, plan curvature, stream power index (SPI), river density, distance from rivers, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), and lithology were mapped. About 281 well locations with high potential were selected. Wells were randomly partitioned into two classes for training the models (70% or 197) and validating them (30% or 84). AdaBoost, Bagging, GAM, and NB algorithms were employed to get groundwater potential maps (GPMs). The GPMs were categorized into potential classes using natural break method of classification scheme. In the next stage, frequency ratio (FR) value was calculated for the output of the four aforementioned models and were summed, and finally a GPM was produced using FREM. For validating the models, area under receiver operating characteristics (ROC) curve was calculated. The ROC curve for prediction dataset was 94.8, 93.5, 92.6, 92.0, and 84.4% for FREM, Bagging, AdaBoost, GAM, and NB models, respectively. The results indicated that FREM had the best performance among all the models. The better performance of the FREM model could be related to reduction of over fitting and possible errors. Other models such as AdaBoost, Bagging, GAM, and NB also produced acceptable performance in groundwater modelling. The GPMs produced in the current study may facilitate groundwater exploitation by determining high and very high groundwater potential zones.

Regarding the ever increasing issue of water scarcity in different countries, the current study plans to apply support vector machine (SVM), random forest (RF), and genetic algorithm optimized random forest (RFGA) methods to assess groundwater potential by spring locations. To this end, 14 effective variables including DEM-derived, river-based, fault-based, land use, and lithology factors were provided. Of 842 spring locations found, 70% (589) were implemented for model training, and the rest of them were used to evaluate the models. The mentioned models were run and groundwater potential maps (GPMs) were produced. At last, receiver operating characteristics (ROC) curve was plotted to evaluate the efficiency of the methods. The results of the current study denoted that RFGA, and RF methods had better efficacy than different kernels of SVM model. Area under curve (AUC) of ROC value for RF and RFGA was estimated as 84.6, and 85.6%, respectively. AUC of ROC was computed as SVM- linear (78.6%), SVM-polynomial (76.8%), SVM-sigmoid (77.1%), and SVM- radial based function (77%). Furthermore, the results represented higher importance of altitude, TWI, and slope angle in groundwater assessment. The methodology created in the current study could be transferred to other places with water scarcity issues for groundwater potential assessment and management.

The main aim of this study was to apply and compare two GIS-based data mining models, namely support vector machine (SVM) by four kernel functions (linear-SVM, polynomial-SVM, radial basic function-SVM, and sigmoidal-SVM) and entropy models in landslide susceptibility mapping, in Shangzhou District, China. Initially, 145 landslide locations were mapped using early reports, aerial photographs, and supported by field surveys. Subsequently, landslides in the study area were divided randomly into training and validation datasets (70/30) using ArcGIS 10.0 software. In the current study, 14 landslide conditioning factors, namely, slope aspect, slope angle, profile curvature, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), normalized difference vegetation index (NDVI), distance from roads, distance from rivers, distance from faults, rainfall, and lithology, were exploited to detect the most susceptible areas. In the next step, landslide susceptibility maps generated by four types of SVM or entropy models were produced. Finally, validation of the landslide susceptibility maps produced by different models was evaluated using receiver operating characteristics (ROC) curves. The results showed that the entropy model exhibited the highest success rate (0.7610), followed by polynomial-SVM (0.7526), the sigmoidal-SVM (0.7518), radial basic function-SVM (0.7446), and linear-SVM (0.7390) models. Similarly, the ROC plots also showed that the prediction rates gave almost similar results. The entropy model had the highest prediction rate (0.7599), followed by polynomial-SVM (0.7259), sigmoidal-SVM (0.7203), radial basic function-SVM (0.7149), and linear-SVM (0.7009) models. Hence, it can be concluded that the five models used in this study gave close results, with the entropy model exhibiting best performance in landslide susceptibility mapping.

The main objective of the current study is to apply a random forest (RF) data-driven model and prioritization of landslide conditioning factors according to this method and its comparison to a multivariate adaptive regression spline (MARS) model for landslide susceptibility mapping in China. For this purpose, at first, landslide locations were identified by earlier reports, aerial photographs, and field surveys and a total of 348 landslides were mapped from various sources in GIS. Then, the landslide inventory was randomly split into a training dataset (70% = 244 landslides) and the remaining (30% = 104 landslides) were used for validation. In this study, 12 landslide conditioning factors were applied to detect the most susceptible areas. These factors were slope aspect, altitude, distance to faults, lithology, normalized difference vegetation index, plan curvature, profile curvature, distance to rivers, distance to roads, slope angle, stream power index, and topographic wetness index. The relationship between each conditioning factor and landslide was finalized using a frequency ration (FR) model. Subsequently, landslide-susceptible areas were mapped using the MARS and RF models. The results revealed that the most important conditioning factors according to the accuracy measure (mean decrease) of the RF model are lithology (23.47%), distance to faults (22.21%), and altitude (19.58%). We also notice that altitude (19.04%), distance to faults (18.83%), and distance to roads (15.29%) have the highest importance according to the Gini measure. Finally, the accuracy of the landslide susceptibility maps produced from the two models was verified using a receiver operating characteristics curve. The results showed that the landslide susceptibility map produced using the MARS model has a higher prediction rate than RF by area under the curve values of 87.51 and 77.32%, respectively. According to the validation results, the map produced by the MARS model exhibits the better accuracy and could be proposed for land-use planning in the study area.

Considering the unstable condition of water resources in Iran and many other countries in arid and semi-arid regions, groundwater studies are very important. Therefore, the aim of this study is to model groundwater potential by qanat locations as indicators and ten advanced and soft computing models applied to the Beheshtabad Watershed, Iran. Qanat is a man-made underground construction which gathers groundwater from higher altitudes and transmits it to low land areas where it can be used for different purposes. For this purpose, at first, the location of the qanats was detected using extensive field surveys. These qanats were classified into two datasets including training (70%) and validation (30%). Then, 14 influence factors depicting the region’s physical, morphological, lithological, and hydrological features were identified to model groundwater potential. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), flexible discriminant analysis (FDA), penalized discriminant analysis (PDA), boosted regression tree (BRT), random forest (RF), artificial neural network (ANN), K-nearest neighbor (KNN), multivariate adaptive regression splines (MARS), and support vector machine (SVM) models were applied in R scripts to produce groundwater potential maps. For evaluation of the performance accuracies of the developed models, ROC curve and kappa index were implemented. According to the results, RF had the best performance, followed by SVM and BRT models. Our results showed that qanat locations could be used as a good indicator for groundwater potential. Furthermore, altitude, slope, plan curvature, and profile curvature were found to be the most important influence factors. On the other hand, lithology, land use, and slope aspect were the least significant factors. The methodology in the current study could be used by land use and terrestrial planners and water resource managers to reduce the costs of groundwater resource discovery.

A quantitative understanding of the hydro-environmental factors that influence the occurrence of agricultural drought events would enable more strategic climate change adaptation and drought management plans. Practical drought hazard mapping remains challenging due to possible exclusion of the most pertinent drought drivers, and to the use of inadequate predictive models that cannot describe drought adequately. This research aims to develop new approaches to map agricultural drought hazard with state-of-the-art machine learning models, including classification and regression trees (CART), boosted regression trees (BRT), random forests (RF), multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA) and support vector machines (SVM). Hydro-environmental datasets were used to calculate the relative departure of soil moisture (RDSM) for eight severe droughts for drought-prone southeast Queensland, Australia, over the period 1994–2013. RDSM was then used to generate an agricultural drought inventory map. Eight hydro-environmental factors were used as potential predictors of drought. The goodness-of-fit and predictive performance of all models were evaluated using different threshold-dependent and threshold-independent methods, including the true skill statistic (TSS), Efficiency (E), F-score, and the area under the receiver operating characteristic curve (AUC-ROC). The RF model (AUC-ROC = 97.7%, TSS = 0.873, E = 0.929, F-score = 0.898) yielded the highest accuracy, while the FDA model (with AUC-ROC = 73.9%, TSS = 0.424, E = 0.719, F-score = 0.512) showed the worst performance. The plant available water holding capacity (PAWC), mean annual precipitation, and clay content were the most important variables to be used for predicting the agricultural drought. About 21.2% of the area is in high or very high drought risk classes, and therefore, warrant drought and environmental protection policies. Importantly, the models do not require data on the precipitation anomaly for any given drought year; the spatial patterns in AGH were consistent for all drought events, despite very different spatial patterns in precipitation anomaly among events. Such machine-learning approaches are able to construct an overall risk map, thus assisting in the adoption of a robust drought contingency planning measure not only for this area, but also, in other regions where drought presents a pressing challenge, including its influence on key practical dimensions of social, environmental and economic sustainability.

Groundwater resources are facing a high pressure due to drought and overexploitation. The main aim of this research is to apply rotation forest (RTF) with decision trees as base classifiers and an improved ensemble methodology based on evidential belief function and tree-based models (EBFTM) for preparing groundwater potential maps (GPM). The performance of these new models is then compared with three previously implemented models, i.e., boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). For this purpose, spring locations in the Meshgin Shahr in Iran were detected. The spring locations were randomly categorized into training (70% of the locations) and validation (30% of the locations) datasets. Furthermore, several groundwater conditioning factors (GCFs) such as hydrogeological, topographical, and land use factors were mapped and regarded as input variables. The tree-based algorithms (i.e., BRT, CART, RF, and RTF) were applied by implementing the input variables and training dataset. The groundwater potential values (i.e., spring occurrence probability) obtained by the BRT, CART, RF, and RTF models for all the pixels of the study area were classified into four potential classes and then used as inputs of the EBF model to construct the new ensemble model (i.e., EBFTM). At last, this paper implemented a receiver operating characteristics (ROC) curve for determining the efficiency of the EBFTM, RTF, BRT, CART, and RF methods. The findings illustrated that the EBFTM had the highest efficacy with an area under the ROC curve (AUC) of 90.4%, followed by the RF, BRT, CART, and RTF models with AUC-ROC values of 90.1, 89.8, 86.9, and 86.2%, respectively. Thus, it could be inferred that the ensemble approach is capable of improving the efficacy of the single tree-based models in GPM production.

Land subsidence (LS) is among the most critical environmental problems, affecting both agricultural sustainability and urban infrastructure. Existing methods often use either simple regression models or complex hydraulic models to explain and predict LS. There are few studies that identify the risk factors and predict the risk of LS using machine learning models. This study compares four tree-based machine learning models for land subsidence hazard modelling at a study area in Hamadan plain (Iran). The study also analyzes the importance of six risk factors including topography (elevation, slope), geomorphology (distance from stream, drainage density), hydrology (groundwater drawdown) and lithology on LS. Thematic layers of each variable related to the LS phenomenon are prepared and utilized as the inputs to the four tree-based machine learning models, including the Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and the Random Forest (RF) algorithms to produce a consolidated LS hazard map. The accuracy of the generated maps is then evaluated using the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS). The RF approach had the lowest predictive error for mapping the LS hazard (i.e., AUC 96.7% for training, AUC 93.8% for validation, TSS 0.912 for training, TSS 0.904 for validation) followed by BRT. Groundwater drawdown was seen to be the most influential factor that contributed to land subsidence in the present study area, followed by lithology and distance from the stream network.

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.

Groundwater (GW) resources provide a large share of the world’s water demand for various sections such as agriculture, industry, and drinking water. Particularly in the arid and semi-arid regions, with surface water scarcity and high evaporation, GW is a valuable commodity. Yet, GW data are often incomplete or nonexistent. Therefore, it is a challenge to achieve a GW potential assessment. In this study, we developed methods to produce reliable GW potential maps (GWPM) with only digital elevation model (DEM)-derived data as inputs. To achieve this objective, a case study area in Iran was selected and 13 factors were extracted from the DEM. A spring location dataset was obtained from the water sector organizations and, along with the non-spring locations, fed into machine learning algorithms for training and validation. For delineating reliable GW potential, algorithms including random forest (RF) and its developed version, parallel RF (PRF), as well as extreme gradient boosting (XGB) with different boosters were used. The area under the receiver operating characteristics curve indicated that the PRF and XGB with linear booster give similar high accuracy (about 86%) for GWPM. The most important factors for accurate GWPM in the modeling procedure were convergence, topographic wetness index, river density, and altitude. Overall, we conclude that high-accuracy GWPMs can be produced with only DEM-derived factors with acceptable accuracy. The developed methodology can be employed to produce initial information for GW exploitation in areas facing a lack of data.

© 2018 by the authors. It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions.

Most part of Iran is arid and semi-arid; thus in most parts of the region, groundwater is the only source of water. This research presents a method based on a spatial multi-criterion evaluation (SMCE) for designing possible sites of underground dams and ranks them according to their suitability. The method was tested for siting underground dams in the Alborz Province, Iran. At first, screening algorithm was applied using exclusionary criteria, and thirty-one potential areas were recognized in the study area. In the next step, a suitable gorge or valley was recognized using the combination of basic maps and extensive field surveys (long axis of tank level) in each potential area. Subsequently, the analytical hierarchy process was used as a powerful tool for decision-making in the SMCE in order to evaluate different criteria for underground dam sites. SMCE techniques were then applied to combine the criteria, and obtain a suitability map in the study area. These sites were then compared and ranked according...

As demand for fresh groundwater in the worldwide is increasing, delineation of groundwater spring potential zones become an increasingly important tool for implementing a successful groundwater determination, protection, and management programs. Therefore, the objective of current study is to evaluate the capability of three machine learning models such as boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF), and comparison of their performance by bivariate (evidential belief function (EBF)), and multivariate (general linear model (GLM)) statistical methods in the groundwater potential mapping. This study was carried out in the Beheshtabad Watershed, Chaharmahal-e-Bakhtiari Province, Iran. In total, 1425 spring locations were detected in the study area. Seventy percent of the spring locations were used for model training, and 30 % for validation purposes. Fourteen conditioning-factors were considered in this investigation, including slope angle, slope aspect, altitude, plan curvature, profile curvature, slope length (LS), stream power index (SPI), topographic wetness index (TWI), distance from rivers, distance from faults, river density, fault density, lithology, and land use. Using the above conditioning factors and different algorithms, groundwater potential maps were generated, and the results were plotted in ArcGIS 9.3. According to the results of success rate curves (SRC), values of area under the curve (AUC) for the five models vary from 0.692 to 0.975. In contrast, the AUC for prediction rate curves (PRC) ranges from 77.26 to 86.39 %. The CART, BRT, and RF machine learning techniques showed very good performance in groundwater potential mapping with the AUC values of 86.39, 86.12, and 86.05 %, respectively. By the way, The GLM and EBF models in comparison by machine learning models showed weaker performance in spring groundwater potential mapping by the AUC values of 77.26, and 67.72 %, respectively. The proposed methods provided rapid, accurate, and cost effective results. Furthermore, the analysis may be transferable to other watersheds with similar topographic and hydro-geological characteristics.

Forest fire is known as an important natural hazard in many countries which causes financial damages and human losses; thus, it is necessary to investigate different aspects of this phenomenon. In this study, performance of four models of linear and quadratic discriminant analysis (LDA and QDA), frequency ratio (FR), and weights-of-evidence (WofE) was investigated to model forest fire susceptibility in the Yihuang area, China. For this purpose, firstly, a forest fire locations map was prepared implementing MODIS satellite images and field surveys. Then, it was classified into two groups including training (70%) and validation (30%) by a random algorithm. In addition, 13 forest fire effective factors were prepared and used such as slope degree, slope aspect, altitude, Topographic Wetness Index (TWI), plan curvature, land use, Normalized Difference Vegetation Index (NDVI), annual rainfall, distance from roads and rivers, wind effect, annual temperature, and soil texture. Using the training dataset and effective factors, LDA, QDA, FR, and WofE models were applied and forest fire susceptibility maps were prepared. Finally, area under the curve (AUC) of receiver operating characteristics (ROC) was implemented for investigating the performance of the models. The results depicted that WofE had the best performance (AUC = 82.2%), followed by FR (AUC = 80.9%), QDA (AUC = 78.3%), and LDA (AUC = 78%), respectively. The results of this study showed the high contribution of altitude, slope degree, and temperature. On the other hand, it was seen that slope aspect and soil had the lowest importance in forest fire susceptibility mapping. From the AUC results, it can be concluded that FR, WofE, LDA, and QDA had acceptable performance and could be used for forest fire susceptibility mapping at the regional scale.

© 2018, Springer International Publishing AG, part of Springer Nature. Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.

The purpose of current study is to produce groundwater qanat potential map using frequency ratio (FR) and Shannon's entropy (SE) models in the Moghan watershed, Khorasan Razavi Province, Iran. The qanat is basically a horizontal, interconnected series of underground tunnels that accumulate and deliver groundwater from a mountainous source district, along a water- bearing formation (aquifer), and to a settlement. A qanat locations map was prepared for study area in 2013 based on a topographical map at a 1:50,000-scale and extensive field surveys. 53 qanat locations were detected in the field surveys. 70 % (38 locations) of the qanat locations were used for groundwater potential mapping and 30 % (15 locations) were used for validation. Fourteen effective factors were considered in this investigation such as slope degree, slope aspect, altitude, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Using the above conditioning factors, groundwater qanat potential map was generated implementing FR and SE models, and the results were plotted in ArcGIS. The predictive capability of frequency ratio and Shannon's entropy models were determined by the area under the relative operating characteristic curve. The area under the curve (AUC) for frequency ratio model was calculated as 0.8848. Also AUC for Shannon's entropy model was 0.9121, which depicts the excellence of this model in qanat occurrence potential estimation in the study area. So the Shannon's entropy model has higher AUC than the frequency ratio model. The produced groundwater qanat potential maps can assist planners and engineers in groundwater development plans and land use planning.

Landslide is one of the most important natural hazards that make numerous financial damages and life losses each year in the worldwide. Identifying the susceptible areas and prioritizing them in order to provide an efficient susceptibility management is very vital. In current study, a comparative analysis was made between combined bivariate and AHP models (bivariate-AHP) with a logistic regression. At first, landslide inventory map of the study area was prepared using extensive field surveys and aerial photographs interpretation. In the next step, nine landslide causative factors were selected including altitude, slope percentage, slope aspect, lithology, distance from faults, streams and roads, land use, and precipitation which affect occurrence of the landslides in the study area. Subsequently, landslide susceptibility maps were produced using weighted (AHP) bivariate and logistic regression models. Finally, receiver operating characteristics (ROC) curve was used in order to evaluate the prediction capability of the mentioned models for landslide susceptibility mapping. According to the results, the combined bivariate and AHP models provided slightly higher prediction accuracy than logistic regression model. The combined bivariate and AHP, and logistic regression models had the area under the curve (AUC-ROC) values of 0.914, and 0.865, respectively. The resultant landslide susceptibility maps can be useful in appropriate watershed management practices and for sustainable development in the regions with similar conditions.

Landslide susceptibility mapping is among the first works for disaster management and land use planning activities in a mountain area like Ganzhou City. The aims of the current study are to assess GIS-based landslide spatial modeling using four models, namely data-driven evidential belief function (EBF), frequency ratio (FR), maximum entropy (Maxent), and logistic regression (LR), and to compare their performances. At first, a landslide inventory map was prepared according to aerial photographs, satellite images, and extensive field surveys. In total, 3971 landslide events were recognized in the study area that used 2979 landslides (75 %) for modeling and 992 landslide events (25 %) for validation. In the next step, the landslide-conditioning factors, namely slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index (TWI), slope-length (LS), lithology, normalized difference vegetation index (NDVI), distance from rivers, distance from faults, distance from roads, and rainfall, were derived from the spatial database. Finally, landslide susceptibility maps of Ganzhou City were mapped in ArcGIS based on EBF, FR, Maxent, and LR approaches and were validated using the receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that in the landslide susceptibility maps using the EBF, FR, Maxent, and LR models, the area under the curve (AUC) values were 0.7367, 0.7789, 0.7903, and 0.8237, respectively. Therefore, it can be concluded that all four models have AUC values of more than 0.70 and can be used in landslide susceptibility mapping in the study area. Also, the LR model had the best performance in the current study. Meanwhile, the mentioned models (EBF, FR, Maxent, and LR) showed almost similar results. The resultant susceptibility maps produced in the current study can be useful for land use planning and hazard mitigation purposes in the study area.

Considering the ever increasing financial damages of floods and the need to manage the surface water, the use of new and more sufficient methods seems to be necessary. Therefore, the present study aims to investigate capability of the Gamma, Beta, Chi-square, and Weibull probability distribution functions (PDFs) for flood hydrograph derivation. The present study was conducted in the Bar watershed, Iran using 14 suitable rainfall-runoff events. After base-flow separation and direct runoff determination, the three hour unit hydrograph of the watershed was computed to determine the coefficients and parameters of the mentioned PDFs. Finally, Nash-Sutcliffe (NS), relative error (RE), and root mean square error (RMSE) indices were used for evaluation. According to the results, Gamma, Chi-square, and Weibull PDFs had satisfying results for predicting the peak flow with RE values of 11.87%, 25.34% and 37.67% respectively. In the case of time to peak, Gamma, Chi-square and beta PDFs showed acceptable results with RE values of 26.66%, 39.17%, and 34.17% respectively. Totally, considering the high importance of peak discharge and time to peak in flood control projects, Gamma PDF is recommended to be used in flood control projects.

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.