Among natural disasters, flood is increasingly recognized as a serious worldwide concern that causes the most damages in parts of agriculture, fishery, housing, and infrastructure and strongly ...affects economic and social activities. Universally, there is a requirement to increase our conception of flood vulnerability and to outstretch methods and tools to assess it. Spatial analysis of flood vulnerability is part of non-structural measures to prevent and reduce flood destructive effects. Hence, the current study proposes a methodology for assessing the flood vulnerability in the area of watershed in a severely flooded area of Iran (i.e., Kashkan Watershed). First interdependency analysis among criteria (including population density (PD), livestock density (LD), percentage of farmers and ranchers (PFR), distance to industrial and mining areas (DTIM), distance to tourist and cultural heritage areas (DTTCH), land use, distance to residential areas (DTRe), distance to road (DTR), and distance to stream (DTS)) was conducted using the decision-making trial and evaluation laboratory (DEMATEL) method. Hence, the cause and effect factors and their interaction levels in the whole network were investigated. Then, using the interdependency relationships among criteria, a network structure from flood vulnerability factors to determine their importance of factors was constructed, and the analytical network process (ANP) was applied. Finally, with the aim to overcome ambiguity, reduce uncertainty, and keep the data variability, an appropriate fuzzy membership function was applied to each layer by analyzing the relationship of each layer with flood vulnerability. Importance analysis indicated that land use (0.197), DTS (0.181), PD (0.180), DTRe (0.140), and DTR (0.138) were the most important variables. The flood vulnerability map produced by the integrated method of DEMATEL-ANP-fuzzy showed that about 19.2% of the region has a high to very high flood vulnerability.
Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important ...component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods.
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•Ensemble machine learning (ML) predicting flood susceptibility•Contribution of models with accuracy values above 80% in ensembling process•Area under curve (AUC) for the models ranges from 0.83 to 0.89.•ML allows quick priority of prone areas for the remediation of floods.
Abstract
Snow avalanche is among the most harmful natural hazards with major socioeconomic and environmental destruction in the cold and mountainous regions. The devastating propagation and ...accumulation of the snow avalanche debris and mass wasting of surface rocks and vegetation particles threaten human life, transportation networks, built environments, ecosystems, and water resources. Susceptibility assessment of snow avalanche hazardous areas is of utmost importance for mitigation and development of land-use policies. This research evaluates the performance of the well-known machine learning methods, i.e., generalized additive model (GAM), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and support vector machine (SVM), in modeling the mass wasting hazard induced by snow avalanches. The key features are identified by the recursive feature elimination (RFE) method and used for the model calibration. The results indicated a good performance of the modeling process (Accuracy > 0.88, Kappa > 0.76, Precision > 0.84, Recall > 0.86, and AUC > 0.89), which the SVM model highlighted superior performance than others. Sensitivity analysis demonstrated that the topographic position index (TPI) and distance to stream (DTS) were the most important variables which had more contribution in producing the susceptibility maps.
In recent years, there has been an increasing interest in spatial modeling, and flood hazard prediction is a major area of interest within the field of hydrology. It is necessary to consider return ...periods for identifying the flood hazard zones. In hydraulic modeling such as HEC-RAS, this is usually done, but in spatial modeling by machine learning (ML) models, this has not been taken into account so far. This study seeks to obtain data that will help to address this research gap. The Sentinel-1 Radar images have been used for identifying the flooded locations in different return periods. An embedded feature selection algorithm (i.e., recursive feature elimination random forest; RFE-RF) was used in the current research for key feature selection. Then, three ML models of neural networks using model averaging, classification and regression tree, and support vector machine were employed. The flood hazard prediction demonstrated a great performance for all the applied models (i.e., accuracy and precision > 90%, Kappa > 88%). Sensitivity analysis disclosed that the variables of elevation and distance from stream are in the first importance order, the variables of precipitation, slope, and land use are in the second importance order, and other variables are in the third importance order in all return periods. The modeling results indicated that among man-made land uses the irrigated area between 17.7 and 31.4%, dry farming from 0.5 to 2.4%, and residential areas between 8.3 and 25.1% are exposed to high and very high flood hazard areas. The current findings add to a growing body of literature on the spatial modeling of floods.
This study aimed to develop a novel framework for risk assessment of nitrate groundwater contamination by integrating chemical and statistical analysis for an arid region. A standard method was ...applied for assessing the vulnerability of groundwater to nitrate pollution in Lenjanat plain, Iran. Nitrate concentration were collected from 102 wells of the plain and used to provide pollution occurrence and probability maps. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence. Afterwards, an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80% were selected to contribute in ensembling process. Results indicated that accuracy for the three models ranged from 0.81 to 0.87, therefore all models were considered for ensemble modeling process. The resultant groundwater pollution risk (produced by vulnerability, pollution, and probability maps) indicated that the central regions of the plain have high and very high risk of nitrate pollution further confirmed by the exiting landuse map. The findings may provide very helpful information in decision making for groundwater pollution risk management especially in semi-arid regions.
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•Novel risk assessment framework for nitrate groundwater contamination in arid regions.•Machine learning (ML) predicting vulnerability, pollution, and occurrence probability.•ML allows quick regional evaluation of risk posed by nitrate in groundwater.
Groundwater resources, unlike surface water, are more vulnerable to disturbances and contaminations, as they take a very long time and significant cost to recover. So, predictive modeling and ...prevention strategies can empower policymakers for efficient groundwater governance through informed decisions and recommendations. Due to the importance of groundwater quality modeling, the hardness susceptibility mapping using machine learning (ML) models has not been explored. For the first time, the current research aimed to predict groundwater hardness susceptibility using the ML models. The performance of two ensemble models of boosted regression trees (BRT) and random forest (RF) is investigated through the arrangement of a comparative study with multivariate discriminant analysis (MDA). According to the hardness values in 135 groundwater quality monitoring wells, the hard and soft water are determined; then, 11 predictor variables including distance from the sea (DFS), land use, elevation, distance from the river (DFR), depth to groundwater (DTGW), pH, precipitation (PCP), evaporation (E), groundwater level (GWL), curvature, and lithology are used for predicting the groundwater hardness susceptibility map. Results indicated that the variables of DFR, DTGW, elevation, and DFS had a higher contribution to the modeling process. So, the high harness areas are mostly related to low elevations, low DTGW, and proximity to river and sea, which facilitate the percolation conditions for minerals containing calcium or magnesium into groundwater.
Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for groundwater quality monitoring and management. Advancement of accurate prediction systems is essential for the ...identification of vulnerable areas in order to raise awareness about the potential salinity susceptibility and protect the groundwater and top-soil in due time. In this study, three machine learning models of Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and Bayesian Generalized Linear Model (Bayesglm) are developed for building prediction models and their performance evaluated in the delineation of salinity susceptibility maps. Both natural and human effective factors (16 features) were used as predictors for groundwater salinity modeling and were randomly divided into the training (80%) and testing (20%) datasets. The models were evaluated using testing datasets after calibration using the selected features by recursive feature elimination (RFE) method. The RFE indicated that modeling with 8 features had better performance among 1 to 16 features (Accuracy = 0.87). Results of the groundwater salinity prediction highlighted that StoGB had a good performance, whereas the RotFor and Bayesglm had an excellent performance based on the Kappa values (> 0.85). Although spatial prediction of the models was different, all of the models indicated that central parts of the region have a very high susceptibility which matches with agricultural areas, lithology map, the locations with low depth to groundwater, low slope, and elevation. Additionally, areas near to the Maharlu lake and locations with a high decline in groundwater are also located in the very high susceptibility zone, which can confirm the effects of saltwater intrusion. The susceptibility maps produced in this study are of utmost importance for water security and sustainable agriculture.
Soil erosion is a serious threat to sustainable agriculture, food production, and environmental security. The advancement of accurate models for soil erosion susceptibility and hazard assessment is ...of utmost importance for enhancing mitigation policies and laws. This paper proposes novel machine learning (ML) models for the susceptibility mapping of the water erosion of soil. The weighted subspace random forest (WSRF), Gaussian process with a radial basis function kernel (Gaussprradial), and naive Bayes (NB) ML methods were used in the prediction of the soil erosion susceptibility. Data included 227 samples of erosion and non-erosion locations through field surveys to advance models of the spatial distribution using predictive factors. In this study, 19 effective factors of soil erosion were considered. The critical factors were selected using simulated annealing feature selection (SAFS). The critical factors included aspect, curvature, slope length, flow accumulation, rainfall erosivity factor, distance from the stream, drainage density, fault density, normalized difference vegetation index (NDVI), hydrologic soil group, soil texture, and lithology. The dataset cells of samples (70% for training and 30% for testing) were randomly prepared to assess the robustness of the different models. The functional relevance between soil erosion and effective factors was computed using the ML models. The ML models were evaluated using different metrics, including accuracy, the kappa coefficient, and the probability of detection (POD). The accuracies of the WSRF, Gaussprradial, and NB methods were 0.91, 0.88, and 0.85, respectively, for the testing data; 0.82, 0.76, and 0.71, respectively, for the kappa coefficient; and 0.94, 0.94, and 0.94, respectively, for POD. However, the ML models, especially the WSRF, had an acceptable performance regarding producing soil erosion susceptibility maps. Maps produced with the most robust models can be a useful tool for sustainable management, watershed conservation, and the reduction of soil and water loss.
Snow avalanche as a natural disaster severely affects socio-economic and geomorphic processes through damaging ecosystems, vegetation, landscape, infrastructures, transportation networks, and human ...life. Modeling the snow avalanche has been seen as an essential approach for understanding the mountainous landscape dynamics to assess hazard susceptibility leading to effective mitigation and resilience. Therefore, the main aim of this study is to introduce and implement an ensemble machine learning model of random subspace (RS) based on a classifier, functional tree (FT), named RSFT model for snow avalanche susceptibility mapping at Karaj Watershed, Iran. According to the best knowledge of literature, the proposed model, RSFT, has not earlier been introduced and implemented for snow avalanche modeling and mapping over the world. Four benchmark models, including logistic regression (LR), logistic model tree (LMT), alternating decision tree (ADT), and functional trees (FT) models were used to check the goodness-of-fit and prediction accuracy of the proposed model. To achieve this objective, the most important factors among many climatic, topographic, lithologic, and hydrologic factors, which affect the snow accumulation and snow avalanche occurrence, were determined by the information gain ratio (IGR) technique. The goodness-of-fit and prediction accuracy of the models were evaluated by some statistical-based indexes including, sensitivity, specificity, accuracy, kappa, and area under the ROC curve, Friedman and Wilcoxon sign rank tests. Results indicated that the ensemble proposed model (RSFT), had the highest performance (Sensitivity= 94.1%, Specificity= 92.4%, Accuracy= 93.3%, and Kappa= 0.782) rather than the other soft-computing benchmark models. The snow avalanche susceptibility maps indicated that the high and very high susceptibility avalanche areas are located in the north and northeast parts of the study area, which have a higher elevation with more precipitation and lower temperatures.
Due to the rapidly increasing demand for groundwater, as one of the principal freshwater resources, there is an urge to advance novel prediction systems to more accurately estimate the groundwater ...potential for an informed groundwater resource management. Ensemble machine learning methods are generally reported to produce more accurate results. However, proposing the novel ensemble models along with running comparative studies for performance evaluation of these models would be equally essential to precisely identify the suitable methods. Thus, the current study is designed to provide knowledge on the performance of the four ensemble models i.e., Boosted generalized additive model (GamBoost), adaptive Boosting classification trees (AdaBoost), Bagged classification and regression trees (Bagged CART), and random forest (RF). To build the models, 339 groundwater resources’ locations and the spatial groundwater potential conditioning factors were used. Thereafter, the recursive feature elimination (RFE) method was applied to identify the key features. The RFE specified that the best number of features for groundwater potential modeling was 12 variables among 15 (with a mean Accuracy of about 0.84). The modeling results indicated that the Bagging models (i.e., RF and Bagged CART) had a higher performance than the Boosting models (i.e., AdaBoost and GamBoost). Overall, the RF model outperformed the other models (with accuracy = 0.86, Kappa = 0.67, Precision = 0.85, and Recall = 0.91). Also, the topographic position index’s predictive variables, valley depth, drainage density, elevation, and distance from stream had the highest contribution in the modeling process. Groundwater potential maps predicted in this study can help water resources managers and policymakers in the fields of watershed and aquifer management to preserve an optimal exploit from this important freshwater.
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