GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

Pham, Binh Thai; Avand, Mohammadtaghi; Janizadeh, Saeid; Phong, Tran Van; Al‐Ansari, Nadhir; Ho, Lanh Si; Das, Sumit; Le, Hiep Van; Amini, Ata; Bozchaloei, Saeid Khosrobeigi; Jafari, Faeze; Prakash, Indra

doi:10.3390/w12030683

Cited by 153 publications

(57 citation statements)

References 122 publications

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“…For example, Nhu et al [36] showed that reduced error pruning tree (REPT) performed better in combination with RSS than the bagging and AdaBoost techniques for gully erosion prediction, whereas Pham et al [92] reported that the REPT model performed better with rotation forest and bagging than its combination with the RSS and multiboost for landslide prediction. Different results have also been reported for flood prediction based on the ensemble models [93,94]. From these studies, we can conclude that the machine learning and ensemble learning techniques are greatly case-and site-specific, and that their performances depend heavily on the local conditions that the training datasets are developed upon, indicating that the application of different methods in different regions should be continued to find the optimum method for each environmental setting [95].…”

Section: Discussionmentioning

confidence: 94%

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Nguyen¹,

Avand

et al. 2020

Applied Sciences

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142

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Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.

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Section: Discussionmentioning

confidence: 94%

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Nguyen¹,

Avand

et al. 2020

Applied Sciences

Self Cite

142

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“…Detail description of these indices is presented in published literature [61,[70][71][72][73][74][75][76][77]. In general, lower RMSE and higher values of AUC, Kappa, ACC, SPF, SST, NPV, and PPV indicate higher model performance [57,58,65,[78][79][80][81][82]. Mathematically, these performance indices are given by [60,77,[83][84][85][86][87]:…”

Section: Validation Methodsmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

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113

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The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

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“…These methods extract related patterns in historical data to predict future events [73]. Data mining methods used to predict gully erosion include logistic regression (LR) [2,30,[74][75][76][77], artificial neural network (ANN) [20,48,[78][79][80], random subspace (RS) [48,62,81], maximum entropy (ME) [82], artificial neural fuzzy system (ANFIS) [56,[83][84][85][86], support vector machine (SVM) [18,59,73], fuzzy analytical network (FAN) [37], multi-criteria decision analysis (MCDA) [87,88], evidential belief function (EBF) [88,89], classification and regression tree (CART) [90,91], random forest (RF) [39,52,[92][93][94], rotation forest (RoF) [95], weights of evidence (WofE) [96], frequency ratio (FR) [28,97], BFTree for gully headcut [81], boosted regression [24], ADTree, RF-ADTree [73,76,98], and naive B...…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

et al. 2020

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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.

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GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

Cited by 153 publications

References 122 publications

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

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