The Experts below are selected from a list of 11496 Experts worldwide ranked by ideXlab platform
Dieu Tien Bui - One of the best experts on this subject based on the ideXlab platform.
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a novel deep learning neural network approach for predicting Flash Flood susceptibility a case study at a high frequency tropical storm area
Science of The Total Environment, 2020Co-Authors: Dieu Tien Bui, Nhatduc Hoang, Francisco Martinezalvarez, Phuongthao Thi Ngo, Pham Viet Hoa, Tien Dat Pham, Pijush Samui, Romulus CostacheAbstract:Abstract This research proposes and evaluates a new approach for Flash Flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to Flash Flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN. A database for the study area, which includes factors of elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was established to train and validate the proposed model. Feature selection was carried out for these factors using the Information gain ratio. The results show that the DLNN attains a good prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value = 94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore, it could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in Flash Flood mitigation and land-use planning.
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a comparative study of kernel logistic regression radial basis function classifier multinomial naive bayes and logistic model tree for Flash Flood susceptibility mapping
Water, 2020Co-Authors: Binh Thai Pham, Indra Prakash, Tran Van Phong, Huu Duy Nguyen, Nadhir Alansari, Ata Amini, Tran Thi Tuyen, Hoang Phan Hai Yen, Dieu Tien BuiAbstract:Risk of Flash Floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naive Bayes (NBM), and Logistic Model Tree (LMT) to generate Flash Flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent Flood problems are being experienced. Performance of these four methods was evaluated to select the best method for Flash Flood susceptibility mapping. In the model studies, ten Flash Flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating Flash Flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate Flash Flood susceptibility maps with the objectives of identifying Flood-susceptible areas/zones for proper Flash Flood risk management.
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identification of areas prone to Flash Flood phenomena using multiple criteria decision making bivariate statistics machine learning and their ensembles
Science of The Total Environment, 2020Co-Authors: Romulus Costache, Dieu Tien BuiAbstract:Taking into account the exponential growth of the number of Flash-Floods events worldwide, the detection of areas prone to these natural hazards is one of the main activities taken in order to mitigate the negative effects of these risk phenomena. In the present paper, new modeling approaches, Alternating Decision Tree (ADT) integrated with IOE (ADT-IOE) and ADT integrated with AHP (ADT-AHP), were proposed for Flash-Flood susceptibility mapping across the Suha river catchment (Romania). Besides, two stand-alone methods, Index of Entropy (IOE) and Analytical Hierarchy Process (AHP), were also investigated. For this regard, 111 torrential points and 111 non-torrential points along with 8 Flash-Flood conditioning factors have been involved in the training process of the four models. The quality of the Flash-Flood models was checked by using the ROC Curve method, classification accuracy (CLA), and Kappa index. The result shows that the two ensemble models, the ADT-IOE (AUC = 0.972, CLC = 86.37%, Kappa statistics = 0.727) and the ADT-AHP (AUC = 0.926, CLA = 87.88%, Kappa statistics = 0.758), have high prediction performance and outperform the other models. Therefore, ADT-IOE and ADT-AHP are new and promising tools for Flash-Flood susceptibility modeling.
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Flash Flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods
Science of The Total Environment, 2019Co-Authors: Dieu Tien Bui, Phuongthao Thi Ngo, Tien Dat Pham, Paraskevas Tsangaratos, Binh Thai PhamAbstract:The main objective of the present study was to provide a novel methodological approach for Flash Flood susceptibility modeling based on a feature selection method (FSM) and tree based ensemble methods. The FSM, used a fuzzy rule based algorithm FURIA, as attribute evaluator, whereas GA were used as the search method, in order to obtain optimal set of variables used in Flood susceptibility modeling assessments. The novel FURIA-GA was combined with LogitBoost, Bagging and AdaBoost ensemble algorithms. The performance of the developed methodology was evaluated at the Bao Yen district and the Bac Ha district of Lao Cai Province in the Northeast region of Vietnam. For the case study, 654 Floods and twelve geo-environmental variables were used. The predictive performance of each model was estimated through the calculation of the classification accuracy, the sensitivity, the specificity, the success and predictive rate curve and the area under the curves (AUC). The FURIA-GA FSM compared to a conventional rule based method gave more accurate predictive results. Also, the FURIA-GA based models, presented higher learning and predictive ability compared to the ensemble models that had not undergone a FSM. Based on the predictive classification accuracy, FURIA-GA-Bagging (93.37%) outperformed FURIA-GA-LogitBoost (92.35%) and FURIA-GA-AdaBoost (89.03%). FURIA-GA-Bagging showed also the highest sensitivity (96.94%) and specificity (89.80%). On the other hand, the FURIA-GA-LogitBoost showed the lowest percentage in very high susceptible zone and the highest relative Flash-Flood density, whereas the FURIA-GA-AdaBoost achieved the highest prediction AUC value (0.9740), based on the prediction rate curve, followed by FURIA-GA-Bagging (0.9566), and FURIA-GA-LogitBoost (0.8955). It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings. The proposed models could be considered as a novel alternative investigation tools appropriate for Flash Flood susceptibility mapping.
Markus Stoffel - One of the best experts on this subject based on the ideXlab platform.
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unravelling past Flash Flood activity in a forested mountain catchment of the spanish central system
Journal of Hydrology, 2015Co-Authors: Markus Stoffel, Juan Antonio Ballesteroscanovas, Clara Rodriguezmorata, Virginia Garofanogomez, J M Rubiales, Raul SanchezsalgueroAbstract:Flash Floods represent one of the most common natural hazards in mountain catchments, and are frequent in Mediterranean environments. As a result of the widespread lack of reliable data on past events, the understanding of their spatio-temporal occurrence and their climatic triggers remains rather limited. Here, we present a dendrogeomorphic reconstruction of past Flash Flood activity in the Arroyo de los Puentes stream (Sierra de Guadarrama, Spanish Central System). We analyze a total of 287 increment cores from 178 disturbed Scots pine trees (Pinus sylvestris L.) which yielded indications on 212 growth disturbances related to past Flash Flood impact. In combination with local archives, meteorological data, annual forest management records and highly-resolved terrestrial data (i.e., LiDAR data and aerial imagery), the dendrogeomorphic time series allowed dating 25 Flash Floods over the last three centuries, with a major event leaving an intense geomorphic footprint throughout the catchment in 1936. The analysis of meteorological records suggests that the rainfall thresholds of Flash Floods vary with the seasonality of events. Dated Flash Floods in the 20th century were primarily related with synoptic troughs owing to the arrival of air masses from north and west on the Iberian Peninsula during negative indices of the North Atlantic Oscillation. The results of this study contribute considerably to a better understanding of hazards related with hydrogeomorphic processes in central Spain in general and in the Sierra de Guadarrama National Park in particular.
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Flash Flood impacts cause changes in wood anatomy of alnus glutinosa fraxinus angustifolia and quercus pyrenaica
Tree Physiology, 2010Co-Authors: J A Ballesteros, Markus Stoffel, Michelle Bollschweiler, J M Bodoque, Andres DiezherreroAbstract:Summary Flash Floods may influence the development of trees growing on channel bars and Floodplains. In this study, we analyze and quantify anatomical reactions to wounding in diffuse-porous (Alnus glutinosa L.) and ring-porous (Fraxinus angustifolia Vahl. and Quercus pyrenaica Willd.) trees in a Mediterranean environment. A total of 54 cross-sections and wedges were collected from trees that had been injured by past Flash Floods. From each of the samples, micro-sections were prepared at a tangential distance of 1.5 cm from the injury to determine wounding-related changes in radial width, tangential width and lumen of earlywood vessels, and fibers and parenchyma cells (FPC). In diffuse-porous A. glutinosa ,t he lumen area of vessels shows a significant (non-parametric test, P-value <0.05) decrease by almost 39% after wounding. For ring-porous F. angustifolia and Q. pyrenaica ,s igni ficant decreases in vessel lumen area are observed as well by 59 and 42%, respectively. Radial width of vessels was generally more sensitive to the decrease than tangential width, but statistically significant values were only observed in F. angustifolia. Changes in the dimensions of earlywood FPC largely differed between species. While in ring-porous F. angustifolia and Q. pyrenaica the lumen of FPC dropped by 22 and 34% after wounding, we observed an increase in FPC lumen area in diffuse-porous A. glutinosa of ∼35%. Our data clearly show that A. glutinosa represents a valuable species for Flash-Flood research in vulnerable Mediterranean environments. For this species, it will be possible in the future to gather information on past Flash Floods with non-destructive sampling based on increment cores. In ring-porous F. angustifolia and Q. pyrenaica, Flash Floods leave less drastic, yet still recognizable, signatures of Flash-Flood activity through significant changes in vessel lumen area. In contrast, the use of changes in FPC dimensions appears less feasible for the determination of past Flash-Flood events as these two species do not react with the same intensity and clarity as A. glutinosa.
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Flash Flood impacts cause changes in wood anatomy of alnus glutinosa fraxinus angustifolia and quercus pyrenaica
Tree Physiology, 2010Co-Authors: J A Ballesteros, Markus Stoffel, Michelle Bollschweiler, J M Bodoque, Andres DiezherreroAbstract:Flash Floods may influence the development of trees growing on channel bars and Floodplains. In this study, we analyze and quantify anatomical reactions to wounding in diffuse-porous (Alnus glutinosa L.) and ring-porous (Fraxinus angustifolia Vahl. and Quercus pyrenaica Willd.) trees in a Mediterranean environment. A total of 54 cross-sections and wedges were collected from trees that had been injured by past Flash Floods. From each of the samples, micro-sections were prepared at a tangential distance of 1.5 cm from the injury to determine wounding-related changes in radial width, tangential width and lumen of earlywood vessels, and fibers and parenchyma cells (FPC). In diffuse-porous A. glutinosa, the lumen area of vessels shows a significant (non-parametric test, P-value <0.05) decrease by almost 39% after wounding. For ring-porous F. angustifolia and Q. pyrenaica, significant decreases in vessel lumen area are observed as well by 59 and 42%, respectively. Radial width of vessels was generally more sensitive to the decrease than tangential width, but statistically significant values were only observed in F. angustifolia. Changes in the dimensions of earlywood FPC largely differed between species. While in ring-porous F. angustifolia and Q. pyrenaica the lumen of FPC dropped by 22 and 34% after wounding, we observed an increase in FPC lumen area in diffuse-porous A. glutinosa of approximately 35%. Our data clearly show that A. glutinosa represents a valuable species for Flash-Flood research in vulnerable Mediterranean environments. For this species, it will be possible in the future to gather information on past Flash Floods with non-destructive sampling based on increment cores. In ring-porous F. angustifolia and Q. pyrenaica, Flash Floods leave less drastic, yet still recognizable, signatures of Flash-Flood activity through significant changes in vessel lumen area. In contrast, the use of changes in FPC dimensions appears less feasible for the determination of past Flash-Flood events as these two species do not react with the same intensity and clarity as A. glutinosa.
Indra Prakash - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of kernel logistic regression radial basis function classifier multinomial naive bayes and logistic model tree for Flash Flood susceptibility mapping
Water, 2020Co-Authors: Binh Thai Pham, Indra Prakash, Tran Van Phong, Huu Duy Nguyen, Nadhir Alansari, Ata Amini, Tran Thi Tuyen, Hoang Phan Hai Yen, Dieu Tien BuiAbstract:Risk of Flash Floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naive Bayes (NBM), and Logistic Model Tree (LMT) to generate Flash Flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent Flood problems are being experienced. Performance of these four methods was evaluated to select the best method for Flash Flood susceptibility mapping. In the model studies, ten Flash Flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating Flash Flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate Flash Flood susceptibility maps with the objectives of identifying Flood-susceptible areas/zones for proper Flash Flood risk management.
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a comparative assessment of decision trees algorithms for Flash Flood susceptibility modeling at haraz watershed northern iran
Science of The Total Environment, 2018Co-Authors: Khaba Khosravi, Inh Thai Pham, Kamra Chapi, Ataollah Shirzadi, Hima Shahabi, Inge Revhaug, Indra PrakashAbstract:Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of Flash Flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of Flood susceptible areas can be done with the help of machine learning techniques for proper timely management of Flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naive Bayes Trees (NBT), and Alternating Decision Trees (ADT) for Flash Flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past Flood locations and eleven Flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for Flash Flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining Flood susceptible areas.
Binh Thai Pham - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of kernel logistic regression radial basis function classifier multinomial naive bayes and logistic model tree for Flash Flood susceptibility mapping
Water, 2020Co-Authors: Binh Thai Pham, Indra Prakash, Tran Van Phong, Huu Duy Nguyen, Nadhir Alansari, Ata Amini, Tran Thi Tuyen, Hoang Phan Hai Yen, Dieu Tien BuiAbstract:Risk of Flash Floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naive Bayes (NBM), and Logistic Model Tree (LMT) to generate Flash Flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent Flood problems are being experienced. Performance of these four methods was evaluated to select the best method for Flash Flood susceptibility mapping. In the model studies, ten Flash Flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating Flash Flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate Flash Flood susceptibility maps with the objectives of identifying Flood-susceptible areas/zones for proper Flash Flood risk management.
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Flash Flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods
Science of The Total Environment, 2019Co-Authors: Dieu Tien Bui, Phuongthao Thi Ngo, Tien Dat Pham, Paraskevas Tsangaratos, Binh Thai PhamAbstract:The main objective of the present study was to provide a novel methodological approach for Flash Flood susceptibility modeling based on a feature selection method (FSM) and tree based ensemble methods. The FSM, used a fuzzy rule based algorithm FURIA, as attribute evaluator, whereas GA were used as the search method, in order to obtain optimal set of variables used in Flood susceptibility modeling assessments. The novel FURIA-GA was combined with LogitBoost, Bagging and AdaBoost ensemble algorithms. The performance of the developed methodology was evaluated at the Bao Yen district and the Bac Ha district of Lao Cai Province in the Northeast region of Vietnam. For the case study, 654 Floods and twelve geo-environmental variables were used. The predictive performance of each model was estimated through the calculation of the classification accuracy, the sensitivity, the specificity, the success and predictive rate curve and the area under the curves (AUC). The FURIA-GA FSM compared to a conventional rule based method gave more accurate predictive results. Also, the FURIA-GA based models, presented higher learning and predictive ability compared to the ensemble models that had not undergone a FSM. Based on the predictive classification accuracy, FURIA-GA-Bagging (93.37%) outperformed FURIA-GA-LogitBoost (92.35%) and FURIA-GA-AdaBoost (89.03%). FURIA-GA-Bagging showed also the highest sensitivity (96.94%) and specificity (89.80%). On the other hand, the FURIA-GA-LogitBoost showed the lowest percentage in very high susceptible zone and the highest relative Flash-Flood density, whereas the FURIA-GA-AdaBoost achieved the highest prediction AUC value (0.9740), based on the prediction rate curve, followed by FURIA-GA-Bagging (0.9566), and FURIA-GA-LogitBoost (0.8955). It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings. The proposed models could be considered as a novel alternative investigation tools appropriate for Flash Flood susceptibility mapping.
Romulus Costache - One of the best experts on this subject based on the ideXlab platform.
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comparative assessment of the Flash Flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models
Science of The Total Environment, 2020Co-Authors: Romulus Costache, Haoyuan Hong, Quoc Bao PhamAbstract:The present study is carried out in the context of the continuous increase, worldwide, of the number of Flash-Floods phenomena. Also, there is an evident increase of the size of the damages caused by these hazards. Bâsca Chiojdului River Basin is one of the most affected areas in Romania by Flash-Flood phenomena. Therefore, Flash-Flood Potential Index (FFPI) was defined and calculated across the Bâsca Chiojdului river basin by using one bivariate statistical method (Statistical Index) and its novel ensemble with the following machine learning models: Logistic Regression, Classification and Regression Trees, Multilayer Perceptron, Random Forest and Support Vector Machine and Decision Tree CART. In a first stage, the areas with torrentiality were digitized based on orthophotomaps and field observations. These regions, together with an equal number of non-torrential pixels, were further divided into training surfaces (70%) and validating surfaces (30%). The next step of the analysis consisted of the selection of Flash-Flood conditioning factors based on the multicollinearity investigation and predictive ability estimation through Information Gain method. Eight factors, from a total of ten Flash-Floods predictors, were selected in order to be included in the FFPI calculation process. By applying the models represented by Statistical Index and its ensemble with the machine learning algorithms, the weight of each conditioning factor and of each factor class/category in the FFPI equations was established. Once the weight values were derived, the FFPI values across the Bâsca Chiojdului river basin were calculated by overlaying the Flash-Flood predictors in GIS environment. According to the results obtained, the central part of Bâsca Chiojdului river basin has the highest susceptibility to Flash-Flood phenomena. Thus, around 30% of the study site has high and very high values of FFPI. The results validation was carried out by applying the Prediction Rate and Success Rate. The methods revealed the fact that the Multilayer Perceptron - Statistical Index (MLP-SI) ensemble has the highest efficiency among the 3 methods.
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a novel deep learning neural network approach for predicting Flash Flood susceptibility a case study at a high frequency tropical storm area
Science of The Total Environment, 2020Co-Authors: Dieu Tien Bui, Nhatduc Hoang, Francisco Martinezalvarez, Phuongthao Thi Ngo, Pham Viet Hoa, Tien Dat Pham, Pijush Samui, Romulus CostacheAbstract:Abstract This research proposes and evaluates a new approach for Flash Flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to Flash Flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN. A database for the study area, which includes factors of elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was established to train and validate the proposed model. Feature selection was carried out for these factors using the Information gain ratio. The results show that the DLNN attains a good prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value = 94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore, it could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in Flash Flood mitigation and land-use planning.
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identification of areas prone to Flash Flood phenomena using multiple criteria decision making bivariate statistics machine learning and their ensembles
Science of The Total Environment, 2020Co-Authors: Romulus Costache, Dieu Tien BuiAbstract:Taking into account the exponential growth of the number of Flash-Floods events worldwide, the detection of areas prone to these natural hazards is one of the main activities taken in order to mitigate the negative effects of these risk phenomena. In the present paper, new modeling approaches, Alternating Decision Tree (ADT) integrated with IOE (ADT-IOE) and ADT integrated with AHP (ADT-AHP), were proposed for Flash-Flood susceptibility mapping across the Suha river catchment (Romania). Besides, two stand-alone methods, Index of Entropy (IOE) and Analytical Hierarchy Process (AHP), were also investigated. For this regard, 111 torrential points and 111 non-torrential points along with 8 Flash-Flood conditioning factors have been involved in the training process of the four models. The quality of the Flash-Flood models was checked by using the ROC Curve method, classification accuracy (CLA), and Kappa index. The result shows that the two ensemble models, the ADT-IOE (AUC = 0.972, CLC = 86.37%, Kappa statistics = 0.727) and the ADT-AHP (AUC = 0.926, CLA = 87.88%, Kappa statistics = 0.758), have high prediction performance and outperform the other models. Therefore, ADT-IOE and ADT-AHP are new and promising tools for Flash-Flood susceptibility modeling.