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The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.
Trung-Hieu Tran; Nguyen Duc Dam; Fazal E. Jalal; Nadhir Al-Ansari; Lanh Si Ho; Tran Van Phong; Mudassir Iqbal; Hiep Van Le; Hanh Bich Thi Nguyen; Indra Prakash; Binh Thai Pham. GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India. Mathematical Problems in Engineering 2021, 2021, 1 -19.
AMA StyleTrung-Hieu Tran, Nguyen Duc Dam, Fazal E. Jalal, Nadhir Al-Ansari, Lanh Si Ho, Tran Van Phong, Mudassir Iqbal, Hiep Van Le, Hanh Bich Thi Nguyen, Indra Prakash, Binh Thai Pham. GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India. Mathematical Problems in Engineering. 2021; 2021 ():1-19.
Chicago/Turabian StyleTrung-Hieu Tran; Nguyen Duc Dam; Fazal E. Jalal; Nadhir Al-Ansari; Lanh Si Ho; Tran Van Phong; Mudassir Iqbal; Hiep Van Le; Hanh Bich Thi Nguyen; Indra Prakash; Binh Thai Pham. 2021. "GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India." Mathematical Problems in Engineering 2021, no. : 1-19.
Groundwater is one of the major valuable water resources for the use of communities, agriculture and industries. In the present study, we have developed three novel hybrid Artificial Intelligence (AI) models which is a combination of Modified RealAdaBoost (MRAB), Bagging (BA), and Rotation Forest (RF) ensembles with Functional Tree (FT) base classifier for the Groundwater Potential Mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo‐hydrological data of 130 groundwater wells and 12 topographical and geo‐environmental factors were used in the model studies. One‐R Attribute Evaluation (ORAE) feature selection method was used for the selection of relevant input parameters for the development of AI models. Performance of these models was evaluated using various statistical measures including Area Under the receiver operation Curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness‐of‐fit and prediction accuracy, but MRAB‐FT (AUC=0.742) model outperformed RF‐FT (AUC=0.736), BA‐FT (AUC=0.714) and single FT (AUC=0.674) models. Therefore, the MRAB‐FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation.
Tran Van Phong; Binh Thai Pham; Phan Trong Trinh; Hai‐Bang Ly; Vu Quoc Hung; Lanh Si Ho; Hiep Van le; Lai Hop Phong; Mohammadtaghi Avand; Indra Prakash. Groundwater Potential Mapping Using GIS Based Hybrid Artificial Intelligence Methods. Groundwater 2021, 1 .
AMA StyleTran Van Phong, Binh Thai Pham, Phan Trong Trinh, Hai‐Bang Ly, Vu Quoc Hung, Lanh Si Ho, Hiep Van le, Lai Hop Phong, Mohammadtaghi Avand, Indra Prakash. Groundwater Potential Mapping Using GIS Based Hybrid Artificial Intelligence Methods. Groundwater. 2021; ():1.
Chicago/Turabian StyleTran Van Phong; Binh Thai Pham; Phan Trong Trinh; Hai‐Bang Ly; Vu Quoc Hung; Lanh Si Ho; Hiep Van le; Lai Hop Phong; Mohammadtaghi Avand; Indra Prakash. 2021. "Groundwater Potential Mapping Using GIS Based Hybrid Artificial Intelligence Methods." Groundwater , no. : 1.
Flood risk assessment is an important task for disaster management activities in flood-prone areas. Therefore, it is crucial to develop accurate flood risk assessment maps. In this study, we proposed a flood risk assessment framework which combines flood susceptibility assessment and flood consequences (human health and financial impact) for developing a final flood risk assessment map using Multi-Criteria Decision Analysis (MCDA) method. Two hybrid Artificial Intelligence (AI) models, namely ABMDT (AdaBoost-DT) and BDT (Bagging-DT) were developed with Decision Table (DT) as a base classifier for creating a flood susceptibility map. We used 847 flood locations of major flooding events in the years 2007, 2009 and 2013 in Quang Nam province of Vietnam; and 14 flood influencing factors of topography, geology, hydrology and environment to construct and validate the hybrid AI models. Various statistical measures were used to validate the models, including the Area Under Receiver Operating Characteristic (ROC) Curve called AUC. Results show that all the proposed models performed well, but the performance of the BDT model (AUC=0.96) is the best in comparison to other models ABMDT (AUC=0.953) and single DT (AUC=0.929). Therefore, the flood susceptibility map produced by the BDT model was used to combine with a flood consequences map to develop a reliable flood risk assessment map for the study area. The final flood risk map can provide a useful source for better flood hazard management of the study area, and the proposed framework and models can be applied to other flood-prone areas.
Binh Thai Pham; Chinh Luu; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Thai Quoc Tran; Huong Thu Ta; Indra Prakash. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology 2020, 592, 125815 .
AMA StyleBinh Thai Pham, Chinh Luu, Tran Van Phong, Huu Duy Nguyen, Hiep Van Le, Thai Quoc Tran, Huong Thu Ta, Indra Prakash. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology. 2020; 592 ():125815.
Chicago/Turabian StyleBinh Thai Pham; Chinh Luu; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Thai Quoc Tran; Huong Thu Ta; Indra Prakash. 2020. "Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam." Journal of Hydrology 592, no. : 125815.
In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area.
Binh Thai Pham; Tran Van Phong; Mohammadtaghi Avand; Nadhir Al-Ansari; Sushant K. Singh; Hiep Van Le; Indra Prakash. Improving Voting Feature Intervals for Spatial Prediction of Landslides. Mathematical Problems in Engineering 2020, 2020, 1 -15.
AMA StyleBinh Thai Pham, Tran Van Phong, Mohammadtaghi Avand, Nadhir Al-Ansari, Sushant K. Singh, Hiep Van Le, Indra Prakash. Improving Voting Feature Intervals for Spatial Prediction of Landslides. Mathematical Problems in Engineering. 2020; 2020 ():1-15.
Chicago/Turabian StyleBinh Thai Pham; Tran Van Phong; Mohammadtaghi Avand; Nadhir Al-Ansari; Sushant K. Singh; Hiep Van Le; Indra Prakash. 2020. "Improving Voting Feature Intervals for Spatial Prediction of Landslides." Mathematical Problems in Engineering 2020, no. : 1-15.
Using multiple ensemble learning techniques for improving the predictive accuracy of landslide models is an active research area. In this study, we combined a radial basis function (RBF) neural network (RBFN) with the Random Subspace (RSS), Attribute Selected Classifier (ASC), Cascade Generalization (CG), Dagging for spatial prediction of landslide susceptibility in the Van Chan district, Yen Yen Bai Province, Vietnam. A geospatial database that contained records from 167 historical landslides and 12 conditioning factors (slope, aspect, elevation, curvature, slope length, valley depth, topographic wetness index, and terrain ruggedness index, and distance to rivers, roads, and faults) were used to develop the ensemble models. The models were validated via area under the receiver operating characteristic curve (AUC) and several other performance metrics (i.e., positive predictive value, negative predictive value, sensitivity, specificity, accuracy, and Kappa). Although the single RBFN model (AUC = 0.799) performed better than the ensemble models (AUCaverage = 0.77) in the training phase, the ensemble models (AUCaverage = 0.83) outperformed RBFN (AUC = 0.79) in the validation phase, demonstrating superior predictive performance of the ensemble models for the prediction of future landslides. Our study provides insights for developing reliable landslide predictive models for different landslide-prone regions around the world.
Binh Thai Pham; Trung Nguyen-Thoi; Chongchong Qi; Tran Van Phong; Jie Dou; Lanh Si Ho; Hiep Van Le; Indra Prakash. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA 2020, 195, 104805 .
AMA StyleBinh Thai Pham, Trung Nguyen-Thoi, Chongchong Qi, Tran Van Phong, Jie Dou, Lanh Si Ho, Hiep Van Le, Indra Prakash. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA. 2020; 195 ():104805.
Chicago/Turabian StyleBinh Thai Pham; Trung Nguyen-Thoi; Chongchong Qi; Tran Van Phong; Jie Dou; Lanh Si Ho; Hiep Van Le; Indra Prakash. 2020. "Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping." CATENA 195, no. : 104805.
Development of zoning and flood-forecasting models is essential for making optimal management decisions before and after floods. The Komijan watershed of Markazi Province, Iran is often affected by floods that have caused great material damage and loss of life. The main objective of this study is to use a new machine-learning method to create three models: best-first decision tree (BFT), a bagging best-first decision tree (BBFT) ensemble and a dagging best-first decision tree (DBFT) ensemble to spatially predict flood probability. Twelve conditioning-factor measures for 272 locations of past floods were used to train and test three models. Receiver operating characteristic (ROC), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), kappa (K), and root mean square error (RMSE) are applied to compare and validate the models. The results are that all three models performed well in mapping, flood probabilities (AUC > 0.904). The BBFT model was best, however, with an AUC = 0.96. Based on the results of the Relief-F attribute evaluation method, two soil and slope factors were weighted highest among the parameters, indicating that they are the most important flood-conditioning factors. These models may improve identification of zones that are most susceptible to flooding, improving the capacity for risk management and providing more detailed information for managers and decision-makers.
Peyman Yariyan; Saeid Janizadeh; Tran Van Phong; Huu Duy Nguyen; Romulus Costache; Hiep Van Le; Binh Thai Pham; Biswajeet Pradhan; John P. Tiefenbacher. Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping. Water Resources Management 2020, 34, 3037 -3053.
AMA StylePeyman Yariyan, Saeid Janizadeh, Tran Van Phong, Huu Duy Nguyen, Romulus Costache, Hiep Van Le, Binh Thai Pham, Biswajeet Pradhan, John P. Tiefenbacher. Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping. Water Resources Management. 2020; 34 (9):3037-3053.
Chicago/Turabian StylePeyman Yariyan; Saeid Janizadeh; Tran Van Phong; Huu Duy Nguyen; Romulus Costache; Hiep Van Le; Binh Thai Pham; Biswajeet Pradhan; John P. Tiefenbacher. 2020. "Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping." Water Resources Management 34, no. 9: 3037-3053.
Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.
Binh Thai Pham; Abolfazl Jaafari; Mohammadtaghi Avand; Nadhir Al-Ansari; Tran Dinh Du; Hoang Phan Hai Yen; Tran Van Phong; Duy Huu Nguyen; Hiep Van Le; Davood Mafi-Gholami; Indra Prakash; Hoang Thi Thuy; Tran Thi Tuyen. Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry 2020, 12, 1022 .
AMA StyleBinh Thai Pham, Abolfazl Jaafari, Mohammadtaghi Avand, Nadhir Al-Ansari, Tran Dinh Du, Hoang Phan Hai Yen, Tran Van Phong, Duy Huu Nguyen, Hiep Van Le, Davood Mafi-Gholami, Indra Prakash, Hoang Thi Thuy, Tran Thi Tuyen. Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry. 2020; 12 (6):1022.
Chicago/Turabian StyleBinh Thai Pham; Abolfazl Jaafari; Mohammadtaghi Avand; Nadhir Al-Ansari; Tran Dinh Du; Hoang Phan Hai Yen; Tran Van Phong; Duy Huu Nguyen; Hiep Van Le; Davood Mafi-Gholami; Indra Prakash; Hoang Thi Thuy; Tran Thi Tuyen. 2020. "Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction." Symmetry 12, no. 6: 1022.
Landslide susceptibility mapping has become one of the most important tools for the management of landslide hazards. In this study, we proposed a novel approach to improve the performance of Credal Decision Tree (CDT) by using four ensemble frameworks: Bagging, Dagging, Decorate, and Rotation Forest (RF) for landslide susceptibility mapping. A total number of 180 past and present landslides data of the Muong Lay district (Viet Nam) was analyzed and used for generating training and validation of the models. Several standard statistical performance evaluation metrics, such as negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, Kappa, Area Under the receiver operating Characteristic curve (AUC) were used to evaluate performance of the models. Results indicated that all the developed and applied models performed well (AUC: 0.842–0.886) but performance of the RF-CDT (AUC: 0.886) model is the best. Therefore, the RF-CDT ensemble model can be used for the correct landslide susceptibility mapping and for proper landslide management not only of the study area but also other hilly areas of the world.
Binh Thai Pham; Tran Van Phong; Trung Nguyen-Thoi; Phan Trong Trinh; Quoc Cuong Tran; Lanh Si Ho; Sushant K. Singh; Tran Thi Thanh Duyen; Loan Thi Nguyen; Huy Quang Le; Hiep Van Le; Nguyen Thi Bich Hanh; Nguyen Kim Quoc; Indra Prakash. GIS-based ensemble soft computing models for landslide susceptibility mapping. Advances in Space Research 2020, 66, 1303 -1320.
AMA StyleBinh Thai Pham, Tran Van Phong, Trung Nguyen-Thoi, Phan Trong Trinh, Quoc Cuong Tran, Lanh Si Ho, Sushant K. Singh, Tran Thi Thanh Duyen, Loan Thi Nguyen, Huy Quang Le, Hiep Van Le, Nguyen Thi Bich Hanh, Nguyen Kim Quoc, Indra Prakash. GIS-based ensemble soft computing models for landslide susceptibility mapping. Advances in Space Research. 2020; 66 (6):1303-1320.
Chicago/Turabian StyleBinh Thai Pham; Tran Van Phong; Trung Nguyen-Thoi; Phan Trong Trinh; Quoc Cuong Tran; Lanh Si Ho; Sushant K. Singh; Tran Thi Thanh Duyen; Loan Thi Nguyen; Huy Quang Le; Hiep Van Le; Nguyen Thi Bich Hanh; Nguyen Kim Quoc; Indra Prakash. 2020. "GIS-based ensemble soft computing models for landslide susceptibility mapping." Advances in Space Research 66, no. 6: 1303-1320.
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.
Phong Tung Nguyen; Duong Hai Ha; Abolfazl Jaafari; Huu Duy Nguyen; Tran Van Phong; Nadhir Al-Ansari; Indra Prakash; Hiep Van Le; Binh Thai Pham. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam. International Journal of Environmental Research and Public Health 2020, 17, 2473 .
AMA StylePhong Tung Nguyen, Duong Hai Ha, Abolfazl Jaafari, Huu Duy Nguyen, Tran Van Phong, Nadhir Al-Ansari, Indra Prakash, Hiep Van Le, Binh Thai Pham. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam. International Journal of Environmental Research and Public Health. 2020; 17 (7):2473.
Chicago/Turabian StylePhong Tung Nguyen; Duong Hai Ha; Abolfazl Jaafari; Huu Duy Nguyen; Tran Van Phong; Nadhir Al-Ansari; Indra Prakash; Hiep Van Le; Binh Thai Pham. 2020. "Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam." International Journal of Environmental Research and Public Health 17, no. 7: 2473.
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.
Phong Tung Nguyen; Duong Hai Ha; Mohammadtaghi Avand; Abolfazl Jaafari; Huu Duy Nguyen; Nadhir Al-Ansari; Tran Van Phong; Rohit Sharma; Raghvendra Kumar; Hiep Van Le; Lanh Si Ho; Indra Prakash; Binh Thai Pham. Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Applied Sciences 2020, 10, 2469 .
AMA StylePhong Tung Nguyen, Duong Hai Ha, Mohammadtaghi Avand, Abolfazl Jaafari, Huu Duy Nguyen, Nadhir Al-Ansari, Tran Van Phong, Rohit Sharma, Raghvendra Kumar, Hiep Van Le, Lanh Si Ho, Indra Prakash, Binh Thai Pham. Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Applied Sciences. 2020; 10 (7):2469.
Chicago/Turabian StylePhong Tung Nguyen; Duong Hai Ha; Mohammadtaghi Avand; Abolfazl Jaafari; Huu Duy Nguyen; Nadhir Al-Ansari; Tran Van Phong; Rohit Sharma; Raghvendra Kumar; Hiep Van Le; Lanh Si Ho; Indra Prakash; Binh Thai Pham. 2020. "Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping." Applied Sciences 10, no. 7: 2469.
Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.
Phong Tung Nguyen; Duong Hai Ha; Huu Duy Nguyen; Tran Van Phong; Phan Trong Trinh; Nadhir Al-Ansari; Hiep Van Le; Binh Thai Pham; Lanh Si Ho; Indra Prakash. Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling. Sustainability 2020, 12, 2622 .
AMA StylePhong Tung Nguyen, Duong Hai Ha, Huu Duy Nguyen, Tran Van Phong, Phan Trong Trinh, Nadhir Al-Ansari, Hiep Van Le, Binh Thai Pham, Lanh Si Ho, Indra Prakash. Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling. Sustainability. 2020; 12 (7):2622.
Chicago/Turabian StylePhong Tung Nguyen; Duong Hai Ha; Huu Duy Nguyen; Tran Van Phong; Phan Trong Trinh; Nadhir Al-Ansari; Hiep Van Le; Binh Thai Pham; Lanh Si Ho; Indra Prakash. 2020. "Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling." Sustainability 12, no. 7: 2622.
Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood locations and development of accurate flash flood susceptibility maps are important for proper flash flood management of a region. With this objective, we proposed and compared several novel hybrid computational approaches of machine learning methods for flash flood susceptibility mapping, namely AdaBoostM1 based Credal Decision Tree (ABM-CDT); Bagging based Credal Decision Tree (Bag-CDT); Dagging based Credal Decision Tree (Dag-CDT); MultiBoostAB based Credal Decision Tree (MBAB-CDT), and single Credal Decision Tree (CDT). These models were applied at a catchment of Markazi state in Iran. About 320 past flash flood events and nine flash flood influencing factors, namely distance from rivers, aspect, elevation, slope, rainfall, distance from faults, soil, land use, and lithology were considered and analyzed for the development of flash flood susceptibility maps. Correlation based feature selection method was used to validate and select the important factors for modeling of flash floods. Based on this feature selection analysis, only eight factors (distance from rivers, aspect, elevation, slope, rainfall, soil, land use, and lithology) were selected for the modeling, where distance to rivers is the most important factor for modeling of flash flood in this area. Performance of the models was validated and compared by using several robust metrics such as statistical measures and Area Under the Receiver Operating Characteristic (AUC) curve. The results of this study suggested that ABM-CDT (AUC = 0.957) has the best predictive capability in terms of accuracy, followed by Dag-CDT (AUC = 0.947), MBAB-CDT (AUC = 0.933), Bag-CDT (AUC = 0.932), and CDT (0.900), respectively. The proposed methods presented in this study would help in the development of accurate flash flood susceptible maps of watershed areas not only in Iran but also other parts of the world.
Binh Thai Pham; Mohammadtaghi Avand; Saeid Janizadeh; Tran Van Phong; Nadhir Al-Ansari; L.S. Ho; Sumit Das; Hiep Van Le; Ata Amini; Saeid Khosrobeigi Bozchaloei; Faeze Jafari; Indra Prakash. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. Water 2020, 12, 683 .
AMA StyleBinh Thai Pham, Mohammadtaghi Avand, Saeid Janizadeh, Tran Van Phong, Nadhir Al-Ansari, L.S. Ho, Sumit Das, Hiep Van Le, Ata Amini, Saeid Khosrobeigi Bozchaloei, Faeze Jafari, Indra Prakash. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. Water. 2020; 12 (3):683.
Chicago/Turabian StyleBinh Thai Pham; Mohammadtaghi Avand; Saeid Janizadeh; Tran Van Phong; Nadhir Al-Ansari; L.S. Ho; Sumit Das; Hiep Van Le; Ata Amini; Saeid Khosrobeigi Bozchaloei; Faeze Jafari; Indra Prakash. 2020. "GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment." Water 12, no. 3: 683.
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 Naïve 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.
Binh Thai Pham; Tran Van Phong; Huu Duy Nguyen; Chongchong Qi; Nadhir Al-Ansari; Ata Amini; Lanh Si Ho; Tran Thi Tuyen; Hoang Phan Hai Yen; Hai-Bang Ly; Indra Prakash; Dieu Tien Bui. A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water 2020, 12, 239 .
AMA StyleBinh Thai Pham, Tran Van Phong, Huu Duy Nguyen, Chongchong Qi, Nadhir Al-Ansari, Ata Amini, Lanh Si Ho, Tran Thi Tuyen, Hoang Phan Hai Yen, Hai-Bang Ly, Indra Prakash, Dieu Tien Bui. A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water. 2020; 12 (1):239.
Chicago/Turabian StyleBinh Thai Pham; Tran Van Phong; Huu Duy Nguyen; Chongchong Qi; Nadhir Al-Ansari; Ata Amini; Lanh Si Ho; Tran Thi Tuyen; Hoang Phan Hai Yen; Hai-Bang Ly; Indra Prakash; Dieu Tien Bui. 2020. "A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping." Water 12, no. 1: 239.
Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.
Viet-Tien Nguyen; Trong Hien Tran; Ngoc Anh Ha; Van Liem Ngo; Al-Ansari Nadhir; Van Phong Tran; Huu Duy Nguyen; Malek M. A.; Ata Amini; Indra Prakash; L.S. Ho; Binh Thai Pham. GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability 2019, 11, 7118 .
AMA StyleViet-Tien Nguyen, Trong Hien Tran, Ngoc Anh Ha, Van Liem Ngo, Al-Ansari Nadhir, Van Phong Tran, Huu Duy Nguyen, Malek M. A., Ata Amini, Indra Prakash, L.S. Ho, Binh Thai Pham. GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability. 2019; 11 (24):7118.
Chicago/Turabian StyleViet-Tien Nguyen; Trong Hien Tran; Ngoc Anh Ha; Van Liem Ngo; Al-Ansari Nadhir; Van Phong Tran; Huu Duy Nguyen; Malek M. A.; Ata Amini; Indra Prakash; L.S. Ho; Binh Thai Pham. 2019. "GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam." Sustainability 11, no. 24: 7118.
The main objective of this study is to propose a novel hybrid model of a sequential minimal optimization and support vector machine (SMOSVM) for accurate landslide susceptibility mapping. For this task, one of the landslide prone areas of Vietnam, the Mu Cang Chai District located in Yen Bai Province was selected. In total, 248 landslide locations and 15 landslide-affecting factors were selected for landslide modeling and analysis. Predictive capability of SMOSVM was evaluated and compared with other landslide models, namely a hybrid model of the cascade generalization optimization-based support vector machine (CGSVM), individual models, such as support vector machines (SVM) and naïve Bayes trees (NBT). For validation, different quantitative criteria such as statistical based methods and area under the receiver operating characteristic curve (AUC) technique were used. Results of the study show that the SMOSVM model (AUC = 0.824) has the highest performance for landslide susceptibility mapping, followed by CGSVM (AUC = 0.815), SVM (AUC = 0.804), and NBT (AUC = 0.800) models, respectively. Thus, the proposed novel SMOSVM model is a promising method for better landslide susceptibility mapping and prediction, which can be applied also in other landslide prone areas.
Binh Thai Pham; Indra Prakash; Wei Chen; Hai-Bang Ly; Lanh Si Ho; Ebrahim Omidvar; Van Phong Tran; Dieu Tien Bui. A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping. Sustainability 2019, 11, 6323 .
AMA StyleBinh Thai Pham, Indra Prakash, Wei Chen, Hai-Bang Ly, Lanh Si Ho, Ebrahim Omidvar, Van Phong Tran, Dieu Tien Bui. A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping. Sustainability. 2019; 11 (22):6323.
Chicago/Turabian StyleBinh Thai Pham; Indra Prakash; Wei Chen; Hai-Bang Ly; Lanh Si Ho; Ebrahim Omidvar; Van Phong Tran; Dieu Tien Bui. 2019. "A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping." Sustainability 11, no. 22: 6323.
Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.
Saeid Janizadeh; Mohammadtaghi Avand; Abolfazl Jaafari; Tran Van Phong; Mahmoud Bayat; Ebrahim Ahmadisharaf; Indra Prakash; Binh Thai Pham; Saro Lee. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability 2019, 11, 5426 .
AMA StyleSaeid Janizadeh, Mohammadtaghi Avand, Abolfazl Jaafari, Tran Van Phong, Mahmoud Bayat, Ebrahim Ahmadisharaf, Indra Prakash, Binh Thai Pham, Saro Lee. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability. 2019; 11 (19):5426.
Chicago/Turabian StyleSaeid Janizadeh; Mohammadtaghi Avand; Abolfazl Jaafari; Tran Van Phong; Mahmoud Bayat; Ebrahim Ahmadisharaf; Indra Prakash; Binh Thai Pham; Saro Lee. 2019. "Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran." Sustainability 11, no. 19: 5426.
Landslide is a natural hazard which causes huge loss of properties and human life in many places of the world. Mapping of landslide susceptibility is an important task for preventing and combating the landslides problems. Main objective of this study is to use different artificial intelligence methods namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), and reduced error-pruning tree (REPT) in the development of models for landslide susceptibility mapping of Muong Lay district of Vietnam. In total data of 217 landslide locations of the study area was used for the development and evaluation of the models. Nine landslide-conditioning factors were used for generating the datasets for training and validating the models. Results show that the SVM outperformed all other methods namely ANN, LR and REPT. Thus, it can be suggested that the SVM method is more useful in developing accurate and robust landslide prediction model.
Tran Van Phong; Trong Trinh Phan; Indra Prakash; Sushant K. Singh; Ataolla Shirzadi; Kamran Chapi; Hai-Bang Ly; Lanh Si Ho; Nguyen Kim Quoc; Binh Thai Pham. Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam. Geocarto International 2019, 36, 1685 -1708.
AMA StyleTran Van Phong, Trong Trinh Phan, Indra Prakash, Sushant K. Singh, Ataolla Shirzadi, Kamran Chapi, Hai-Bang Ly, Lanh Si Ho, Nguyen Kim Quoc, Binh Thai Pham. Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam. Geocarto International. 2019; 36 (15):1685-1708.
Chicago/Turabian StyleTran Van Phong; Trong Trinh Phan; Indra Prakash; Sushant K. Singh; Ataolla Shirzadi; Kamran Chapi; Hai-Bang Ly; Lanh Si Ho; Nguyen Kim Quoc; Binh Thai Pham. 2019. "Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam." Geocarto International 36, no. 15: 1685-1708.
We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas.
Phong Tung Nguyen; Tran Thi Tuyen; Ataollah Shirzadi; Binh Thai Pham; Himan Shahabi; Ebrahim Omidvar; Ata Amini; Hersh Entezami; Indra Prakash; Tran Van Phong; Ba Thao Vu; Tran Thanh; Lee Saro; Dieu Tien Bui. Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction. Applied Sciences 2019, 9, 2824 .
AMA StylePhong Tung Nguyen, Tran Thi Tuyen, Ataollah Shirzadi, Binh Thai Pham, Himan Shahabi, Ebrahim Omidvar, Ata Amini, Hersh Entezami, Indra Prakash, Tran Van Phong, Ba Thao Vu, Tran Thanh, Lee Saro, Dieu Tien Bui. Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction. Applied Sciences. 2019; 9 (14):2824.
Chicago/Turabian StylePhong Tung Nguyen; Tran Thi Tuyen; Ataollah Shirzadi; Binh Thai Pham; Himan Shahabi; Ebrahim Omidvar; Ata Amini; Hersh Entezami; Indra Prakash; Tran Van Phong; Ba Thao Vu; Tran Thanh; Lee Saro; Dieu Tien Bui. 2019. "Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction." Applied Sciences 9, no. 14: 2824.