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Hiep Van Le
Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam

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Journal article
Published: 22 January 2021 in Minerals Engineering
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Estimation of pressure drops of fresh cemented paste backfill slurry is a novel idea with great potentials. This paper presented a hybrid machine learning (ML) method for improved pressure drops estimation using a combination of artificial neural network and differential evolution. A comprehensive parametric study was conducted on training dataset size (Nsize), ML methods, and Monte Carlo random sampling. Moreover, dependent analysis of pressure drops to each influencing variable was performed. The results indicate that 300 Monte Carlo realizations were sufficient for the converged and reliable results. The optimum Nsize was determined to be 70%, and the proposed hybrid method outperformed six individual ML methods. The estimation performance has been significantly improved compared to the methods used in the literature (R2 increased from 0.83 to 0.95 on the testing dataset). Solids content, inlet velocity, SiO2, CaO, and Fe2O3 were determined to be the most significant variables for pressure drops.

ACS Style

Chongchong Qi; Li Guo; Hai-Bang Ly; Hiep Van Le; Binh Thai Pham. Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method. Minerals Engineering 2021, 163, 106790 .

AMA Style

Chongchong Qi, Li Guo, Hai-Bang Ly, Hiep Van Le, Binh Thai Pham. Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method. Minerals Engineering. 2021; 163 ():106790.

Chicago/Turabian Style

Chongchong Qi; Li Guo; Hai-Bang Ly; Hiep Van Le; Binh Thai Pham. 2021. "Improving pressure drops estimation of fresh cemented paste backfill slurry using a hybrid machine learning method." Minerals Engineering 163, no. : 106790.

Journal article
Published: 07 January 2021 in CATENA
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Flood spatial susceptibility prediction is the first essential step in developing flood mitigation strategies and reducing flood damage. Flood occurrence is a complex process that is not easily predicted through simple methods. This study describes optimization of support vector regression (SVR) using meta-optimization algorithms including the grasshopper optimization algorithm (GOA) and particle swarm optimization (PSO) for flood modeling at Qazvin Plain, Iran. Geospatial data including nine readily available geo-environmental flood conditioning factors (i.e., ground slope, aspect, elevation, planform curvature, profile curvature, proximity to a river, land use, lithology and rainfall) were derived. The information gain ratio (IGR) method was used to determine the relative importance of input variables. A historical flood inventory map for 43 locations was created from existing reports. The geospatial data and historical flood levels were used to construct the training and testing datasets. Then, the training dataset was used to generate flood-susceptibility maps using the optimized SVR model with the GOA and PSO algorithms. Finally, the predictive accuracy of the models was quantified using the statistical measures of root mean square error (RMSE), mean absolute error (MAE), and area under the receiver operating characteristic (ROC) curve (AUC). Although both the GOA and PSO algorithms improved SVR performance, the SVR-GOA model performed best (AUC = 0.959, RMSE = 0.31 and MSE = 0.098), followed by the SVR-PSO model (AUC = 0.959, RMSE = 0.33 and MSE = 0.11) and standalone SVR model (AUC = 0.87, RMSE = 0.35 and MSE = 0.12). Elevation, lithology and aspect had the highest IGR values and were identified as the most effective predictors of flood susceptibility.

ACS Style

Mahdi Panahi; Esmaeel Dodangeh; Fatemeh Rezaie; Khabat Khosravi; Hiep Van Le; Moung-Jin Lee; Saro Lee; Binh Thai Pham. Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling. CATENA 2021, 199, 105114 .

AMA Style

Mahdi Panahi, Esmaeel Dodangeh, Fatemeh Rezaie, Khabat Khosravi, Hiep Van Le, Moung-Jin Lee, Saro Lee, Binh Thai Pham. Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling. CATENA. 2021; 199 ():105114.

Chicago/Turabian Style

Mahdi Panahi; Esmaeel Dodangeh; Fatemeh Rezaie; Khabat Khosravi; Hiep Van Le; Moung-Jin Lee; Saro Lee; Binh Thai Pham. 2021. "Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling." CATENA 199, no. : 105114.

Journal article
Published: 30 November 2020 in Journal of Hydrology
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Binh 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.

Journal article
Published: 28 October 2020 in Journal of Contaminant Hydrology
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Occurrence of pharmaceutical micropollutants in aquatic environments has been one amongst serious environmental problems. During this study, two reactors, including a sequencing batch reactor (SBR) + powdered composite adsorbent (CA) (first reactor, SBR + CA) and a sequencing batch reactor (second reactor, SBR), were designed to treat synthetic wastewater. Powdered CA was added with a dosage of 4.8 g L−1 to the first reactor. Tap water was contaminated with chemical oxygen demand (COD), ammonia and three pharmaceuticals, namely, atenolol (ATN), ciprofloxacin (CIP) and diazepam (DIA) to produce synthetic wastewater. The SBR + CA illustrated a better performance during synthetic municipal wastewater treatment. Up to 138.6 mg L−1 (92.4%) of COD and up to 114.2 mg L−1 (95.2%) of ammonia were removed by the first reactor. Moreover, optimisation of pharmaceuticals removal was conducted through response surface methodology (RSM) and artificial neural network (ANN). Based on the RSM, the best elimination of ATN (90.2%, 2.26 mg L−1), CIP (94.0%, 2.35 mg L−1) and DIA (95.5%, 2.39 mg L−1) was detected at the optimum initial concentration of MPs (2.51 mg L−1) and the contact time (15.8 h). In addition, ANN represented a high R2 value (>0.99) and a rational mean squared error (<1.0) during the optimisation of micropollutants removal by both reactors. Moreover, adsorption isotherm study showed that the Freundlich isotherm could justify the abatement of micropollutants by using CA better than the Langmuir isotherm.

ACS Style

Amin Mojiri; John Zhou; Mohammadtaghi Vakili; Hiep Van Le. Removal performance and optimisation of pharmaceutical micropollutants from synthetic domestic wastewater by hybrid treatment. Journal of Contaminant Hydrology 2020, 235, 103736 .

AMA Style

Amin Mojiri, John Zhou, Mohammadtaghi Vakili, Hiep Van Le. Removal performance and optimisation of pharmaceutical micropollutants from synthetic domestic wastewater by hybrid treatment. Journal of Contaminant Hydrology. 2020; 235 ():103736.

Chicago/Turabian Style

Amin Mojiri; John Zhou; Mohammadtaghi Vakili; Hiep Van Le. 2020. "Removal performance and optimisation of pharmaceutical micropollutants from synthetic domestic wastewater by hybrid treatment." Journal of Contaminant Hydrology 235, no. : 103736.

Journal article
Published: 13 October 2020 in Journal of Hydrology
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This paper introduces a new deep-learning algorithm of deep belief network (DBN) based on an extreme learning machine (ELM) that is structured by back propagation (BN) and optimized by particle swarm optimization (PSO) algorithm, named DEBP, for flood susceptibility mapping in the Vu Gia-Thu Bon watershed, central Vietnam. We use 847 locations of floods that occurred in 2007, 2009, and 2013 and 16 flood conditioning factors evaluated by an information gain ratio (IGR) technique to construct and validate the proposed model. Statistical metrics, including sensitivity, specificity, accuracy, F1-measure, Jaccard coefficient, Matthews correlation coefficient (MCC), root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), are used to assess the goodness-of-fit/performance and prediction accuracy of the new deep learning model. We further compare the proposed model with several well-known machine learning algorithms, including artificial neural network-based radial base function (ANNRBF), logistic regression (LR), logistic model tree (LMTree), functional tree (FTree), and alternating decision tree (ADTree). The new proposed model, DEBP, has the highest goodness-of-fit (AUC = 0.970) and prediction accuracy (AUC = 0.967) of all of the tested models and thus shows promise as a tool for flood susceptibility modeling. We conclude that novel deep learning algorithms such as the one used in this study can improve the accuracy of flood susceptibility maps that are required by planners, decision makers, and government agencies to manage of areas vulnerable to flood-induced damage.

ACS Style

Binh Thai Pham; Chinh Luu; Tran Van Phong; Phan Trong Trinh; Ataollah Shirzadi; Somayeh Renoud; Shahrokh Asadi; Hiep Van Le; Jason von Meding; John J. Clague. Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling? Journal of Hydrology 2020, 592, 125615 .

AMA Style

Binh Thai Pham, Chinh Luu, Tran Van Phong, Phan Trong Trinh, Ataollah Shirzadi, Somayeh Renoud, Shahrokh Asadi, Hiep Van Le, Jason von Meding, John J. Clague. Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling? Journal of Hydrology. 2020; 592 ():125615.

Chicago/Turabian Style

Binh Thai Pham; Chinh Luu; Tran Van Phong; Phan Trong Trinh; Ataollah Shirzadi; Somayeh Renoud; Shahrokh Asadi; Hiep Van Le; Jason von Meding; John J. Clague. 2020. "Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?" Journal of Hydrology 592, no. : 125615.

Journal article
Published: 25 July 2020 in CATENA
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Binh 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.

Article
Published: 30 June 2020 in Water Resources Management
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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.

ACS Style

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 Style

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 (9):3037-3053.

Chicago/Turabian Style

Peyman 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.

Journal article
Published: 17 June 2020 in Symmetry
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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.

ACS Style

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 Style

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 (6):1022.

Chicago/Turabian Style

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. 2020. "Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction." Symmetry 12, no. 6: 1022.

Journal article
Published: 27 May 2020 in Applied Sciences
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Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides.

ACS Style

Quoc Cuong Tran; Duc Do Minh; Abolfazl Jaafari; Nadhir Al-Ansari; Duc Dao Minh; Duc Tung Van; Duc Anh Nguyen; Trung Hieu Tran; Lanh Si Ho; Duy Huu Nguyen; Indra Prakash; Hiep Van Le; Binh Thai Pham. Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam. Applied Sciences 2020, 10, 3710 .

AMA Style

Quoc Cuong Tran, Duc Do Minh, Abolfazl Jaafari, Nadhir Al-Ansari, Duc Dao Minh, Duc Tung Van, Duc Anh Nguyen, Trung Hieu Tran, Lanh Si Ho, Duy Huu Nguyen, Indra Prakash, Hiep Van Le, Binh Thai Pham. Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam. Applied Sciences. 2020; 10 (11):3710.

Chicago/Turabian Style

Quoc Cuong Tran; Duc Do Minh; Abolfazl Jaafari; Nadhir Al-Ansari; Duc Dao Minh; Duc Tung Van; Duc Anh Nguyen; Trung Hieu Tran; Lanh Si Ho; Duy Huu Nguyen; Indra Prakash; Hiep Van Le; Binh Thai Pham. 2020. "Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam." Applied Sciences 10, no. 11: 3710.

Journal article
Published: 21 May 2020 in Advances in Space Research
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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.

ACS Style

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 Style

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 (6):1303-1320.

Chicago/Turabian Style

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. 2020. "GIS-based ensemble soft computing models for landslide susceptibility mapping." Advances in Space Research 66, no. 6: 1303-1320.

Journal article
Published: 10 April 2020 in Sustainability
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Vietnam has been extensively affected by floods, suffering heavy losses in human life and property. While the Vietnamese government has focused on structural measures of flood defence such as levees and early warning systems, the country still lacks flood risk assessment methodologies and frameworks at local and national levels. In response to this gap, this study developed a flood risk assessment framework that uses historical flood mark data and a high-resolution digital elevation model to create an inundation map, then combined this map with exposure and vulnerability data to develop a holistic flood risk assessment map. The case study is the October 2010 flood event in Quang Binh province, which caused 74 deaths, 210 injuries, 188,628 flooded properties, 9019 ha of submerged and damaged agricultural land, and widespread damages to canals, levees, and roads. The final flood risk map showed a total inundation area of 64,348 ha, in which 8.3% area of low risk, 16.3% area of medium risk, 12.0% area of high risk, 37.1% area of very high risk, and 26.2% area of extremely high risk. The holistic flood risk assessment map of Quang Binh province is a valuable tool and source for flood preparedness activities at the local scale.

ACS Style

Chinh Luu; Hieu Xuan Tran; Binh Thai Pham; Nadhir Al-Ansari; Thai Quoc Tran; Nga Quynh Duong; Nam Hai Dao; Lam Phuong Nguyen; Huu Duy Nguyen; Huong Thu Ta; Hiep Van Le; Jason Von Meding. Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam. Sustainability 2020, 12, 3058 .

AMA Style

Chinh Luu, Hieu Xuan Tran, Binh Thai Pham, Nadhir Al-Ansari, Thai Quoc Tran, Nga Quynh Duong, Nam Hai Dao, Lam Phuong Nguyen, Huu Duy Nguyen, Huong Thu Ta, Hiep Van Le, Jason Von Meding. Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam. Sustainability. 2020; 12 (7):3058.

Chicago/Turabian Style

Chinh Luu; Hieu Xuan Tran; Binh Thai Pham; Nadhir Al-Ansari; Thai Quoc Tran; Nga Quynh Duong; Nam Hai Dao; Lam Phuong Nguyen; Huu Duy Nguyen; Huong Thu Ta; Hiep Van Le; Jason Von Meding. 2020. "Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam." Sustainability 12, no. 7: 3058.

Journal article
Published: 04 April 2020 in International Journal of Environmental Research and Public Health
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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.

ACS Style

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 Style

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 (7):2473.

Chicago/Turabian Style

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. 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.

Journal article
Published: 12 March 2020 in Sustainability
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Determination of shear strength of soil is very important in civil engineering for foundation design, earth and rock fill dam design, highway and airfield design, stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid soft computing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) was developed and used to estimate the undrained shear strength of soil based on the clay content (%), moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). In this study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearson correlation coefficient (R) method was used to evaluate and compare performance of the proposed model with single RF model. The results show that the proposed hybrid model (RF-PSO) achieved a high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of the models also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) is superior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, the proposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which can be used for the suitable designing of civil engineering structures.

ACS Style

Binh Thai Pham; Chongchong Qi; Lanh Si Ho; Trung Nguyen-Thoi; Nadhir Al-Ansari; Manh Duc Nguyen; Huu Duy Nguyen; Hai-Bang Ly; Hiep Van Le; Indra Prakash. A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil. Sustainability 2020, 12, 2218 .

AMA Style

Binh Thai Pham, Chongchong Qi, Lanh Si Ho, Trung Nguyen-Thoi, Nadhir Al-Ansari, Manh Duc Nguyen, Huu Duy Nguyen, Hai-Bang Ly, Hiep Van Le, Indra Prakash. A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil. Sustainability. 2020; 12 (6):2218.

Chicago/Turabian Style

Binh Thai Pham; Chongchong Qi; Lanh Si Ho; Trung Nguyen-Thoi; Nadhir Al-Ansari; Manh Duc Nguyen; Huu Duy Nguyen; Hai-Bang Ly; Hiep Van Le; Indra Prakash. 2020. "A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil." Sustainability 12, no. 6: 2218.