This page has only limited features, please log in for full access.
Foamed concrete (FC) shows advantageous applications in civil engineering, such as reduction in dead loads, contribution to energy conservation, or decrease the construction phase labor cost. Compressive Strength is considered the most important factor in terms of FC mechanical properties. In recent years, Artificial Neural Network (ANN) is one of popular and effective machine learning models, which can be used to accurately predict the FCCS. However, ANN’s structure and parameters are normally chosen by experience. In this study, therefore, the objective is to use particle swarm optimization (PSO) metaheuristic optimization (one of the effective soft computing techniques) to optimize the parameters and structure of a Levenberg–Marquardt-based Artificial Neural Network (LMA-ANN) for accurate and quick prediction of the FCCS. A total of 375 data of experiments on FC gathered from the available literature were used to generate the training and testing datasets. Various validation criteria such as mean absolute error, root mean square error, and correlation coefficient (R) were used for the validation of the models. The results showed that the PSO-LMA-ANN algorithm is a highly efficient predictor of the FCCS, achieving the highest value of R up to 0.959 with the optimized [5-7-6-1] structure. An interpretation of the mixture components and the FCCS using Partial Dependence Plots was also performed to understand the effect of each input on the FCCS. The dry density was the most important parameter for the prediction of FCCS, followed by the water/cement ratio, foam volume, sand/cement ratio, and the testing age. The results of the present work might help in accurate and quick prediction of the FCCS and the design optimization process of the FC.
Hai-Bang Ly; May Huu Nguyen; Binh Thai Pham. Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Computing and Applications 2021, 1 -21.
AMA StyleHai-Bang Ly, May Huu Nguyen, Binh Thai Pham. Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Computing and Applications. 2021; ():1-21.
Chicago/Turabian StyleHai-Bang Ly; May Huu Nguyen; Binh Thai Pham. 2021. "Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength." Neural Computing and Applications , no. : 1-21.
Groundwater potential maps are important tools for the sustainable management of water resources, especially in agricultural producing countries like Vietnam. Here, we describe the development and application of a spatially explicit ensemble modeling framework that allows for analyzing spatially explicit data for estimating groundwater potential across the Kon Tum Province, Vietnam. Based on this framework, the Naïve Bayes (NB) method was integrated with the Bagging (B), AdaBoost (AB), and Rotation Forest (RF) ensemble learning techniques to develop three ensemble models, namely BNB, ABNB, and RFNB. A suite of well yield data and thirteen explanatory variables (i.e., elevation, aspect, slope, curvature, river density, topographic wetness index, sediment transport index, soil type, geology, land use, rainfall, and flow direction and accumulation) were incorporated into the modeling processes over the independent training and validation levels of the single NB model and its three ensembles. Several performance metrics (i.e., area under the receiver operating characteristic curve (AUC), root mean square error (RMSE), accuracy, sensitivity, specificity, negative predictive value, and positive predictive value) demonstrated that the three ensemble models successfully surpassed the single NB model in groundwater potential mapping. The ensemble RFNB model with AUC = 0.849, accuracy = 83.33%, sensitivity = 100%, specificity = 75%, and RMSE = 0.406 exhibited the most accurate performance for mapping groundwater potential in the Kon Tum Province, followed by the ABNB (AUC = 0.844), BNB (AUC = 0.815), and single NB (AUC = 0.786) models, respectively. Further, the correlation based feature selection method identified elevation, slope, land use, rainfall, and STI as the most useful explanatory variables for explaining the distribution of groundwater potential in the Kon Tum Province. The methodology proposed in this case study and the produced potential maps enable managers to align water use patterns with the shared benefits and costs of different users and to develop strategies for sustainable groundwater exploitation, preservation, and management.
Binh Thai Pham; Abolfazl Jaafari; Tran Van Phong; Davood Mafi-Gholami; Mahdis Amiri; Nguyen Van Tao; Van-Hao Duong; Indra Prakash. Naïve Bayes ensemble models for groundwater potential mapping. Ecological Informatics 2021, 101389 .
AMA StyleBinh Thai Pham, Abolfazl Jaafari, Tran Van Phong, Davood Mafi-Gholami, Mahdis Amiri, Nguyen Van Tao, Van-Hao Duong, Indra Prakash. Naïve Bayes ensemble models for groundwater potential mapping. Ecological Informatics. 2021; ():101389.
Chicago/Turabian StyleBinh Thai Pham; Abolfazl Jaafari; Tran Van Phong; Davood Mafi-Gholami; Mahdis Amiri; Nguyen Van Tao; Van-Hao Duong; Indra Prakash. 2021. "Naïve Bayes ensemble models for groundwater potential mapping." Ecological Informatics , no. : 101389.
Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.
Trinh Quoc Ngo; Nguyen Duc Dam; Nadhir Al-Ansari; Mahdis Amiri; Tran Van Phong; Indra Prakash; Hiep Van Le; Hanh Bich Thi Nguyen; Binh Thai Pham. Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India. Advances in Civil Engineering 2021, 2021, 1 -19.
AMA StyleTrinh Quoc Ngo, Nguyen Duc Dam, Nadhir Al-Ansari, Mahdis Amiri, Tran Van Phong, Indra Prakash, Hiep Van Le, Hanh Bich Thi Nguyen, Binh Thai Pham. Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India. Advances in Civil Engineering. 2021; 2021 ():1-19.
Chicago/Turabian StyleTrinh Quoc Ngo; Nguyen Duc Dam; Nadhir Al-Ansari; Mahdis Amiri; Tran Van Phong; Indra Prakash; Hiep Van Le; Hanh Bich Thi Nguyen; Binh Thai Pham. 2021. "Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India." Advances in Civil Engineering 2021, no. : 1-19.
Romulus Costache; Alireza Arabameri; Hossein Moayedi; Quoc Bao Pham; M. Santosh; Hoang Nguyen; Manish Pandey; Binh Thai Pham. Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree. Geocarto International 2021, 1 -28.
AMA StyleRomulus Costache, Alireza Arabameri, Hossein Moayedi, Quoc Bao Pham, M. Santosh, Hoang Nguyen, Manish Pandey, Binh Thai Pham. Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree. Geocarto International. 2021; ():1-28.
Chicago/Turabian StyleRomulus Costache; Alireza Arabameri; Hossein Moayedi; Quoc Bao Pham; M. Santosh; Hoang Nguyen; Manish Pandey; Binh Thai Pham. 2021. "Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree." Geocarto International , no. : 1-28.
Flash flood is one of the most common natural hazards affecting many mountainous areas. Previous studies explored flash flood susceptibility models; however, there is still a lack of case studies in the transport sector. This paper aimed to develop advanced hybrid machine learning (ML) algorithms for flash flood susceptibility modeling and mapping using data from the road network National Highway 6 in Hoa Binh province, Vietnam. A single ML model of reduced error pruning trees (REPT) and four hybrid ML models of Decorate-REPT, AdaBoostM1-REPT, Bagging-REPT, and MultiBoostAB-REPT were applied to develop flash flood susceptibility maps. Field surveys were conducted about the flash flood locations on the 115-km route length of the National Highway 6 in 2017, 2018, and 2019 flood events. This study used 88 flash flood locations and 14 flood conditioning factors to construct and validate the proposed models. Statistical metrics, including sensitivity, specificity, accuracy, root mean square error, and area under the receiver operating characteristic curve, were applied to evaluate the models’ performance and accuracy. The DCREPT model showed the best performance (AUC = 0.988) among the training models and had the highest prediction accuracy (AUC = 0.991) among the testing models. We found that 12,572 ha (Decorate-REPT), 9564 ha (AdaBoostM1-REPT), 11,954 ha (Bagging-REPT), 14,432 ha (MultiBoostAB-REPT), and 17,660 ha (REPT) of the 3-km buffer area of the highway are in the high- and very high-flash-flood-susceptibility areas. The proposed methodology could be potentially generalized to other transportation routes in mountainous areas to generate flash flood susceptibility prediction maps.
Hang Ha; Chinh Luu; Quynh Duy Bui; Duy-Hoa Pham; Tung Hoang; Viet-Phuong Nguyen; Minh Tuan Vu; Binh Thai Pham. Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models. Natural Hazards 2021, 1 -24.
AMA StyleHang Ha, Chinh Luu, Quynh Duy Bui, Duy-Hoa Pham, Tung Hoang, Viet-Phuong Nguyen, Minh Tuan Vu, Binh Thai Pham. Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models. Natural Hazards. 2021; ():1-24.
Chicago/Turabian StyleHang Ha; Chinh Luu; Quynh Duy Bui; Duy-Hoa Pham; Tung Hoang; Viet-Phuong Nguyen; Minh Tuan Vu; Binh Thai Pham. 2021. "Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models." Natural Hazards , no. : 1-24.
Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In particular, Quang Binh province is often affected by floods and storms over the year. However, it still lacks studies on flood hazard estimation and prediction tools in this area. This study aims to develop a flooding susceptibility assessment tool using various machine learning (ML) techniques namely alternating decision tree (AD Tree), logistic model tree (LM Tree), reduced-error pruning tree (REP Tree), J48 decision tree (J48) and Naïve Bayes tree (NB Tree); historical flood marks; and available data of topography, hydrology, geology, and environment considering Quang Binh province as a study area. We used flood mark locations of major flooding events in the years 2007, 2010, and 2016; and ten flood conditioning factors to construct and validate the ML models. Various validation methods, including area under the ROC curve (AUC), were used to validate and compare the models. The result of the models’ validation suggests that all models have good performance: AD Tree (AUC = 0.968), LM Tree (AUC = 0.967), REP Tree (AUC = 0.897), J48 (AUC = 0.953), and NB Tree (AUC = 0.986). Out of these, NB Tree managed to achieve the best performance in terms of flood prediction with an accuracy higher than 92 %. The final flood susceptibility map highlights 6,265 km2 (78.8 % area) with a very low flooding hazard, 391 km2 (4.9 % area) with a low flooding hazard, 224 km2 (2.8 % area) with a moderate flooding hazard, 243 km2 (3.1 %) with a high flooding hazard, and 829 km2 (10.4 % area) with very high flooding hazard. The final flooding susceptibility assessment map could add a valuable source for flood risk reduction and management activities of Quang Binh province.
Chinh Luu; Quynh Duy Bui; Romulus Costache; Luan Thanh Nguyen; Thu Thuy Nguyen; Tran Van Phong; Hiep Van Le; Binh Thai Pham. Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam. Natural Hazards 2021, 108, 3229 -3251.
AMA StyleChinh Luu, Quynh Duy Bui, Romulus Costache, Luan Thanh Nguyen, Thu Thuy Nguyen, Tran Van Phong, Hiep Van Le, Binh Thai Pham. Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam. Natural Hazards. 2021; 108 (3):3229-3251.
Chicago/Turabian StyleChinh Luu; Quynh Duy Bui; Romulus Costache; Luan Thanh Nguyen; Thu Thuy Nguyen; Tran Van Phong; Hiep Van Le; Binh Thai Pham. 2021. "Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam." Natural Hazards 108, no. 3: 3229-3251.
Recently, floods are occurring more frequently every year around the world due to increased anthropogenic activities and climate change. There is a need to develop accurate models for flood susceptibility prediction and mapping, which can be helpful in developing more efficient flood management plans. In this study, the Partial Decision Tree (PART) classifier and the AdaBoost, Bagging, Dagging, and Random Subspace ensembles learning techniques were combined to develop novel GIS-based ensemble computational models (ABPART, BPART, DPART and RSSPART) for flood susceptibility mapping in the Quang Binh Province, Vietnam. In total, 351 flood locations were used in the model study. This data was divided into a 70:30 ratio for model training (70% ≅ 255 locations) and (30% ≅ 96 locations) for model validation. Ten flood influencing factors, namely elevation, slope, curvature, flow direction, flow accumulation, river density, distance from river, rainfall, land-use, and geology, were used for the development of models. The OneR feature selection method was used to select and prioritize important factors for the spatial modeling. The results revealed that land-use, geology, and slope are the most important conditioning factors in the occurrence of floods in the study area. Standard statistical methods, including the ROC curve (AUC), were used for the performance evaluation of models. Results indicated that the performance of all models was good (AUC > 0.9) and RSSPART (AUC = 0.959) outperformed the others. Thus, the RSSPART model can be used for accurately predicting and mapping flood susceptibility.
Chinh Luu; Binh Thai Pham; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Quynh Duy Bui; Luan Thanh Nguyen; Hiep Van Le; Indra Prakash; Phan Trong Trinh. GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam. Journal of Hydrology 2021, 599, 126500 .
AMA StyleChinh Luu, Binh Thai Pham, Tran Van Phong, Romulus Costache, Huu Duy Nguyen, Mahdis Amiri, Quynh Duy Bui, Luan Thanh Nguyen, Hiep Van Le, Indra Prakash, Phan Trong Trinh. GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam. Journal of Hydrology. 2021; 599 ():126500.
Chicago/Turabian StyleChinh Luu; Binh Thai Pham; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Quynh Duy Bui; Luan Thanh Nguyen; Hiep Van Le; Indra Prakash; Phan Trong Trinh. 2021. "GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam." Journal of Hydrology 599, no. : 126500.
This research provides an innovative combination of an adaptive neuro-fuzzy inference system (ANFIS) model for solving a nonlinear and complex problem related to soil shear strength prediction. The new hybrid model is optimized by an optimization technique i.e., Henry gas solubility optimization (HGSO), called as HGSO-ANFIS. In predicting soil shear strength, the results of liquid limit, specific gravity, clay content, moisture content, void ratio, and plastic limit were considered and used as the model predictors. The HGSO-ANFIS model is implemented based on Henry’s law and can be used in engineering issues. The HGSO algorithm is developed based on the huddling behavior of gas to find the main answers and to avoid being trapped in the local minima. The search space in this model can be presented with a better performance than the base model. The performance of the new hybrid HGSO-ANFIS model was tested with real data to compare the other ANFIS-based models. The performance of the best HGSO-ANFIS model for the testing data was 0.954 and 0.1891 for coefficient of determination (R2) and root mean square error (RMSE), respectively. The model results showed that the new hybrid HGSO-ANFIS model can get higher level of accuracy compared to the other ANFIS-based models and it can be applied for various prediction and optimization problems.
Wangfei Ding; Manh Duc Nguyen; Ahmed Salih Mohammed; Danial Jahed Armaghani; Mahdi Hasanipanah; Loi Van Bui; Binh Thai Pham. A new development of ANFIS-Based Henry gas solubility optimization technique for prediction of soil shear strength. Transportation Geotechnics 2021, 29, 100579 .
AMA StyleWangfei Ding, Manh Duc Nguyen, Ahmed Salih Mohammed, Danial Jahed Armaghani, Mahdi Hasanipanah, Loi Van Bui, Binh Thai Pham. A new development of ANFIS-Based Henry gas solubility optimization technique for prediction of soil shear strength. Transportation Geotechnics. 2021; 29 ():100579.
Chicago/Turabian StyleWangfei Ding; Manh Duc Nguyen; Ahmed Salih Mohammed; Danial Jahed Armaghani; Mahdi Hasanipanah; Loi Van Bui; Binh Thai Pham. 2021. "A new development of ANFIS-Based Henry gas solubility optimization technique for prediction of soil shear strength." Transportation Geotechnics 29, no. : 100579.
This study propose a new approach through which the landslide susceptibility in Quang Nam (Vietnam) will be estimated using the best model among the following algorithms: Decision Table (DT), Naïve Bayes (NB), Decision Table - Naïve Bayes (DTNB), Bagging Ensemble, Cascade Generalization Ensemble, Dagging Ensemble, Decorate Ensemble, MultiBoost Ensemble, MultiScheme Ensemble, Real Ada Boost Ensemble, Rotation Forest Ensemble, Random Sub Space Ensemble. In this regard, a map with 1130 landslide, was created and further partitioned into training (70%) and testing (30%) locations. The correlation-based features selections (CFS) method was used to select a number of 15 landslide influencing factors. Landslide locations, included in the training sample, and the landslide predictors were used as input data in order to run the above mentioned models. Kappa index, Accuracy (%) and ROC curve were employed to estimate the model’s performance and to test the outcomes provided by the models. Among the eleven machine learning algorithms, Random Sub Space Decision Table Naïve Bayes (RSSDTNB) was the most performant model with an AUC =0.839, Accuracy =76.55% and Kappa Index =0.531. Therefore, this algorithm was involved in the estimation of landslide susceptibility. The Success Rate (AUC =0.815) and Prediction Rate (AUC =0.826) revealed the achievement of high-quality results.
Binh Thai Pham; Vinh Duy Vu; Romulus; Romulus Costache; Tran Van Phong; Trinh Quoc Ngo; Trung-Hieu Tran; Huu Duy Nguyen; Mahdis Amiri; Mai Thanh Tan; Phan Trong Trinh; Hiep Van Le; Indra Prakash. Landslide susceptibility mapping using state-of-the-art machine learning ensembles. Geocarto International 2021, 1 -25.
AMA StyleBinh Thai Pham, Vinh Duy Vu, Romulus, Romulus Costache, Tran Van Phong, Trinh Quoc Ngo, Trung-Hieu Tran, Huu Duy Nguyen, Mahdis Amiri, Mai Thanh Tan, Phan Trong Trinh, Hiep Van Le, Indra Prakash. Landslide susceptibility mapping using state-of-the-art machine learning ensembles. Geocarto International. 2021; ():1-25.
Chicago/Turabian StyleBinh Thai Pham; Vinh Duy Vu; Romulus; Romulus Costache; Tran Van Phong; Trinh Quoc Ngo; Trung-Hieu Tran; Huu Duy Nguyen; Mahdis Amiri; Mai Thanh Tan; Phan Trong Trinh; Hiep Van Le; Indra Prakash. 2021. "Landslide susceptibility mapping using state-of-the-art machine learning ensembles." Geocarto International , no. : 1-25.
Landslides are considered to be a significant risk to life and property all over the world in general and in Vietnam in particular. Spatial prediction of landslides is required to reduce the landslides risk and to plan the development of hilly areas. In this regard, the accurate landslide susceptibility maps are very useful tool for decision-makers to identify areas where new landslides are likely to occur for planning timely adequate remedial measures. For the development of landslide susceptibility maps, seven hybrid models were developed namely AdaBoost-LMT (ABLMT), Bagging-LMT (BLMT), Cascade Generalization-LMT (CGLMT), Dagging-LMT (DLMT), MultiBoostAB-LMT (MBLMT), Rotation Forest-LMT (RFLMT) and Random Sub Space-LMT (RSSLMT) with Logistic Model Trees (LMT) as a base classifier. The models performance and validation was assessed thourgh various statistical indices such as sensitivity, specificity, accuracy, Area Under ROC Curve, RMSE and k index. The results show that all these models are performing well for the prediction of landslide susceptibility in the study area, but the performance of the RSSLMT model is the best (Area Under the ROC Curve (AUC): 0.816). In this study open source data has been used for the development of landslide susceptibility maps Along National Highway-6, passing through Hoa Binh province, Vietnam. These approaches can be applied also in other hilly regions of the world which are susceptible to landslides for better landslides prevention and management.
Ha Thi Hang; Hoang Tung; Pham Duy Hoa; Nguyen Viet Phuong; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Hoang-Anh Le; Hiep Van Le; Indra Prakash; Binh Thai Pham. Spatial prediction of landslides along National Highway-6, Hoa Binh province, Vietnam using novel hybrid models. Geocarto International 2021, 1 -26.
AMA StyleHa Thi Hang, Hoang Tung, Pham Duy Hoa, Nguyen Viet Phuong, Tran Van Phong, Romulus Costache, Huu Duy Nguyen, Mahdis Amiri, Hoang-Anh Le, Hiep Van Le, Indra Prakash, Binh Thai Pham. Spatial prediction of landslides along National Highway-6, Hoa Binh province, Vietnam using novel hybrid models. Geocarto International. 2021; ():1-26.
Chicago/Turabian StyleHa Thi Hang; Hoang Tung; Pham Duy Hoa; Nguyen Viet Phuong; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Hoang-Anh Le; Hiep Van Le; Indra Prakash; Binh Thai Pham. 2021. "Spatial prediction of landslides along National Highway-6, Hoa Binh province, Vietnam using novel hybrid models." Geocarto International , no. : 1-26.
Fire is among the most dangerous and devastating natural hazards in forest ecosystems around the world. The development of computational ensemble models for improving the predictive accuracy of forest fire susceptibilities could save time and cost in firefighting efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the prediction of forest fire susceptibility in the Pu Mat National Park, Nghe An Province, Vietnam. A geospatial database that contained records from 56 historical fires and nine explanatory variables was employed to train the standalone LWL model and its derived ensemble models. The models were validated for their goodness-of-fit and predictive capability using the area under the receiver operating characteristic curve (AUC) and several other statistical performance criteria. The CG-LWL and Bagging-LWL models with AUC = 0.993 showed the highest training performance, whereas the Dagging-LWL ensemble model with AUC = 0.983 performed better than Decorate-LWL (AUC = 0.976), CG-LWL and Bagging-LWL (AUC = 0.972), and LWL (AUC = 0.965) for predicting the spatial pattern of fire susceptibilities across the study area. Our study promotes the application of ensemble models in forest fire prediction and enhances the researchers' understanding of the processes of model building. Although these four ensemble models were originally developed for the estimation of forest fire susceptibility, the models are sufficiently general to be used for predicting other types of natural hazards, such as landslides, floods, and dust storms, by considering local geo-environmental factors.
Tran Thi Tuyen; Abolfazl Jaafari; Hoang Phan Hai Yen; Trung Nguyen-Thoi; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Tran Thi Mai Phuong; Son Hoang Nguyen; Indra Prakash; Binh Thai Pham. Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm. Ecological Informatics 2021, 63, 101292 .
AMA StyleTran Thi Tuyen, Abolfazl Jaafari, Hoang Phan Hai Yen, Trung Nguyen-Thoi, Tran Van Phong, Huu Duy Nguyen, Hiep Van Le, Tran Thi Mai Phuong, Son Hoang Nguyen, Indra Prakash, Binh Thai Pham. Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm. Ecological Informatics. 2021; 63 ():101292.
Chicago/Turabian StyleTran Thi Tuyen; Abolfazl Jaafari; Hoang Phan Hai Yen; Trung Nguyen-Thoi; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Tran Thi Mai Phuong; Son Hoang Nguyen; Indra Prakash; Binh Thai Pham. 2021. "Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm." Ecological Informatics 63, no. : 101292.
Understanding the radon dispersion released from this mine are important targets as radon dispersion is used to assess radiological hazard to human. In this paper, the main objective is to develop and optimize a machine learning model namely Artificial Neural Network (ANN) for quick and accurate prediction of radon dispersion released from Sinquyen mine, Vietnam. For this purpose, a total of million data collected from the study area, which includes input variables (the gamma data of uranium concentration with 3x3m grid net survey inside mine, 21 of CR-39 detectors inside dwellings surrounding mine, and gamma dose at 1 m from ground surface data) and an output variable (radon dispersion) were used for training and validating the predictive model. Various validation methods namely coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) were used. In addition, Partial dependence plots (PDP) was used to evaluate the effect of each input variable on the predictive results of output variable. The results show that ANN performed well for prediction of radon dispersion, with low values of error (i.e., R2=0.9415, RMSE=0.0589, and MAE=0.0203 for the testing dataset). The increase of number of hidden layers in ANN structure leads the increase of accuracy of the predictive results. The sensitivity results show that all input variables govern the dispersion radon activity with different amplitudes and fitted with different equations but the gamma dose is the most influenced and important variable in comparison with strike, distance and uranium concentration variables for prediction of radon dispersion.
Hao Duong Van; Hai-Bang Ly; Trinh Dinh Huan; Son Nguyen Thai; Binh Thai Pham. Development of Artificial Neural Network for Prediction of Radon Dispersion Released from Sinquyen Mine, Vietnam. Environmental Pollution 2021, 282, 116973 .
AMA StyleHao Duong Van, Hai-Bang Ly, Trinh Dinh Huan, Son Nguyen Thai, Binh Thai Pham. Development of Artificial Neural Network for Prediction of Radon Dispersion Released from Sinquyen Mine, Vietnam. Environmental Pollution. 2021; 282 ():116973.
Chicago/Turabian StyleHao Duong Van; Hai-Bang Ly; Trinh Dinh Huan; Son Nguyen Thai; Binh Thai Pham. 2021. "Development of Artificial Neural Network for Prediction of Radon Dispersion Released from Sinquyen Mine, Vietnam." Environmental Pollution 282, no. : 116973.
Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.
Hai-Bang Ly; Thuy-Anh Nguyen; Binh Thai Pham. Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach. Advances in Civil Engineering 2021, 2021, 1 -14.
AMA StyleHai-Bang Ly, Thuy-Anh Nguyen, Binh Thai Pham. Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach. Advances in Civil Engineering. 2021; 2021 ():1-14.
Chicago/Turabian StyleHai-Bang Ly; Thuy-Anh Nguyen; Binh Thai Pham. 2021. "Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach." Advances in Civil Engineering 2021, no. : 1-14.
The cemented paste backfill (CPB) technology has matured as a promising way for tailings recycling in the mining industry. Nevertheless, the current CPB design requires a large number of lab experiments to determine the unconfined compressive strength (UCS) of CPB. The utilisation of artificial intelligence (AI) prediction to reduce the lab experiments has been attempted without reaching its full potential. In this study, a hybrid model based on adaptive neuro fuzzy inference system (ANFIS) and artificial bee colony (ABC) was used for performance improvement. The ANFIS was used to learn the relationship between inputs and UCS while the ABC algorithm was used to tune the parameters of the initial ANFIS. The convergence of the prediction performance was tested using Monte Carlo simulations. A comparison between this study and previous studies was conducted and a sensitivity analysis was performed to investigate the importance of input variables. The results show that the ABC algorithm was efficient in tunning parameters of the ANFIS model. The representative ANFIS-ABC model yielded an R2 of 0.967 on the training part and an R2 of 0.976 on the testing part, indicating an excellent prediction. 310 Monte Carlo simulations were needed before a stable performance was achieved for all quality assessment criteria. The proposed hybrid model outperformed AI models in the literature (R2 was increased from 0.83/0.958/0.86 to 0.976 on the testing set). Solid content, cement-tailings ratio and curing time were found to be the most significant input parameters for the UCS of CPB.
Chongchong Qi; Hai-Bang Ly; Lu Minh Le; Xingyu Yang; Li Guo; Binh Thai Pham. Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony. Construction and Building Materials 2021, 284, 122857 .
AMA StyleChongchong Qi, Hai-Bang Ly, Lu Minh Le, Xingyu Yang, Li Guo, Binh Thai Pham. Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony. Construction and Building Materials. 2021; 284 ():122857.
Chicago/Turabian StyleChongchong Qi; Hai-Bang Ly; Lu Minh Le; Xingyu Yang; Li Guo; Binh Thai Pham. 2021. "Improved strength prediction of cemented paste backfill using a novel model based on adaptive neuro fuzzy inference system and artificial bee colony." Construction and Building Materials 284, no. : 122857.
In this paper, we proposed a novel approach for flood risk assessment, which is a combination of a deep learning algorithm and Multi-Criteria Decision Analysis (MCDA). The framework of the flood risk assessment involves three main elements: hazard, exposure, and vulnerability. For this purpose, one of the flood-prone areas of Vietnam, namely Quang Nam province was selected as the study area. Data of 847 past flood locations of this area was analyzed to generate training and testing datasets for the models. In this study, we have used one of the popular Deep Neural Networks (DNNs) algorithm for generation of flood susceptibility map while Analytic Hierarchy Process (AHP), which is a popular MCDA approach, was used to generate the hazard, exposure, and vulnerability maps. We have also used hybrid models namely BFPA and DFPA which are the ensembles of Bagging and Decorate with Forest by Penalizing Attributes algorithm for the comparison of performance with DNNs method. Various standard statistical indices including Receiver Operating Characteristic (ROC) curves were used for the performance evaluation and validation of the models. Results indicated that integration of DNNs and MCDA models is a promising approach for developing accurate flood risk assessment map of an area for the better flood hazard management.
Binh Thai Pham; Chinh Luu; Dong Van Dao; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Jason von Meding; Indra Prakash. Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems 2021, 219, 106899 .
AMA StyleBinh Thai Pham, Chinh Luu, Dong Van Dao, Tran Van Phong, Huu Duy Nguyen, Hiep Van Le, Jason von Meding, Indra Prakash. Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems. 2021; 219 ():106899.
Chicago/Turabian StyleBinh Thai Pham; Chinh Luu; Dong Van Dao; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Jason von Meding; Indra Prakash. 2021. "Flood risk assessment using deep learning integrated with multi-criteria decision analysis." Knowledge-Based Systems 219, no. : 106899.
The Sin Quyen iron oxide–copper–gold (IOCG) deposit is located in the southwestern part of the Sin Quyen Formation, close to the Red River shear zone. The deposit is controlled by the Sin Quyen fault, parallel to the Red River fault system in North Vietnam. U-Pb dating of zircon and uraninite and 39Ar/40Ar dating of biotite and K-feldspar in the Sin Quyen deposit showed that the mineralization was emplaced in two main phases: the first phase formed magnetite, uraninite, and allanite of Precambrian age, between 520 and 744 Ma; the second most important phase introduced Cu-sulfides with Au, this event formed chalcopyrite, pyrrhotite, and pyrite in the temperature range 320 ± 40 °C between 88 and 22 Ma. In this study, a tectonic model was presented to explain when and how the Sin Quyen IOCG deposit and some other Cu-Au deposits along the Ailao Shan Red River Shear zone occurred in Cenozoic time.
Van-Hao Duong; Phan Trong Trinh; Thanh-Duong Nguyen; Adam Piestrzyski; Dinh Chau Nguyen; Jadwiga Pieczonka; Xuan Dac Ngo; Phong Tran Van; Binh Thai Pham; Huong Nguyen-Van; Liem Ngo Van; Dieu Tien Bui; Dang Vu Khac; Chi Tien Bui. Cu-Au mineralization of the Sin Quyen deposit in north Vietnam: A product of Cenozoic left-lateral movement along the Red River shear zone. Ore Geology Reviews 2021, 132, 104065 .
AMA StyleVan-Hao Duong, Phan Trong Trinh, Thanh-Duong Nguyen, Adam Piestrzyski, Dinh Chau Nguyen, Jadwiga Pieczonka, Xuan Dac Ngo, Phong Tran Van, Binh Thai Pham, Huong Nguyen-Van, Liem Ngo Van, Dieu Tien Bui, Dang Vu Khac, Chi Tien Bui. Cu-Au mineralization of the Sin Quyen deposit in north Vietnam: A product of Cenozoic left-lateral movement along the Red River shear zone. Ore Geology Reviews. 2021; 132 ():104065.
Chicago/Turabian StyleVan-Hao Duong; Phan Trong Trinh; Thanh-Duong Nguyen; Adam Piestrzyski; Dinh Chau Nguyen; Jadwiga Pieczonka; Xuan Dac Ngo; Phong Tran Van; Binh Thai Pham; Huong Nguyen-Van; Liem Ngo Van; Dieu Tien Bui; Dang Vu Khac; Chi Tien Bui. 2021. "Cu-Au mineralization of the Sin Quyen deposit in north Vietnam: A product of Cenozoic left-lateral movement along the Red River shear zone." Ore Geology Reviews 132, no. : 104065.
The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.
Quang Hung Nguyen; Hai-Bang Ly; Lanh Si Ho; Nadhir Al-Ansari; Hiep Van Le; Van Quan Tran; Indra Prakash; Binh Thai Pham. Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil. Mathematical Problems in Engineering 2021, 2021, 1 -15.
AMA StyleQuang Hung Nguyen, Hai-Bang Ly, Lanh Si Ho, Nadhir Al-Ansari, Hiep Van Le, Van Quan Tran, Indra Prakash, Binh Thai Pham. Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil. Mathematical Problems in Engineering. 2021; 2021 ():1-15.
Chicago/Turabian StyleQuang Hung Nguyen; Hai-Bang Ly; Lanh Si Ho; Nadhir Al-Ansari; Hiep Van Le; Van Quan Tran; Indra Prakash; Binh Thai Pham. 2021. "Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil." Mathematical Problems in Engineering 2021, no. : 1-15.
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.
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 StyleChongchong 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 StyleChongchong 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.
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.
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 StyleMahdi 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 StyleMahdi 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.
There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.
Romulus Costache; Alireza Arabameri; Thomas Blaschke; Quoc Pham; Binh Pham; Manish Pandey; Aman Arora; Nguyen Linh; Iulia Costache. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. Sensors 2021, 21, 280 .
AMA StyleRomulus Costache, Alireza Arabameri, Thomas Blaschke, Quoc Pham, Binh Pham, Manish Pandey, Aman Arora, Nguyen Linh, Iulia Costache. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. Sensors. 2021; 21 (1):280.
Chicago/Turabian StyleRomulus Costache; Alireza Arabameri; Thomas Blaschke; Quoc Pham; Binh Pham; Manish Pandey; Aman Arora; Nguyen Linh; Iulia Costache. 2021. "Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors." Sensors 21, no. 1: 280.