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Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques

Geoscience Frontiers 2021年第3期12卷 333-342页

作者： Binh Thai Pham Abolfazl Jaafari Tran van Phong Hoang Phan Hai Yen Tran Thi Tuyen Vu van Luong Huu Duy Nguyen hiep van le Loke Kok Foong University of Transport Technology Ha Noi 100000Viet Nam Research Institute of Forests and Rangelands Agricultural ResearchEducation and Extension Organization(AREEO)TehranIran Institute of Geological Sciences Vietnam Academy of Sciences and Technology84 Chua Lang StreetDong daHa NoiViet Nam Department of Geography School of Social EducationVinh UniversityViet Nam Department of Resource and Environment Management School of Agriculture and ResourcesVinh UniversityViet Nam Faculty of Geography VNU University of Science334 Nguyen TraiHa NoiViet Nam Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityViet Nam

Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood *** this study,we proposed and validated three ensemble models based on the Best First Decision Tree(BFT)and the Bagging(Bagging-BFT),Decorate(Bagging-BFT),and Random Subspace(RSS-BFT)ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner.A total number of 126 historical flood events from the Nghe An Province(Vietnam)were connected to a set of 10 flood influencing factors(slope,elevation,aspect,curvature,river density,distance from rivers,flow direction,geology,soil,and land use)for generating the training and validation *** models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood *** on the Area Under the receiver operating characteristic Curve(AUC),the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT(AUC=0.982)and Bagging-BFT(AUC=0.967)models.A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.

关键词： Machine learning Ensemble learners Hybrid modeling

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Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

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Geoscience Frontiers 2021年第5期12卷 54-68页

作者： Hoang Phan Hai Yen Binh Thai Pham Tran van Phong Duong Hai Ha Romulus Costache hiep van le Huu Duy Nguyen Mahdis Amiri Nguyen van Tao Indra Prakash Department of Geography School of Social Sciences EducationVinh University182 Le DuanVinhNghe AnVietnam University of Transport Technology Hanoi 100000Viet Nam Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)84 Chua LangDong DaHanoiViet Nam Institute for Water and Environment Hanoi 100000Viet Nam Research Institute of the University of Bucharest 90-92 Sos.Panduri5th DistrictBucharestRomania National Institute of Hydrology and Water Management București-Ploiești Road97E1st District013686 BucharestRomania Faculty of Geography VNU University of ScienceVietnam National University334 Nguyen TraiHanoi 100000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan ***Iran DDG(R)Geological Survey of India Gandhinagar 382010India

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like *** this article,we proposed new machine learning ensemble techniques namely AdaBoost ensemble(ABLWL),Bagging ensemble(BLWL),Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL)with Locally Weighted learning(LWL)algorithm as a base classifier to build the groundwater potential map of Gia Lai province in *** this study,eleven conditioning factors(aspect,altitude,curvature,slope,Stream Transport Index(STI),Topographic Wetness Index(TWI),soil,geology,river density,rainfall,land-use)and 134 wells yield data was used to create training(70%)and testing(30%)datasets for the development and validation of the *** statistical indices were used namely Positive Predictive Value(PPV),Negative Predictive Value(NPV),Sensitivity(SST),Specificity(SPF),Accuracy(ACC),Kappa,and Receiver Operating Characteristics(ROC)curve to validate and compare performance of *** show that performance of all the models is good to very good(AUC:0.75 to 0.829)but the ABLWL model with AUC=0.89 is the *** the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.

关键词： Locally weighted learning Hybrid models Groundwater potential GIS Vietnam

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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

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Frontiers of Structural and Civil Engineering 2023年第5期17卷 812-826页

作者： Long Hoang NGUYEN Dung Quang VU Duc Dam NGUYEN Fazal E.JALAL Mudassir IQBAL Vinh The DANG hiep van le Indra PRAKASH Binh Thai PHAM Faculty of Civil Engineering University of Transport TechnologyHanoi 10000Vietnam Department of Civil Engineering State Key Laboratory of Ocean EngineeringShanghai Jiao Tong UniversityShanghai 200025China Department of Civil Engineering University of Engineering and TechnologyPeshawar 25120Pakistan DDG(R)Geological Survey of India Gandhinagar 382010India

A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements,such as the modulus of the subgrade reaction(Y1)and the elastic modulus of the slab(Y2),which are crucial for assessing the structural strength of *** this study,we developed a novel hybrid artificial intelligence model,i.e.,a genetic algorithm(GA)-optimized adaptive neuro-fuzzy inference system(ANFIS-GA),to predict Y1 and Y2 based on easily determined 13 parameters of rigid *** performance of the novel ANFIS-GA model was compared to that of other benchmark models,namely logistic regression(LR)and radial basis function regression(RBFR)*** models were validated using standard statistical measures,namely,the coefficient of correlation(R),mean absolute error(MAE),and root mean square error(RMSE).The results indicated that the ANFIS-GA model was the best at predicting Y1(R=0.945)and Y2(R=0.887)compared to the LR and RBFR ***,the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer modulus of subgrade reaction elastic modulus metaheuristic algorithms

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Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak

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Computers, Materials & Continua 2021年第5期67卷 1595-1612页

作者： Debabrata Dansana Raghvendra Kumar Arupa Parida Rohit Sharma Janmejoy Das Adhikari hiep van le Binh Thai Pham Krishna Kant Singh Biswajeet Pradhan Department of Computer Science and Engineering GIET UniversityGunupurOdishaIndia Department of Electronics&Communication Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyNCR CampusDelhi-NCR CampusModinagarGhaziabadIndia Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam University of Transport Technology Hanoi100000Viet Nam Department of ECE KIET Group of InstitutionsGhaziabadIndia Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) University of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversitySeoul05006Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600SelangorMalaysia

The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 *** World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,***,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely ***19 is a major threat worldwide due to its ability to rapidly *** vaccines are yet available for *** objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of *** applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the *** also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the *** study considered the cases from 22 January 2020 to 25 April 2020.

关键词： COVID-19 SEIR forecasting global pandemic predict conrmed case

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Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water levels in the Mekong Delta Region, Vietnam

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1431-1449页

作者： Nguyen Thanh Hoan Nguyen van Dung Ho le Thu Hoa Thuy Quynh Nadhir Al-Ansari Tran van Phong Phan Trong Trinh Dam Duc Nguyen hiep van le Hanh Bich Thi Nguyen Mahdis Amiri Indra Prakash Binh Thai Pham Institute of Geography Vietnam Academy of Science and TechnologyHanoi10000Viet Nam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)Dong DaHanoi10000Viet Nam University of Transport Technology Thanh XuanHa Noi10000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran DDG(R)Geological Survey of India Gandhinagar382010India

Water level predictions in the river,lake and delta play an important role in flood *** year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high *** subsidence may also aggravate flooding problems in this ***,accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and *** are so many methods available to predict the water levels based on historical data but nowadays Machine learning(ML)methods are considered the best tool for accurate *** this study,we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely:Bagging(RF),Bagging(SOM)and Bagging(M5P)to predict historical water levels in the study *** of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees(REPT),which is a benchmark ML *** data of 19 years period was divided into 70:30 ratio for the *** data of the period 1/2000 to 5/2013(which is about 70%of total data)was used for the training and for the period 5/2013 to 12/2018(which is about 30%of total data)was used for testing(validating)the *** of the models was evaluated using standard statistical measures:Coefficient of Determination(R2),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).Results show that the performance of all the developed models is good(R2>0.9)for the prediction of water levels in the study ***,the Bagging-based hybrid models are slightly better than another model such as ***,these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

关键词： Computational techniques bagging water level time series algorithms

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Ensemble machine learning models based on Reduced Error Pruning Tree for prediction of rainfall-induced landslides

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International Journal of Digital Earth 2021年第5期14卷 575-596页

作者： Binh Thai Pham Abolfazl Jaafari Trung Nguyen-Thoi Tran van Phong Huu Duy Nguyen Neelima Satyam Md Masroor Sufia Rehman Haroon Sajjad Mehebub Sahana hiep van le Indra Prakash Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityViet Nam Research Institute of Forests and Rangelands Agricultural ResearchEducationand Extension Organization(AREEO)TehranIran Institute of Geological Sciences Vietnam Academy of Sciences and TechnologyHanoiViet Nam Faculty of Geography V.N.U.University of ScienceVietnam National UniversityHanoiViet Nam Discipline of Civil Engineering Indian Institute of Technology IndoreMadhya PradheshIndia Department of Geography Jamia Millia IslamiaNew DelhiIndia School of Environment Education and DevelopmentUniversity of ManchesterManchesterUK Institute of Research and Development Duy Tan UniversityDa NangViet Nam DDG(R)Geological Survey of India GujaratIndia

In this paper,we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree(REPT)as a base classifier with the Bagging(B),Decorate(D),and Random Subspace(RSS)ensemble learning techniques for spatial prediction of rainfallinduced landslides in the Uttarkashi district,located in the Himalayan range,*** do so,a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation *** Mean Square Error(RMSE)and Area Under the receiver operating characteristic Curve(AUC)were used to evaluate the training and validation performances of the *** results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of *** D-REPT model with RMSE=0.351 and AUC=0.907 was identified as the most accurate model,followed by RSS-REPT(RMSE=0.353 and AUC=0.898),B-REPT(RMSE=0.396 and AUC=0.876),and the single REPT model(RMSE=0.398 and AUC=0.836),*** prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.

关键词： Machine learning ensemble modeling Bagging Decorate Random Subspace

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Stoichiometric Ti3C2Tx Coating for Inhibiting Dendrite Growth in Anode-Free Lithium Metal Batteries

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Energy & Environmental Materials 2024年第4期7卷 153-162页

作者： Xiangrong Zeng Manmatha Mahato Woong Oh Hyunjoon Yoo van hiep Nguyen Saewoong Oh Geetha Valurouthu Soon-Ki Jeong Chi Won Ahn Yury Gogotsi Il-Kwon Oh National Creative Research Initiative for Functionally Antagonistic Nano-Engineering Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST) Department of Materials Science & Engineering and A. J. Drexel Nanomaterials Institute Drexel University Department of Energy Engineering Soonchunhyang University National Nanofab Center (NNFC) Korea Advanced Institute of Science and Technology (KAIST)

Lithium metal batteries (LMBs) and anode-free LMBs (AFLMBs) present a solution to the need for batteries with a significantly superior theoretical energy ***,their adoption is hindered by low Coulombic efficiency (CE) and rapid capacity fading,primarily due to the formation of unstable solid electrolyte interphase (SEI) layer and Li dendrite growth as a result of uneven Li plating.

关键词： anode-free lithium metal batteries stoichiometric MXene solid electrolyte interphase surface terminations

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Applying Wide & Deep learning Model for Android Malware Classification

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Computer Systems Science & Engineering 2023年第6期45卷 2741-2759页

作者： le Duc Thuan Pham van Huong Hoang van hiep Nguyen Kim Khanh Ha Noi University of Science and Technology Ha Noi100000Viet Nam Academy of Cryptography Techniques Ha Noi100000Viet Nam

Android malware has exploded in popularity in recent years,due to the platform’s dominance of the mobile *** the advancement of deep learning technology,numerous deep learning-based works have been proposed for the classification of Android *** learning technology is designed to handle a large amount of raw and continuous data,such as image content ***,it is incompatible with discrete features,i.e.,features gathered from multiple ***,if the feature set is already well-extracted and sparsely distributed,this technology is less effective than traditional machine *** the other hand,a wide learning model can expand the feature set to enhance the classification *** maximize the benefits of both methods,this study proposes combining the components of deep learning based on multi-branch CNNs(Convolutional Network Neural)with wide learning *** feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep *** proposed model,partition,and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features:API,permission,and raw *** accuracy with Wide and Deep CNN(WDCNN)model is 98.64%,improved by 1.38%compared to RNN(Recurrent Neural Network)model.

关键词： Wide and deep(W&D)learning convolutional neural network image feature raw features generalized features

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Groundwater level Prediction Using Artificial Neural Networks: A Case Study in Tra Noc Industrial Zone, Can Tho City, Vietnam

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Journal of Water Resource and Protection 2018年第9期10卷 870-883页

作者： Tran van Ty le van Phat Huynh van hiep College of Engineering Technology Can Tho University Can Tho Vietnam Department of Natural Resources and Environment of Can Tho City Can Tho City Vietnam Tra Vinh University Tra Vinh Vietnam

The objective of this study is to predict groundwater levels (GWLs) under different impact factors using Artificial Neural Network (ANN) for a case study in Tra Noc Industrial Zone, Can Tho City, Vietnam. This can be achieved by evaluating the current state of groundwater resources (GWR) exploitation, use and dynamics;setting-up, calibrating and validating the ANN;and then predicting GWLs at different lead times. The results show that GWLs in the study area have been found to reduce rapidly from 2000 to 2015, especially in the Middle-upper Pleistocene (qp2-3) and upper Pleistocene (qp3) due to the over-withdrawals from the enterprises for production purposes. Concerning this problem, an Official letter of the People’s Committee of Can Tho City was issued and taken into enforcement in 2012 resulting in the reduction of exploitation. The calibrated ANN structures have successfully demonstrated that the GWLs can be predicted considering different impact factors. The predicted results will help to raise awareness and to draw an attention of the local/central government for a clear GWR management policy for the Mekong delta, especially the industrial zones in the urban areas such as Can Tho city.

关键词： Groundwater Resources (GWR) Groundwater levels (GWLs) Artificial Neural Network (ANN) Prediction Tra Noc Industrial Zone

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Designing Software to Analyze Sewing Process of Industrial Knitted Products

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Computer Systems Science & Engineering 2023年第2期44卷 1837-1852页

作者： Phan Thanh Thao Nguyen Thanh Hung Pham Thi le My Nguyen Xuan hiep Duy-Nam Phan Hanoi University of Science and Technology Hanoi10000Vietnam

In the textile industry,garment manufacturing contains four major pro-cesses containing cutting,sewing,ﬁnishing,and *** is the most crucial and intricate section,dealing with a large number of varied operations.A successful sewing process needs to be optimized regarding different factors,including time,sewing equipment,and skilled *** lineﬂow is combined by a set of operations with a particular *** utmost impor-tance of all garment industry is to arrange the workstations to minimize the num-ber of employees in order to produce at the best productive rate with the most reasonable cost,shortest time,and satisfying *** most garment factories,the production lines are balanced using the empirical judgment of the line *** the whole process the data of production time at each step,labor pro-ductivity,proper choices of equipment were always needed to calculate line efﬁ*** far as the issue is concerned,there has not been an academically sewing process analyzing software providing adequate data of sewing motions and sewing time as the credible input for the line balancing *** this goal,this paper presents the results of research on optimizing academically self-built software to analyze the sewing process of knitted products applied to industrial production using Java programming language on Google *** results achieved by the software are not only to analyze sewing products and the technological sewing process,calculate the sewing time on the machine but also analyze the sewing activities of workers into manipulations,movements,and motions to calculate the preparation time for two typical knitted products,namely,Polo-Shirt and T-Shirt with the case studies at General Textile Garment Joint Stock Company Hanoi and Star Fashion Company Limited.

关键词： Sewing motion sewing time GSD MTM polo-shirt T-shirt sewing process sewing operation analyzing software

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