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International Journal of Digital Earth 2023年第1期16卷 3661-3679页

作者： Laura Melgar-García Francisco Martínez-álvarez dieu tien bui Alicia Troncoso Data Science&Big Data Lab Pablo de Olavide UniversitySevilleSpain GIS Group Department of Business and ITUniversity of South-Eastern NorwayGullbringvegen 36B?i Telemark3800Norway

Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the ***, the ability to predict flood-prone areas in advance is crucialfor effective risk management. The objective of this research is to assessand compare three convolutional neural networks, U-Net, WU-Net, andU-Net++, for spatial prediction of pluvial flood with a case study at atropical area in the north of Vietnam. They are relative new convolutionalgorithms developed based on U-shaped architectures. For this task, ageospatial database with 796 historical flood locations and 12 floodindicators was prepared. For training the models, the binary crossentropywas employed as the loss function, while the Adaptive momentestimation (ADAM) algorithm was used for the optimization of themodel parameters, whereas, F1-score and classification accuracy (Acc)were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models,achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and informationto enhance decision-making processes and facilitate the implementation of effective risk management strategies.

关键词： Flash-flood assessment convolutional networks climate change GIS

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

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International Journal of Digital Earth 2016年第11期9卷 1077-1097页

作者： dieu tien bui Binh Thai Pham Quoc Phi Nguyen Nhat-Duc Hoang Geographic Information System Group Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBøi TelemarkNorway Faculty of Geomatics and Land Administration Hanoi University of Mining and GeologyHanoiVietnam Department of Civil Engineering Gujarat Technological UniversityAhmedabadGujaratIndia Department of Geotechnical Engineering University of Transport TechnologyHa NoiVietNam Department of Environmental Sciences Hanoi University of Mining and GeologyHanoiVietnam Faculty of Civil Engineering Institute of Research and DevelopmentDuy Tan UniversityDanangVietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as *** LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM *** this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid *** receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed *** results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating *** model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision *** result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

关键词： Shallow landslide Least-Squares Support Vector Machines differential evolution GIS Vietnam

A Novel Evolutionary Genetic Optimization-Based Adaptive Neuro-Fuzzy Inference System and Geographical Information Systems Predict and Map Soil Organic Carbon Stocks Across an Afromontane Landscape

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Pedosphere 2017年第5期27卷 877-889页

作者： kennedy o.were dieu tien bui Φystein bjarne dick bal ram singh Department of Mathematical Sciences and Technology Norwegian University of Life SciencesP.O.Box 5003 NO.1432 AsNorway Kenya Agricultural and Livestock Research Organisation Kenya Soil SurveyP.O.Box 14733—00806 NairobiKenya Department of Business Administration and Computer Science Faculty of Arts and SciencesUniversity College of Southeast NorwayNO一3800 BΦi TelemarkNoway Department of Environmental Sciences Norwegian University of Life SciencesP.O.Box 5003NO一1432 AsNorway

Soil organic carbon(SOC) pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide(CO_2) and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth. As such, the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system(ANFIS-EG) in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve, Kenya. Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model, which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest *** was achieved with a reasonable level of uncertainty(i.e., root mean square error of 15.07 Mg C ha^(-1)), hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies. There is potential for improving the model performance, as indicated by the current ratio of performance to deviation(1.6). The mapping also revealed marginally higher SOC stocks in the forested ecosystems(i.e., an average of 109.78 Mg C ha^(-1)) than in the agro-ecosystems(i.e., an average of 95.9 Mg C ha^(-1)).

关键词：自适应神经模糊推理系统土壤有机碳储量进化遗传算法地理信息系统预测地图森林保护区景观

Deep learning neural networks for spatially explicit prediction of flash flood probability

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Geoscience Frontiers 2021年第3期12卷 370-383页

作者： Mahdi Panahi Abolfazl Jaafari Ataollah Shirzadi Himan Shahabi Omid Rahmati Ebrahim Omidvar Saro Lee dieu tien bui Geoscience Platform Research Division Korea Institute of Geoscience and Mineral Resources(KIGAM)124Gwahak-ro Yuseong-guDaejeon 34132Republic of Korea Division of Science Education Kangwon National UniversityChuncheon-siGangwon-do 24341Republic of Korea Research Institute of Forests and Rangelands Agricultural ResearchEducationand Extension Organization(AREEO)Tehran 13185-116Iran Department of Rangeland and Watershed Management Faculty of Natural ResourcesUniversity of KurdistanSanandaj 66177-15175Iran Department of Geomorphology Faculty of Natural ResourcesUniversity of KurdistanSanandajIran Kordestan Agricultural and Natural Resources Research and Education Center Agricultural ResearchEducationand Extension Organization(AREEO)SanandajIran Department of Rangeland and Watershed Management Faculty of Natural Resources and Earth SciencesUniversity of KashanKashan 87317-53153Iran Department of Geophysical Exploration Korea University of Science and Technology217 Gajeong-roYuseong-guDaejeon 34113Republic of Korea Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam Department of Zrebar Lake Environmental Research Kurdistan Studies InstituteUniversity of KurdistanSanandajIran

Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning *** study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood *** develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was *** step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing *** CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics *** results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future ***,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study *** study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future *** general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.

关键词： Spatial modeling Machine learning Convolutional neural networks Recurrent neural networks GIS Iran

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling:A case study in the Himalayan area, India

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International Journal of Sediment Research 2018年第2期33卷 157-170页

作者： Binh Thai Pham Ataollah Shirzadi dieu tien bui Indra Prakash M.B.Dholakia Department of Civil Engineering Gujarat Technological University Geographic Information System Group Department of Business and IT University College of Southeast Norway 5. Department of Science ε Technology Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG) 6. Department of Civil Engineering LDCE Gujarat Technological University Department of Geotechnical Engineering University of Transport Technology Department of Rangeland and Watershed Management College of Natural Resources University of Kurdistan

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function(RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area(India). The proposed approach is an integration of the Radial Basis Function(RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters(slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall,distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic(ROC) curve, area under the ROC curve(AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression(LR), Multi-layer Perceptron Neural Networks(MLP Neural Nets), Na(?)ve Bayes(NB), and the hybrid model of Rotation Forest and Decision Trees(RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models(LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.

关键词： Landslide GIS Rotation Forest Radial Base Function Neural Network India

A tree-based intelligence ensemble approach for spatial prediction of potential groundwater

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International Journal of Digital Earth 2020年第12期13卷 1408-1429页

作者： Mohammadtaghi Avand Saeid Janizadeh dieu tien bui Viet Hoa Pham Phuong Thao T.Ngo Viet-Ha Nhu Faculty of Natural Resources and Marine Sciences Tarbiat Modares UniversityTehranIran Geographic Information Science Research Group Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Environment and Labour Safety Ton Duc Thang UniversityHo Chi Minh CityVietnam Ho Chi Minh City Institute of Resources Geography Vietnam Academy of Science and TechnologyHo Chi Minh CityVietnam Institute of Research and Development Duy Tan UniversityDa NangVietnam Department of Geological-Geotechnical Engineering Hanoi University of Mining and GeologyHanoiVietnam

The objective of this research is to propose and confirm a new machine learning approach of Best-First tree(BFtree),AdaBoost(AB),MultiBoosting(MB),and Bagging(Bag)ensembles for potential groundwater mapping and assessing role of influencing *** Yasuj-Dena area(Iran)is selected as a case *** this regard,a Yasuj-Dena database was established with 362 springs locations and 12 groundwater-influencing factors(slope,aspect,elevation,stream power index(SPI),length of slope(LS),topographic wetness index(TWI),topographic position index(TPI),land use,lithology,distance from fault,distance from river,and rainfall).The database was employed to train and validate the proposed groundwater *** area under the curve(AUC)and statistical metrics were employed to check and confirm the quality of the *** result shows that the BFTree-Bag model(AUC=0.810,kappa=0.495)has the highest prediction performance,followed by the BFTree-MB model(AUC=0.785,kappa=0.477),and the BFTree-MB model(AUC=0.745,kappa=0.422).Compared to the benchmark of Random Forests,the BFTree-Bag model performs better;therefore,we conclude that the BFtree-Bag is a new tool should be used for modeling of groundwater potential.

关键词： Environmental modeling groundwater potential GIS ensemble model decision tree

Comparison of machine learning models for gully erosion susceptibility mapping

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Geoscience Frontiers 2020年第5期11卷 1609-1620页

作者： Alireza Arabameri Wei Chen Marco Loche Xia Zhao Yang Li Luigi Lombardo Artemi Cerda Biswajeet Pradhan dieu tien bui Department of Geomorphology Tarbiat Modares UniversityTehranIran College of Geology&Environment Xi’an University of Science and TechnologyXi’an 710054China Key Laboratory of Coal Resources Exploration and Comprehensive Utilization Ministry of Land and ResourcesXi’an 710021China Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation Xi’an 710054China Universita Degli Studi di Cagliari Dipartimento di Scienze Chimiche e Geologiche09042MonserratoCagliariItaly University of Twente Faculty of Geo-Information Science and Earth Observation(ITC)PO Box 217EnschedeAE 7500Netherlands Soil Erosion and Degradation Research Group Departament de GeografiaUniversitat de ValenciaBlasco Iba~nez2846010ValenciaSpain Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNew South WalesAustralia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-ro Gwangjin-guSeoul05006Republic of Korea Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local *** focused on the Bastam watershed where 9.3%of its surface is currently affected by *** learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ***,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning *** cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of ***,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation *** the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we *** differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility *** is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

关键词： Oil erosion GIS Alternating decision tree model Logistic model tree model

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

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Geoscience Frontiers 2021年第3期12卷 146-168页

作者： Himan Shahabi Ataollah Shirzadi Somayeh Ronoud Shahrokh Asadi Binh Thai Pham Fatemeh Mansouripour Marten Geertsema John J.Clague dieu tien bui Department of Geomorphology Faculty of Natural ResourcesUniversity of KurdistanSanandaj 66177-15175Iran Board Member of Department of Zrebar Lake Environmental Research Kurdistan Studies InstituteUniversity of KurdistanSanandaj 66177-15175Iran Department of Rangeland and Watershed Management Faculty of Natural ResourcesUniversity of KurdistanSanandajIran Data Mining Laboratory Department of EngineeringCollege of FarabiUniversity of TehranTehranIran Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam British Columbia Ministry of ForestsLandsNatural Resource Operations and Rural DevelopmentPrince GeorgeBC V2L 1R5Canada Department of Earth Sciences Simon Fraser UniversityBurnabyBC V5A 1S6Canada Geographic Information Science Research Group Ton Duc Thang UniversityHo Chi Minh CityViet Nam Faculty of Environment and Labour Safety Ton Duc Thang UniversityHo Chi Minh CityViet Nam

Flash floods are responsible for loss of life and considerable property damage in many *** susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency *** main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)*** well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed *** metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed *** result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.

关键词： Environmental modeling Flash flood Deep belief network Over-fitting Iran

Landslide Susceptibility Assessment at Hoa Binh Province of Vietnam Using Frequency Ratio Model

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Landslide Susceptibility Assessment at Hoa Binh Province of ...

Tungsten-based nanocatalysts with different structures for visible light responsive photocatalytic degradation of bisphenol A

2012 Asia Pacific Conference on Environmental Science and Technology(APEST 2012)

作者： dieu tien bui Biswajeet Pradhan Owe Lofman Inge Revhaug Oystein B Dick Department of Mathematical Sciences and Technology Norwegian University of Life SciencesP.O.Box 5003IMTN-1432AasNorway Faculty of Surveying and Mapping Hanoi University of Mining and GeologyDong NgacTu LiemHanoiVietnam Advanced Technology Spatial and Numerical Modelling LaboratoryUniversity Putra MalaysiaSerdangSelangor Darul Ehsan 43400Malaysia

The main objective of this study is to investigate a potential application of frequency ratio model for landslide susceptibility mapping in the Hoa Binh province of Vietnam.A landslide inventory map with the total of 118 landslide locations was constructed using the data from various *** landslide inventory was randomly divided into a training dataset 70%（82 landslide locations） for building the landslide model and the remaining 30%（36 landslides locations） for the validation of the *** landslide occurrence related factors were selected such as:slope,aspect,relief amplitude,lithology,landuse,soil type,distance to roads,distance to rivers and distance to *** these factors,landslide susceptibility indexes were calculated by employing the frequency ratio *** the prediction rate method,the landslide susceptibility map was validated using the validation dataset which was not used during the model *** validation results showed that the area under the curve（AUC） for landslide susceptibility map is *** indicates that the model have good prediction capabilities.

关键词： Frequency ratio GIS Remote sensing Landslide susceptibility Hoa Binh Vietnam.

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Journal of Environmental Sciences 2024年第5期139卷 569-588页

作者： Hai Bang Truong Thi Thu Loan Doan Nguyen tien Hoang Nguyen Van Tam Minh Kim Nguyen Le Gia Trung Jin Seog Gwag Nguyen tien Tran Optical Materials Research Group Science and Technology Advanced InstituteVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Applied Technology School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam The University of Da Nang University of Science and Technology54 Nguyen Luong BangDa NangViet Nam The University of Da Nang University of Science and Education459 Ton Duc Thang St.Lien ChieuDa Nang 550000Viet Nam Institute of Veterinary Science and Technology 31ha zoneTrau QuyGia LamHa Noi 12400Viet Nam Department of Physics Yeungnam UniversityGyeongsanGyeongbuk 38541Republic of Korea Center for Advanced Chemistry Institute of Research and DevelopmentDuy Tan University03 Quang TrungDa Nang 550000Viet Nam Faculty of Natural Sciences Duy Tan University03 Quang TrungDa Nang 550000Viet Nam

Environmental pollution,such as water contamination,is a critical issue that must be absolutely ***,three different morphologies of tungsten-based photocatalysts(WO_(3)nanorods,WO_(3)/WS_(2)nanobricks,WO_(3)/WS_(2)nanorods)are made using a simple hydrothermal method by changing the solvents(H_(2)O,DMF,aqueous HCl solution).The as-prepared nanocatalysts have excellent thermal stability,large porosity,and high *** results show all materials have good photocatalytic activity in aqueous media,with WO_(3)/WS_(2)nanorods(NRs)having the best activity in the photodegradation of bisphenol A(BPA)under visible-light *** may originate from increased migration of charge carriers and effective prevention of electron–hole recombination in WO_(3)/WS_(2)NRs,whereby this photocatalyst is able to generate more reactive·OH and·O_(2)^(–)species,leading to greater photocatalytic *** 99.6% of BPA is photodegraded within 60 min when using 1.5 g/L WO_(3)/WS_(2)NRs and 5.0 mg/L BPA at pH ***,the optimal conditions(pH,catalyst dosage,initial BPA concentration)for WO_(3)/WS_(2)NRs are also elaborately *** rod-like heterostructures are expressed as potential catalysts with excellent photostability,efficient reusability,and highly active effectivity in different types of *** particular,the removal efficiency of BPA by WO_(3)/WS_(2)NRs reduces by only 1.5% after five recycling runs and even reaches 89.1%in contaminated lake *** study provides promising insights for the nearly complete removal of BPA from wastewater or different water resources,which is advantageous to various applications in environmental remediation.

关键词： Photodegradation Bisphenol A Tungsten-based nanocatalyst Structure Nanorod