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

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

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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

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Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

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Pedosphere 2022年第5期32卷 718-732页

作者： Khabat KHOSRAVI Phuong T.T.NGO Rahim BARZEGAR John QUILTY Mohammad T.AALAMI Dieu T.bui Department of Watershed Management Engineering Ferdowsi University of MashhadMashhad 93(Iran) Department of Earth and Environment Florida International UniversityMiami 33199(USA) Institute of Research and Development Duy Tan UniversityDa Nang 550000(Vietnam) Department of Bioresource Engineering McGill UniversitySte Anne de Bellevue QC H9X(Canada) Faculty of Civil Engineering University of TabrizTabriz 51(Iran) Department of Civil and Environmental Engineering University of WaterlooWaterloo N2L 3G1(Canada) Department of Business and IT University of South-Eastern NorwayNotodden 3603(Norway)

Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and *** and physical models have been developed to predict cumulative infiltration(CI),but are often *** this study,several novel standalone machine learning algorithms(M5Prime(M5P),decision stump(DS),and sequential minimal optimization(SMO))and hybrid algorithms based on additive regression(AR)(i.e.,AR-M5P,AR-DS,and AR-SMO)and weighted instance handler wrapper(WIHW)(i.e.,WIHW-M5P,WIHW-DS,and WIHW-SMO)were developed for CI *** Soil Conservation Service(SCS)model developed by the United States Department of Agriculture(USDA),one of the most popular empirical models to predict CI,was considered as a ***,154 measurements of CI(explanatory/input variables)were taken from 16 sites in a semi-arid region of Iran(Illam and Lorestan provinces).Six input variable combinations were considered based on Pearson correlations between candidate model inputs(time of measuring and soil bulk density,moisture content,and sand,clay,and silt percentages)and *** dataset was divided into two subgroups at random:70%of the data were used for model building(training dataset)and the remaining 30%were used for model validation(testing dataset).The various models were evaluated using different graphical approaches(bar charts,scatter plots,violin plots,and Taylor diagrams)and quantitative measures(root mean square error(RMSE),mean absolute error(MAE),Nash-Sutcliffe efficiency(NSE),and percent bias(PBIAS)).Time of measuring had the highest correlation with CI in the study *** best input combinations were different for different *** results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone *** AR-M5P model provided the most accurate CI predictions(RMSE=0.75 cm,MAE=0.59 cm,NSE=0.98),while the SCS model had the lowest performance(RMSE=4.77 cm,MAE=2.64 cm,NSE=

关键词： additive regression hybrid algorithms empirical model soil water infiltration weighted instances handler wrapper

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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

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Calibration of a neutron dose rate meter in various neutron standard fields

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Nuclear Science and Techniques 2020年第6期31卷 75-82页

作者： Tuan Khai Nguyen Hoai-Nam Tran Quynh Ngoc Nguyen Thi My Linh Dang Van Loat bui Thiansin Liamsuwan Ngoc-Thiem Le Vietnam Agency for Radiation and Nuclear Safety 113 Tran Duy HungHanoi 100000Vietnam Institute of Fundamental and Applied Sciences Duy Tan UniversityHo Chi Minh 700000Vietnam Faculty of Natural Sciences Duy Tan University3 Quang TrungDa Nang 550000Vietnam Institute for Nuclear Science and Technology VINATOM179 Hoang Quoc VietHanoi 100000Vietnam Faculty of Physics VNU University of Science334 Nguyen TraiHanoi 100000Vietnam Nuclear Research and Development Division Thailand Institute of Nuclear TechnologyOngkharakNakorn NayokThailand 7 Faculty of Medicine and Public HealthHRH Princess Chulabhorn College of Medical ScienceChulabhorn Royal AcademyBangkokThailand

This paper presents the calibration of a neutron dose rate meter and the evaluation of its calibration factors(CFs)in several neutron standard fields(i.e.,two standard fields with bare sources of252Cf and241Am-Be,and five simulated workplace fields with241Am-Be moderated sources).The calibration in standard fields with bare sources was conducted by following the recommendations of the ISO 8529 *** measured total neutron ambient dose equivalent rates,denoted as H*(10)tot,were analyzed to obtain direct components,denoted as H*(10)dir,using a reduced fitting *** CF was then calculated as the ratio between the conventional true value of the neutron ambient dose equivalent rate in a free field,denoted as H*(10)FF,and the value of H*(10)*** contrast,in the simulated workplace neutron fields,the calibration of the neutron dose rate meter was conducted by following the ISO 12789 *** CF was calculated as the ratio between the values of H*(10)totmeasured by a standard instrument(i.e.,Bonner sphere spectrometer)and the neutron dose rate *** CF values were obtained in the range of 0.88–*** standard uncertainties(k=1)of the CFs were determined to be in the range of approximately 6.6–13.1%.

关键词： Neutron standard fields Neutron dose rate meter Calibration factor 252Cf source 241AmBe source

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K-Ar Dating of Fault Gouges from the Red River Fault Zone of Vietnam

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Acta Geologica Sinica(English Edition) 2016年第5期90卷 1653-1663页

作者： bui Hoang Bac NGO Xuan Thanh Yungoo SONG Tetsumaru ITAYA Koshi YAGI KHUONG The Hung NGUYEN Tien Dung Faculty of Geosciences and Geoengineering Hanoi University of Mining and Geology Duc Thang Bac Tu Liem Hanoi Vietnam Department of Earth System Sciences Yonsei University 50 Yonsei-ro Seoul 120-749 Republic of Korea Japan Geochronology Network (NPO) 2-5 Nakashima Naka-ku Okayama 703-8252 Japan Hiruzen Institute for Geology and Chronology Co. Ltd. 2-5 Nakashima Nakaku Okayama 703-8252 Japan

Constraining the timing of fault zone formation is fundamentally important in terms of geotectonics to understand structural evolution and brittle fault *** paper presents the first authigenic illite K-Ar age data from fault gouge samples collected from the Red River Shear Zone at Lao Cai province,*** fault gouge samples were separated into three grain-size fractions（〈0.1 μm,0.1-0.4 μm and 0.4-1.0 μm）.The results show that the K-Ar age values decrease from coarser to finer grain fractions（24.1 to 19.2 Ma）,suggesting enrichment in finer fraction of morerecently grown authigenic *** timing of the fault movement are the lower intercept ages at 0%detrital illite（19.2 ± 0.92 Ma and 19.4 ± 0.49 Ma）.In combination with previous geochronological data,this result indicates that the metamorphism of the Day Nui Con Voi（DNCV） metamorphic complex took place before ca.26.8 *** about 26.8 Ma-25 Ma,the fault strongly acted to cause the rapid exhumation of the rocks along the Red River-Ailoa Shan Fault Zone（RR-ASFZ）.During brittle deformation,the DNCV slowly uplifted,implying weak movement of the *** brittle deformation might have lasted for ca.5 Ma.

关键词： illite K-Ar dating Red River Fault Zone polytype gouge authigenitic

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Enhancing global hepatocellular carcinoma management:Bridging Eastern and Western perspectives on dexamethasone and Nacetylcysteine before transarterial chemoembolization

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World Journal of Gastroenterology 2024年第47期30卷 4983-4990页

作者： Thang Viet Luong Nam Van duc Nguyen Linh Duy Le Hieu Nguyen Hoang Minh Hai Nguyen Ngoc Dang Department of Internal Medicine University of Medicine and PharmacyHue UniversityHue 530000Viet Nam Department of Diagnostic Imaging University of Medicine and PharmacyHue UniversityHue 530000Viet Nam Faculty of Medicine Duy Tan UniversityDa Nang 550000Viet Nam

This article explores the integration of Eastern and Western perspectives on the use of dexamethasone and N-acetylcysteine as premedications in transarterial chemoembolization for hepatocellular carcinoma(HCC).By examining key concerns raised by Western researchers,particularly regarding the different etiologies of liver cirrhosis,and contrasting them with robust clinical data from Asia,this article highlights the necessity for region-specific research and proposes future directions for global HCC management.

关键词： Hepatocellular carcinoma Transarterial chemoembolization Dexamethasone N-acetylcysteine East-West perspectives Post-embolization syndrome

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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

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德意志联邦共和国教师培训的经验及其对越南教师培训改革的启示

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佳木斯职业学院学报 2017年第5期33卷 202-203页

作者： [越南]bui Minh duc NGUYEN Thanh Hung NGUYEN Thi Ngoc Be DAO Thi Viet Anh 越南河内师范大学二越南顺化师范大学心理教育系

为了满足普通教育计划改革的要求,需要改革教师培训计划。旨在建设和发展一支数量足够、结构合理、高素质、高水平的教师队伍,提高各师范大学教师培训工作的质量与效果。本文主要就处于世界领先水平的德意志联邦共和国教师培训工作进行... 详细信息

为了满足普通教育计划改革的要求,需要改革教师培训计划。旨在建设和发展一支数量足够、结构合理、高素质、高水平的教师队伍,提高各师范大学教师培训工作的质量与效果。本文主要就处于世界领先水平的德意志联邦共和国教师培训工作进行介绍,力图找出越南可借鉴的经验,提出了越南教师培训改革的若干建议。

关键词：经验教师培训质量效果教师

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限定固有频率约束下基于全面学习和相量粒子群的结构优化

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Acta Mechanica Sinica 2023年第4期39卷 44-56页

作者： Ei Cho Pyone Sawekchai Tangaramvong Thu Huynh Van Linh Van Hong bui 高伟 Center of Excellence in Applied Mechanics and Structures Department of Civil Engineering Chulalongkorn University Bangkok 10330 Thailand Division of Computational Mathematics and Engineering Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Centre of Infrastructure Engineering and Safety School of Civil and Environmental Engineer The University of New South Wales Sydney NSW 2000 Australia

本文提出了全面学习和相量粒子群相结合的方法用于解决限定固有频率约束下的空间结构优化问题.通过将相量理论和全面学习策略相结合以提高粒子群算法的效率.在优化的过程中,一个所谓的相角采用了周期正弦和余弦函数给定义速度的关键参... 详细信息

本文提出了全面学习和相量粒子群相结合的方法用于解决限定固有频率约束下的空间结构优化问题.通过将相量理论和全面学习策略相结合以提高粒子群算法的效率.在优化的过程中,一个所谓的相角采用了周期正弦和余弦函数给定义速度的关键参数建模使得速度可以从所有粒子前一步的最佳位置中选取.这个方法不但实现了粒子们可以快速学习并且在少量的计算中进行了在作用力和固有频率约束下结构杆件的安全性评估和优化尺寸分布.提出的优化设计方法成功解决了一些工程应用结构的基准设计问题,这证明了本文方法相对于最新元启发方法的准确性和稳定性.

关键词：余弦函数固有频率最佳位置粒子群算法安全性评估空间结构优化相量参数建模

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