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Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 467-500页

作者： Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models Duc-Dam Nguyen Nguyen Viet Tiep Quynh-Anh Thi Bui Hiep Van Le Indra Prakash Romulus Costache Manish Pandey Binh Thai Pham Faculty of Civil Engineering University of Transport TechnologyThanh XuanHa Noi100000Vietnam Faculty of Hydraulic Engineering Hanoi University of Civil EngineeringHa Noi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India Research Institute of the University of Bucharest Bucharest050663Romania National Institute of hydrology and Water Management Bucharest013686Romania department of Civil Engineering Transilvania University of BrasovBrasov500152Romania Danube Delta National Institute for Research and Development Tulcea820112Romania Marwadi University Research Center(MURC) Marwadi UniversityRajkotGujaratBharat360003India department of Civil Engineering Faculty of Engineering&TechnologyMarwadi UniversityRajkotGujaratBharat360003India

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and ***,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and *** models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to *** factors were instrumental in predicting the output variable,which was the probability of landslide *** analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other *** AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of *** results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

关键词： Landslide susceptibility map spatial analysis ensemble modelling information values(IV)

Generation of a long-term daily gridded precipitation dataset for the rTjSj-Upper Indus Basin(UIB)through temporal Reconstruction,Correction I sag&Informed Regionalization-“ReCIR”

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International Soil and Water Conservation Research 2021年第3期9卷 445-460页

作者： Asim Jahangir Khan Manfred Koch department of Geohydraulics and Engineering hydrology University of Kassel34125KasselGermany

This study attempted to generate a long-term(1961-2010)daily gridded precipitation dataset for the Upper Indus Basin(UIB)with orographic adjustments so as to generate realistic precipitation estimates,enabling hydrological and water resource investigations that can close the water balance,that is difficult,if not impossible to achieve with the currently available precipitation data products for the *** procedure includes temporal reconstruction of precipitation series at points where data were not recorded prior to the mid-nineties,followed by a regionalization of the precipitation series to a smaller scale across the basin(0.125°x 0.125°),while introducing adjustments for the orographic effect and changes in glacier *** reconstruction process involves interpolation of the precipitation at virtual locations of the current(1995-)dense observational network,followed by corrections for frequency and intensity and adjustments for temporal trends at these virtual *** data generated in this way were further validated for temporal and spatial representativeness through evaluation of SWAT-modelled streamflow responses against observed flows across the *** results show that the calibrated SWAT-simulated daily discharge at the basin outlet as well as at different sub-basin outlets,when forcing the model with the reconstructed precipitation of years 1973—1996,is almost identical to that when forcing it with the reference precipitation data(1997-2008).Finally,the spatial distribution pattern of the reconstructed(1961—1996)and reference(1997—2008)precipitation were also found consistent across the UIB,reflecting well the large-scale atmospheric-circulation pattern in the region.

关键词： Precipitation Temporal reconstruction Orographic correction factor(OCF) Soil and water assessment tool(SWAT) Glacier/water mass balance Upper indus basin(UIB)

Hydrogeochemical evaluation and statistical analysis of groundwater of Sylhet, north-eastern Bangladesh

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Acta Geochimica 2019年第3期38卷 440-455页

作者： Nasir Ahmed Md.Bodrud-Doza S.M.Didar-Ul Islam Manzoor Ahmad Choudhry Md.Iftakharul Muhib Anwar Zahid Shahadat Hossain Md.Moniruzzaman Nipa Deb Md.Abdul Quaiyum Bhuiyan Isotope hydrology Division Institute of Nuclear Science and Technology Atomic Energy Research Establishment department of Environmental Sciences Jahangirnagar University Isotope hydrology Section Department of Nuclear Sciences and Applications International Atomic Energy Agency Groundwater hydrology Bangladesh Water Development Board

To investigate the hydrogeochemical characteristics of groundwater 23 shallow, 30 intermediate and 38 deep wells samples were collected from Sylhet district of Bangladesh, and analyzed for temperature, pH, Eh, EC,DO, DOC, Na^+, K^+, Ca^(2+), Mg^(2+), Cl^-, SO_4^(2-), NO_3^-,HCO_3^-, SiO_2^-, Fe, Mn and As. Besides, 12 surface water samples from Surma and Kushiyara Rivers were also collected and analyzed to understand the influence into aquifers. Results revealed that, most of the groundwater samples are acidic in nature, and Na–HCO_3 is the dominant groundwater type. The mean value of temperature, EC,Na^+, K^+, Ca^(2+), Mg^(2+), Cl^-, NO_3^- and SO_4^(2-) were found within the range of permissible limits, while most of the samples exceeds the allowable limits of Fe, Mn and As concentrations. However, relatively higher concentration of Fe and Mn were found in deep water samples and reverse trend was found in case of As. The mean concentrations of As in shallow, intermediate and deep wells were 39.3, 25.3and 21.4 lg/L respectively, which varied from 0.03 to148 lg/L. From spatial distribution, it was found that Fe,Mn and As concentrations are high but patchy in northern,north-western, and south-western part of Sylhet region. The most influential geochemical process in study area were identified as silicate weathering, characterized by active cation exchange process and carbonate weathering, which thereby can enhance the elemental concentrations in groundwater. Pearson's correlation matrix, principal component analysis and cluster analysis were also employed to evaluate the controlling factors, and it was found that, both natural and anthropogenic sources were influencing the groundwater chemistry of the aquifers. However, surface water has no significant role to contaminate the aquifers,rather geogenic factors affecting the trace elemental contamination. Thus it is expected that, outcomes of this study will provide useful insights for future groundwater monitoring and

关键词： Arsenic Groundwater Hydrogeochemistry Multivariate statistics Spatial distribution

Rivers increasingly warmer:Prediction of changes in the thermal regime of rivers in Poland

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Journal of Geographical Sciences 2025年第1期 139-174页

作者： Mariusz PTAK Teerachai AMNUAYLOJAROEN Mariusz SOJKA department of hydrology and Water Management Adam Mickiewicz University School of Energy and Environment University of Phayao Atmospheric Pollution and Climate Research Unit School of Energy and Environment University of Phayao department of Land Improvement Environmental Development and Spatial Management Poznań University of Life Sciences

Emphasis on future environmental changes grows due to climate change, with simulations predicting rising river temperatures globally. For Poland, which has a long history of thermal studies of rivers, such an approach has not been implemented to date. This study used 9 Global Climate Models and tested three machine-learning techniques to predict river temperature changes. Random Forest performed best, with R2=0.88 and lowest error(RMSE:2.25, MAE:1.72). The range of future water temperature changes by the end of the 21st century was based on the Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. It was determined that by the end of the 21st century, the average temperature will increase by 2.1°C(SSP2-4.5) and 3.7°C(SSP5-8.5). A more detailed analysis, divided by two major basins Vistula and Odra, covered about 90% of Poland's territory. The average temperature increase, according to scenarios SSP2-4.5 and SSP5-8.5 for the Odra basin rivers,is 1.6°C and 3.2°C and for the Vistula basin rivers 2.3°C and 3.8°C, respectively. The Vistula basin's higher warming is due to less groundwater input and continental climate *** findings provide a crucial basis for water management to mitigate warming effects in Poland.

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Determination of the effective utilization coefficient of irrigation water based on geographically weighted regression

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Frontiers of Earth Science 2022年第2期16卷 401-410页

作者： Rui SHI Gaoxu WANG Xuan ZHANG Yi XU Yongxiang WU Wei WU College of hydrology and Water Resource Hohai UniversityNanjing 210098China State Key Laboratory of hydrology-Water Resources and Hydraulic Engineering Nanjing 210029China hydrology and Water Resources department Nanjing Hydraulic Research InstituteNanjing 210029China

This study uses geographically weighted regression to determine the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang Province,China,owing to the influences of spatial attributes on the irrigation *** sample set of this study comprised 165 agricultural test sites.A multivariate linear regression model and a geographically weighted regression model were established using the effective utilization coefficient of agricultural irrigation water as the dependent variable in addition to a suite of independent variables,including the actual irrigation area,the percentage of farmland using water-saving irrigation,the type of irrigation area,the net water consumption per mu,the water intake method,the terrain slope,and the soil field *** revealed a positive spatial correlation and noticeable agglomeration features in the effective utilization coefficient of irrigation water in Zhejiang *** geographically weighted regression model performed better in terms of fit and prediction accuracy than the multivariate linear regression *** obtained findings confirm the suitability of the geographically weighted regression model for determining the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang,and offer a new approach on a regional scale.

关键词： effective utilization coefficient of irrigation water spatial autocorrelation multivariate linear regression geographically weighted regression

Effects of heterogeneity distribution on hillslope stability during rainfalls

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Water Science and Engineering 2016年第2期9卷 134-144页

作者： Jing-sen Cai E-chuan Yan Tian-chyi Jim Yeh Yuan-yuan Zha Faculty of Engineering China University of Geosciences department of hydrology and Water Resources University of Arizona

The objective of this study was to investigate the spatial relationship between the most likely distribution of saturated hydraulic conductivity(Ks) and the observed pressure head(P) distribution within a hillslope. The cross-correlation analysis method was used to investigate the effects of the variance of ln Ks, spatial structure anisotropy of ln Ks, and vertical infiltration flux(q) on P at some selected locations within the hillslope. The cross-correlation analysis shows that, in the unsaturated region with a uniform flux boundary, the dominant correlation between P and Ksis negative and mainly occurs around the observation location of P. A relatively high P value is located in a relatively low Kszone, while a relatively low P value is located in a relatively high Kszone. Generally speaking, P is positively correlated with q/Ksat the same location in the unsaturated region. In the saturated region, the spatial distribution of Kscan significantly affect the position and shape of the phreatic surface. We therefore conclude that heterogeneity can cause some parts of the hillslope to be sensitive to external hydraulic stimuli(e.g., rainfall and reservoir level change), and other parts of the hillslope to be insensitive. This is crucial to explaining why slopes with similar geometries would show different responses to the same hydraulic stimuli, which is significant to hillslope stability analysis.

关键词： Cross-correlation analysis Heterogeneity Hillslope stability Saturated hydraulic conductivity Stochastic conceptualization Pore-water pressure

Identifying soil structure along headwater hillslopes using ground penetrating radar based technique

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Journal of Mountain Science 2016年第3期13卷 405-415页

作者： HAN Xiao-le LIU Jin-tao ZHANG Jun ZHANG Zhi-cai State Key Laboratory of hydrology-Water Resources and Hydraulic Engineering Hohai University department of College of hydrology and Water Resources Hohai University Institute of Geochemistry Chinese Academy of Sciences

Soil structure plays an important role in understanding soil attributes as well as hydrological processes. Effective method to obtain high quality soil map is therefore important for both soil science research and soil work ability improvement. However,traditional method such as digging soil pits is destructive and time-consuming. In this study, the structure of headwater hillslopes from Hemuqiao catchment(Taihu Basin, China) have been analyzed both by indirect(ground penetrating radar, GPR) and direct(excavation or soil auger) methods. Four transects at different locations of hillslopes in the catchment were selected for GPR survey. Three of them(#1, #2, and #3) were excavated to obtain fullscale soil information for interpreting radar *** found that the most distinct boundary that can be detected by GPR is the boundary between soil and underlain bedrock. In some cases(e.g., 8-17 m in transect #2), in which the in situ soil was scarcely affected by colluvial process, different soil layers can be identified. This identification process utilized the sensitive of GPR to capture abrupt changes of soil characteristics in layer boundaries, e.g., surface organic layer(layer #1) and bamboo roots layer(layer#2, contain stone fragments), illuvial deposits layer(layer #3) and regolith layer(layer #4). However, in areas where stone fragments were irregularly distributed in the soil profile(highly affected bycolluvial and/or fluvial process), it was possible to distinguish which part contains more stone fragments in soil profile on the basis of reflection density(transect #3). Transect #4(unexcavated) was used to justify the GPR method for soil survey based on experiences from former transects. After that, O horizon thickness was compared by a hand *** work has demonstrated that GPR images can be of a potential data source for hydrological predictions.

关键词： Ground Penetrating Radar(GPR) Hillslope Soil structure

Transferability and Scalability of Soil Total Carbon Prediction Models in Florida, USA

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Pedosphere 2018年第6期28卷 856-872页

作者： Sabine GRUNWALD Congrong YU Xiong XIONG department of Soil and Water Sciences University of Florida State Key Laboratory of hydrology-Water Resources and Hydraulic Engineering Hohai University College of hydrology and Water Resource Hohai University Agricultural and Biological Engineering department University of Florida

The applicability, transferability, and scalability of visible/near-infrared(VNIR)-derived soil total carbon(TC) models are still poorly understood. The objectives of this study were to: i) compare models of three multivariate statistical methods, partial least squares regression(PLSR), support vector machine(SVM), and random forest methods, to predict soil logarithm-transformed TC(logTC) using five fields(local scale) and a pooled(regional-scale) VNIR spectral dataset(a total of 560 TC spectral datasets), ii)assess the model transferability among fields, and iii) evaluate their up-and downscaling behaviors in Florida, USA. The transferability and up-and downscaling of the models were limited by the following factors: i) the spectral data domain, ii) soil attribute domain,iii) methods that describe the internal model structure of VNIR-TC relationships, and iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R^2> 0.86,bias 2.70%, and ratio of prediction error to interquartile range(RPIQ) > 4.54. The PLSR method performed substantially better than the SVM method to scale and transfer the TC models. This could be attributed to the tendency of SVM to overfit models, while the asset of the PLSR method was its robustness when the models were validated with independent datasets, transferred, and/or scaled. The upscaled soil TC models performed somewhat better in terms of model fit(R2), RPD, and RPIQ, whereas the downscaled models showed less bias and smaller RMSE based on PLSR. We found no universal trend indicating which of the four limiting factors mentioned above had the most impact that constrained the transferability and scalability of the models. Given that several factors can impinge on the empirically derived soil spectral prediction models, as demonstrated by this study,

关键词： attribute domain multivariate statistical methods partial least squares regression random forest support vector machine soil carbon dynamics visible-near infrared spectroscopy

Laplace-Transform Finite Element Solution of Nonlocal and Localized Stochastic Moment Equations of Transport

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Communications in Computational Physics 2009年第6期6卷 131-161页

作者： Eric Morales-Casique Shlomo P.Neuman department of hydrology and Water Resources University of ArizonaTucsonArizona 85721USA

Morales-Casique et al.(*** Res.,29(2006),pp.1238-1255)developed exact first and second nonlocal moment equations for advective-dispersive transport in finite,randomly heterogeneous geologic *** velocity and concentration in these equations are generally nonstationary due to trends in heterogeneity,conditioning on site data and the influence of forcing ***-Casique et al.(*** Res.,29(2006),pp.1399-1418)solved the Laplace transformed versions of these equations recursively to second order in the standard deviationσY of(natural)log hydraulic conductivity,and iteratively to higher-order,by finite elements followed by numerical inversion of the Laplace *** did the same for a space-localized version of the mean transport *** we recount briefly their theory and algorithms;compare the numerical performance of the Laplace-transform finite element scheme with that of a high-accuracy ULTIMATE-QUICKEST algorithm coupled with an alternating split operator approach;and review some computational results due to Morales-Casique et al.(*** Res.,29(2006),pp.1399-1418)to shed light on the accuracy and computational efficiency of their recursive and iterative solutions in comparison to conditional Monte Carlo simulations in two spatial dimensions.

关键词： Geologic media porous media random heterogeneity conditioning stochastic transport moment equations nonlocality localization Laplace transform finite elements ultimatequickest