{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T01:17:09Z","timestamp":1793323029086,"version":"4.5.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFC1509703"],"award-info":[{"award-number":["2019YFC1509703"]}]},{"name":"National Key Research and Development Program of China","award":["19A170010"],"award-info":[{"award-number":["19A170010"]}]},{"name":"National Key Research and Development Program of China","award":["214200510030"],"award-info":[{"award-number":["214200510030"]}]},{"name":"National Key Research and Development Program of China","award":["221111321500"],"award-info":[{"award-number":["221111321500"]}]},{"name":"Henan Province University Key Scientific Research Project Plan","award":["2019YFC1509703"],"award-info":[{"award-number":["2019YFC1509703"]}]},{"name":"Henan Province University Key Scientific Research Project Plan","award":["19A170010"],"award-info":[{"award-number":["19A170010"]}]},{"name":"Henan Province University Key Scientific Research Project Plan","award":["214200510030"],"award-info":[{"award-number":["214200510030"]}]},{"name":"Henan Province University Key Scientific Research Project Plan","award":["221111321500"],"award-info":[{"award-number":["221111321500"]}]},{"name":"Central Plains Science and technology innovation leader Project","award":["2019YFC1509703"],"award-info":[{"award-number":["2019YFC1509703"]}]},{"name":"Central Plains Science and technology innovation leader Project","award":["19A170010"],"award-info":[{"award-number":["19A170010"]}]},{"name":"Central Plains Science and technology innovation leader Project","award":["214200510030"],"award-info":[{"award-number":["214200510030"]}]},{"name":"Central Plains Science and technology innovation leader Project","award":["221111321500"],"award-info":[{"award-number":["221111321500"]}]},{"name":"Key research and development Project of Henan province","award":["2019YFC1509703"],"award-info":[{"award-number":["2019YFC1509703"]}]},{"name":"Key research and development Project of Henan province","award":["19A170010"],"award-info":[{"award-number":["19A170010"]}]},{"name":"Key research and development Project of Henan province","award":["214200510030"],"award-info":[{"award-number":["214200510030"]}]},{"name":"Key research and development Project of Henan province","award":["221111321500"],"award-info":[{"award-number":["221111321500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The traditional susceptibility evaluation of geological hazards usually comprises a global susceptibility evaluation of the entire study area but ignores the differences between the local areas caused by spatial non-stationarity. In view of this, the geographically weighted regression model (GWR) was used to divide the study area at regional scale. Seven local areas were obtained with low spatial auto-correlation of each evaluation factor. Additionally, 11 evaluation factors, including the aspect, elevation, curvature, ground roughness, relief amplitude, slope, lithology, distance from the fault, height of the cut slope, multiyear average rainfall and the normalized difference vegetation index (NDVI) were selected to establish the evaluation index system of the geological hazard susceptibility. The Pearson coefficient was used to remove the evaluation factors with high correlation. The global and seven local areas were evaluated for susceptibility using the information value model and the global and regional division susceptibility evaluation results were obtained. The results show that the regional division information value model had better prediction performance (AUC = 0.893) and better accuracy. This model adequately considers the influence of the geological hazard impact factors in the different local areas on geological hazard susceptibility and weakens the influence of some factors that have higher influence in the global model but lower influence in local areas on the evaluation results. Therefore, the use of the regional division information value model for susceptibility evaluation is more consistent with the actual situation in the study area and is more suitable for guiding risk management and hazard prevention and mitigation.<\/jats:p>","DOI":"10.3390\/ijgi12010017","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T02:06:03Z","timestamp":1673402763000},"page":"17","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Evaluation of Geological Hazard Susceptibility Based on the Regional Division Information Value Method"],"prefix":"10.3390","volume":"12","author":[{"given":"Jingru","family":"Ma","sequence":"first","affiliation":[{"name":"College of Geosciences and Engineering, North China University of Water Resources and Electric Power, No. 136 Jinshui East Road, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geosciences and Engineering, North China University of Water Resources and Electric Power, No. 136 Jinshui East Road, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangxiang","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Geosciences and Engineering, North China University of Water Resources and Electric Power, No. 136 Jinshui East Road, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4985","DOI":"10.15244\/pjoes\/134852","article-title":"AHP-Normal Cloud-Model-Based Method for Risk Assessment of Rockfall Hazards in Laoying Yan","volume":"30","author":"Gu","year":"2021","journal-title":"Pol. J. Environ. Stud."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.culher.2022.04.009","article-title":"Multihazard susceptibility assessment using analytic hierarchy process: The Derwent Valley Mills UNESCO World Heritage Site case study (United Kingdom)","volume":"55","author":"Guerriero","year":"2022","journal-title":"J. Cult. Herit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s12665-015-5073-4","article-title":"First level seismic microzonation map of Al-Madinah province, western Saudi Arabia using the geographic information system approach","volume":"75","author":"Moustafa","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_4","first-page":"411","article-title":"Three-mark Matrix Improves AHP Assessment Model and its Application on Geological Hazard Susceptibility","volume":"3","author":"Zhang","year":"2010","journal-title":"Disaster Adv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e13502","DOI":"10.1002\/ep.13502","article-title":"Biogas modeling by fuzzy comprehensive index of municipal wastewater and sludge","volume":"40","author":"Anaokar","year":"2020","journal-title":"Environ. Prog. Sustain. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.enggeo.2012.01.008","article-title":"Discussion on \u201cCombining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS\u201d","volume":"129","author":"Gokceoglu","year":"2012","journal-title":"Eng. Geol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1016\/j.asr.2010.01.006","article-title":"Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia","volume":"45","author":"Pradhan","year":"2010","journal-title":"Adv. Space Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"117","DOI":"10.9798\/KOSHAM.2014.14.6.117","article-title":"Landslide Hazard Prediction Map Based on Logistic Regression Model for Applying in the Whole Country of South Korea","volume":"14","author":"Woo","year":"2014","journal-title":"J. Korean Soc. Hazard Mitig."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s11069-013-0661-7","article-title":"Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China","volume":"68","author":"Xu","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Chen, W. (2020). Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation. Remote. Sens., 12.","DOI":"10.3390\/rs12142180"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1007\/s12205-012-1242-0","article-title":"GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju (Korea)","volume":"16","author":"Quan","year":"2012","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/19475705.2018.1487471","article-title":"Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: An example of the 2013 Minxian (China) Mw 5.9 event","volume":"10","author":"Tian","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1007\/s11069-014-1245-x","article-title":"Estimating landslide susceptibility through a artificial neural network classifier","volume":"74","author":"Tsangaratos","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s12665-018-7268-y","article-title":"Bagging based Support Vector Machines for spatial prediction of landslides","volume":"77","author":"Prakash","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.geomorph.2017.06.013","article-title":"Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India","volume":"295","author":"Kumar","year":"2017","journal-title":"Geomorphology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1080\/10106049.2020.1837258","article-title":"Assessing and mapping landslide susceptibility using different machine learning methods","volume":"37","author":"Orhan","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Prakash, I., Chen, W., Ly, H.-B., Ho, L.S., Omidvar, E., Tran, V.P., and Bui, D.T. (2019). A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping. Sustainability, 11.","DOI":"10.3390\/su11226323"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yu, L., Cao, Y., Zhou, C., Wang, Y., and Huo, Z. (2019). Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines: A Case Study from Wushan Segment in the Three Gorges Reservoir Area, China. Appl. Sci., 9.","DOI":"10.3390\/app9224756"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1080\/10106049.2017.1323964","article-title":"Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea","volume":"33","author":"Kim","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107201","DOI":"10.1016\/j.geomorph.2020.107201","article-title":"A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm","volume":"362","author":"Sun","year":"2020","journal-title":"Geomorphology"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ba, Q., Chen, Y., Deng, S., Wu, Q., Yang, J., and Zhang, J. (2017). An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6010018"},{"key":"ref_22","first-page":"508","article-title":"Application of weighted information volume model in the evaluation of landslide susceptibility-An example in Enshi City, Hubei Province","volume":"20","author":"Fan","year":"2012","journal-title":"J. Eng. Geol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s12517-017-3315-3","article-title":"Landslide hazard mapping along national highway-154A in Himachal Pradesh, India using information value and frequency ratio","volume":"10","author":"Singh","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_24","first-page":"128","article-title":"Comparison of models for hazard assessment of landslide","volume":"15","author":"Zhao","year":"2006","journal-title":"J. Ziran Zaihai Xuebao\/J. Nat. Disasters"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1111\/j.0033-0124.1992.00088.x","article-title":"On the Issues of Scale, Resolution, and Fractal Analysis in the Mapping Sciences","volume":"44","author":"Lam","year":"1992","journal-title":"Prof. Geogr."},{"key":"ref_26","first-page":"129","article-title":"Slope instability recognition, analysis and zonation","volume":"247","author":"Soeters","year":"1996","journal-title":"Landslides Investig. Mitig."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.enggeo.2008.03.016","article-title":"Applicability of landslide susceptibility and hazard zoning at different scales","volume":"102","author":"Cascini","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_28","first-page":"13","article-title":"Guidelines for landslide susceptibility, hazard and risk zoning for land use planning","volume":"42","author":"Flentje","year":"2007","journal-title":"Aust. Geomech. J."},{"key":"ref_29","first-page":"32","article-title":"Methodology for evaluating susceptibility to landslides under strong seismic conditions at large scale evaluation scales","volume":"26","author":"Wei","year":"2011","journal-title":"J. Hunan Univ. Sci. Technol. Nat. Sci. Ed."},{"key":"ref_30","unstructured":"Qin, W.Z. (2007). Basic Theory and Applied Research on Geographically Weighted Regression. [Ph.D. Thesis, Tongji University]."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s10980-006-9058-2","article-title":"Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models","volume":"22","author":"Kupfer","year":"2006","journal-title":"Landsc. Ecol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2234","DOI":"10.1016\/j.rse.2009.06.007","article-title":"Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia","volume":"113","author":"Propastin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geomorph.2014.07.026","article-title":"Landslide susceptibility mapping using geographically-weighted principal component analysis","volume":"226","author":"Sabokbar","year":"2014","journal-title":"Geomorphology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1093\/icesjms\/fsp224","article-title":"Exploring spatial non-stationarity of fisheries survey data using geo-graphically weighted regression (GWR): An example from the Northwest Atlantic","volume":"67","author":"Windle","year":"2010","journal-title":"ICES J. Mar. Sci."},{"key":"ref_35","first-page":"340","article-title":"Information and logistic regression models based coupling analysis for susceptibility of geological hazards","volume":"26","author":"Fan","year":"2018","journal-title":"J. Eng. Geol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gao, R., Wang, C., Liang, Z., Han, S., and Li, B. (2021). A Research on Susceptibility Mapping of Multiple Geological Hazards in Yanzi River Basin, China. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10040218"},{"key":"ref_37","first-page":"12490","article-title":"Geographic Information System and Weighted Information Value Assessments of Geologic Hazards in Maoxian County, Sichuan","volume":"21","author":"Chen","year":"2021","journal-title":"Sci. Technol. Eng."},{"key":"ref_38","first-page":"126","article-title":"Assessment on the susceptibility of sudden geological hazards in mountainous areas of Beijing","volume":"32","author":"Luo","year":"2021","journal-title":"Chin. J. Geol. Hazard Control"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.enggeo.2008.03.010","article-title":"Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview","volume":"102","author":"Castellanos","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_40","first-page":"840","article-title":"Research on Geological Disaster Susceptibility Division Method Based on BP Neural Network","volume":"35","author":"Zhu","year":"2021","journal-title":"Resour. Environ. Eng."},{"key":"ref_41","first-page":"602","article-title":"Comparative study of geological hazards susceptibility assessment: Constraints from the information value+ logistic regression model and the CF+ logistic regression model","volume":"32","author":"Zhang","year":"2018","journal-title":"Geoscience"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1016\/j.neucom.2011.03.027","article-title":"Global and local modelling in RBF networks","volume":"74","author":"Herrera","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1142\/S0129065704001930","article-title":"Efficient training of RBF networks for classification","volume":"14","author":"Nabney","year":"2004","journal-title":"Int. J. Neural Syst."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/1\/17\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:52Z","timestamp":1760119552000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/1\/17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["ijgi12010017"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12010017","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]}}}