{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T22:39:07Z","timestamp":1777934347113,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Start-up Fund for Scientific Research of the East China University of Technology","award":["DHTP2018001"],"award-info":[{"award-number":["DHTP2018001"]}]},{"name":"Jiangxi Talent Program","award":["900\/2120800004"],"award-info":[{"award-number":["900\/2120800004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.<\/jats:p>","DOI":"10.3390\/ijgi9110695","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T08:18:23Z","timestamp":1606119503000},"page":"695","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3056-3069","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6362-9840","authenticated-orcid":false,"given":"Weicheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1998-2916","authenticated-orcid":false,"given":"Yaozu","family":"Qin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"given":"Ziyu","family":"Lin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"given":"Guiliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"264 Geological Team of Jiangxi Nuclear Industry, Ganzhou 341000, China"}]},{"given":"Renxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"264 Geological Team of Jiangxi Nuclear Industry, Ganzhou 341000, China"}]},{"given":"Yong","family":"Song","sequence":"additional","affiliation":[{"name":"264 Geological Team of Jiangxi Nuclear Industry, Ganzhou 341000, China"}]},{"given":"Tao","family":"Lang","sequence":"additional","affiliation":[{"name":"264 Geological Team of Jiangxi Nuclear Industry, Ganzhou 341000, China"}]},{"given":"Xiaoting","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"given":"Wenchao","family":"Huangfu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"given":"Penghui","family":"Ou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"given":"Lifeng","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"given":"Xiaolan","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"given":"Shanling","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]},{"given":"Chongjian","family":"Shao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"ref_1","unstructured":"China Geological Survey (2020, August 15). Notification on National Geological Hazard in 2019, Available online: http:\/\/www.cgs.gov.cn\/gzdt\/zsdw\/202003\/t20200331_504559.html."},{"key":"ref_2","first-page":"422","article-title":"Enhancing the performance of regional land cover mapping","volume":"52","author":"Wu","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.1002\/ldr.3148","article-title":"Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraq","volume":"29","author":"Wu","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105147","DOI":"10.1016\/j.enggeo.2019.105147","article-title":"GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China","volume":"259","author":"Zhao","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104580","DOI":"10.1016\/j.catena.2020.104580","article-title":"Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping","volume":"191","author":"Huang","year":"2020","journal-title":"Catena"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.catena.2015.05.019","article-title":"Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines","volume":"133","author":"Hong","year":"2015","journal-title":"Catena"},{"key":"ref_7","first-page":"1","article-title":"Application of artificial neural network model based on GIS in geological hazard zoning","volume":"1","author":"Tan","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104425","DOI":"10.1016\/j.catena.2019.104425","article-title":"A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China","volume":"188","author":"Wang","year":"2020","journal-title":"Catena"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104426","DOI":"10.1016\/j.catena.2019.104426","article-title":"Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment","volume":"188","author":"Bui","year":"2020","journal-title":"Catena"},{"key":"ref_10","first-page":"1","article-title":"Dynamic development of landslide susceptibility based on slope unit and deep neural networks","volume":"1","author":"Hua","year":"2020","journal-title":"Landslides"},{"key":"ref_11","first-page":"104470","article-title":"Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping","volume":"139","author":"Fang","year":"2020","journal-title":"Comput. Geoences"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104364","DOI":"10.1016\/j.catena.2019.104364","article-title":"Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping","volume":"187","author":"Reza","year":"2020","journal-title":"Catena"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xiong, K., Adhikari, B.R., Stamatopoulos, C.A., Zhan, Y., Wu, S., Dong, Z., and Di, B. (2020). Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China. Remote Sens., 12.","DOI":"10.3390\/rs12020295"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4725","DOI":"10.1007\/s12665-013-2863-4","article-title":"Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China","volume":"71","author":"Wu","year":"2014","journal-title":"Environ. Earth Ences"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1080\/19475705.2017.1289250","article-title":"GIS-based landslide susceptibility modelling: A comparative assessment of kernel logistic regression, Na\u00efve-Bayes tree, and alternating decision tree models","volume":"8","author":"Chen","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1007\/s10346-018-0966-4","article-title":"A review of the recent literature on rainfall thresholds for landslide occurrence","volume":"15","author":"Segoni","year":"2018","journal-title":"Landslides"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3724\/SP.J.1235.2011.00097","article-title":"Formation, distribution and risk control of landslides in China","volume":"3","author":"Huang","year":"2011","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"ref_18","unstructured":"264 Brigade of the Jiangxi Nuclear Industry Geological Bureau (2020, October 27). The Guixi Geological Hazard Survey Project Implemented by Our Team Successfully Passed the Field Acceptance by the Expert Group. Available online: http:\/\/www.hgy264.com\/show-27-6127-1.html."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1007\/s10346-015-0657-3","article-title":"Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: A case study in the upper Lo River catchment (Vietnam)","volume":"13","author":"Hung","year":"2016","journal-title":"Landslides"},{"key":"ref_21","first-page":"1","article-title":"On the red weathering crusts of southern China. Quaternary Sciences","volume":"1","author":"Xi","year":"1991","journal-title":"Quat. Sci. (Chin. Engl. Abstr.)"},{"key":"ref_22","first-page":"94","article-title":"Red clay and red weathered crust in southern China","volume":"4","author":"Zhu","year":"1995","journal-title":"Res. Soil Water Conserv. (Chin. Engl. Abstr.)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1002\/2015WR017758","article-title":"Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall-induced landslides","volume":"52","author":"Fan","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_24","first-page":"611","article-title":"Influence of soil properties on landslide occurrences in Bududa district, Eastern Uganda","volume":"4","author":"Kitutu","year":"2009","journal-title":"Afr. J. Agric. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"137231","DOI":"10.1016\/j.scitotenv.2020.137231","article-title":"Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble","volume":"718","author":"Hong","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1007\/s10346-020-01392-9","article-title":"Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability","volume":"17","author":"Napoli","year":"2020","journal-title":"Landslides"},{"key":"ref_27","unstructured":"Fadhil, A.M., and Negm, A. (2019). Using Radar and Optical Data for Soil Salinity Modeling and Mapping in Central Iraq. Environmental Remote Sensing in Iraq, Springer. Chapter 2."},{"key":"ref_28","first-page":"1025","article-title":"Image-Based Atmospheric Corrections-Revisited and Improved","volume":"62","author":"Chavez","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_29","unstructured":"Wu, W. (2003). Application de la Geomatique au Suivi de la Dynamique Environnementale en Zones Arides. [Ph.D. Thesis, Universit\u00e9 Paris 1]."},{"key":"ref_30","first-page":"81","article-title":"Use remote sensing to assess impacts of land management policies in the Ordos rangelands in China","volume":"6","author":"Wu","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/rs6021211","article-title":"The generalized difference vegetation index (GDVI) for dryland characterization","volume":"6","author":"Wu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104630","DOI":"10.1016\/j.catena.2020.104630","article-title":"The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models","volume":"193","author":"Bordoni","year":"2020","journal-title":"Catena"},{"key":"ref_33","first-page":"102093","article-title":"Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment, Central Java, Indonesia","volume":"89","author":"Chikalamo","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, X., Wu, W., Lin, Z., Zhang, G., Chen, R., Song, Y., Wang, Z., Lang, T., Qin, Y., and Ou, P. (2020). Landslide risk zoning in Ruijin, Jiangxi, China. Nat. Hazards Earth Syst. Sci. Discuss, in press.","DOI":"10.5194\/nhess-2020-270"},{"key":"ref_35","first-page":"61","article-title":"Analysis on the Influence of Reservoir Impoundment on the Bank Landslide","volume":"3","author":"Luo","year":"2003","journal-title":"Des. Hydroelectr. Power Stn. (Chin. Engl. Abstr.)"},{"key":"ref_36","first-page":"2722","article-title":"Study on influence of reservoir water impounding on reservoir landslide","volume":"12","author":"Wang","year":"2007","journal-title":"Rock Soil Mech. (Chin. Engl. Abstr.)"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.envsoft.2012.01.014","article-title":"imageRF\u2014A user-oriented implementation for remote sensing image analysis with Random Forests","volume":"35","author":"Waske","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_39","unstructured":"Jakimow, B., Oldenburg, C., and Rabe, A. (2012). Manual for Application: ImageRF (1.1), Universit\u00e4t Bonn and Humboldt Universit\u00e4t zu Berlin."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/11\/695\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:36:07Z","timestamp":1760178967000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/11\/695"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,23]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["ijgi9110695"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9110695","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,23]]}}}