{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:36:48Z","timestamp":1760060208710,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"key project of sustainable development international cooperation program by NSFC","award":["42361144883","CBASYX0906","42271422"],"award-info":[{"award-number":["42361144883","CBASYX0906","42271422"]}]},{"name":"International Research Centre of Big Data for Sustainable Development Goals (CBAS)","award":["42361144883","CBASYX0906","42271422"],"award-info":[{"award-number":["42361144883","CBASYX0906","42271422"]}]},{"name":"National Natural Science Foundation of China","award":["42361144883","CBASYX0906","42271422"],"award-info":[{"award-number":["42361144883","CBASYX0906","42271422"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In landslide susceptibility evaluation, scientific sampling minimizes potential societal losses and enhances the efficiency of disaster prevention and mitigation. However, traditional sampling methods, such as selecting landslide and non-landslide samples based on equal proportions or area proportions, overlook the different societal losses resulting from landslide omission and misreporting, and the potential societal losses faced by their evaluation results are often not minimized. Therefore, this study proposes a sampling method that takes potential societal losses into account and uses the Landslide Misjudgment Potential Societal Loss Evaluation Index (LMPSLEI) to quantify the total potential social losses in the area due to landslide omission and misreporting. The LMPSLEI is minimized by optimizing the sample ratio, thus minimizing the potential societal losses faced by the evaluation results and enhancing the scientific basis of disaster prevention and mitigation efforts. This study takes the Wenchuan earthquake area as the research region, selects 13 conditional factors and employs two models\u2014Random Forest (RF) and Convolutional Neural Network (CNN)\u2014to conduct case studies. We derive the recommended sample ratio based on the formula, hypothesizing that the LMPSLEI will be minimized under this ratio. The results show that the sample ratio for LMPSLEI minimization in the RF model is similar to the recommended sample ratio, while the sample ratio for LMPSLEI minimization in the CNN model is slightly higher than the recommended sample ratio. The recommended sample ratio can achieve the minimum of LMPSLEI or reach a lower value under different societal losses weights of landslide omission\/misreporting, and thus it can be used as a preliminary choice of sampling for landslide susceptibility evaluation considering the potential societal losses.<\/jats:p>","DOI":"10.3390\/ijgi14080309","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T07:45:31Z","timestamp":1755071131000},"page":"309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["New Sampling Method for Landslide Susceptibility Evaluation with Consideration of Minimizing Potential Societal Losses"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhao","family":"Lu","sequence":"first","affiliation":[{"name":"School of Earth and Environmental Sciences, Yunnan Land and Resources Vocational College, Kunming 652501, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9095-243X","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongming","family":"Wei","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianfeng","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Earth and Environmental Sciences, Yunnan Land and Resources Vocational College, Kunming 652501, China"},{"name":"Engineering Center of Yunnan Education Department for Health Geological Survey & Evaluation, Kunming 650218, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10037-020-00143-6","article-title":"Economic landslide susceptibility under a socio-economic perspective: An application to Umbria Region (Central Italy)","volume":"40","author":"Donnini","year":"2020","journal-title":"Rev. Reg. Res."},{"key":"ref_2","first-page":"1657","article-title":"Application and Development Trend of Machine Learning in Landslide Intelligent Disaster Prevention and Mitigation","volume":"48","author":"Dou","year":"2023","journal-title":"Earth Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.catena.2018.03.003","article-title":"Review on landslide susceptibility mapping using support vector machines","volume":"165","author":"Yu","year":"2018","journal-title":"Catena."},{"key":"ref_4","first-page":"3978","article-title":"GIS-based landslide susceptibility assessment using analytical hierarchy process in wenchuan earthquake region","volume":"28","author":"Xu","year":"2009","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1007\/s00254-007-0882-8","article-title":"Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models","volume":"54","author":"Akgun","year":"2008","journal-title":"Environ. Geol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103225","DOI":"10.1016\/j.earscirev.2020.103225","article-title":"Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance","volume":"207","author":"Abdelaziz","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2009.09.025","article-title":"GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China","volume":"115","author":"Shi","year":"2010","journal-title":"Geomorphology."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Tien","year":"2016","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.catena.2018.01.012","article-title":"GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method","volume":"164","author":"Chen","year":"2018","journal-title":"Catena"},{"key":"ref_10","first-page":"280","article-title":"Landslide Susceptibility Evaluation Based on Coupled X-Multilayer Perceptron Model\u2014A Case Study of Songtao Autonomous County of Guizhou Province, China","volume":"41","author":"Zeng","year":"2023","journal-title":"Mt. Res."},{"key":"ref_11","unstructured":"Li, Z.Q. (2020). Evaluation of Landslide Susceptibility Based on BP Artificial Neural Network and GIS in Dingcheng District, Hunan Province. [Master\u2032s Thesis, Hebei GEO University]."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ado, M., Amitab, K., Maji, A.K., Jasi\u0144ska, E., Gono, R., Leonowicz, Z., and Jasi\u0144ski, M. (2022). Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey. Remote Sens., 14.","DOI":"10.3390\/rs14133029"},{"key":"ref_13","first-page":"1665","article-title":"Landslide susceptibility evaluation based on Information Value model and Machine Learning method: A case study of Lixian County, Sichuan Province","volume":"42","author":"Zhou","year":"2022","journal-title":"Sci. Geogr. Sin."},{"key":"ref_14","first-page":"79","article-title":"Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models","volume":"41","author":"Huang","year":"2022","journal-title":"Bull. Geol. Sci. Technol."},{"key":"ref_15","first-page":"47","article-title":"Evaluation of the Susceptibility of Earthquake Landslides Based on Different Machine Learning Algorithms\u2014Taking Ludian Earthquake as an Example","volume":"47","author":"Jiri","year":"2022","journal-title":"J. Kunming Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yao, J., Qin, S., Qiao, S., Che, W., Chen, Y., Su, G., and Miao, Q. (2020). Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China. Appl. Sci., 10.","DOI":"10.3390\/app10165640"},{"key":"ref_17","first-page":"2244","article-title":"Landslide Susceptibility Analysis based on Deep Learning","volume":"23","author":"Wang","year":"2021","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_18","first-page":"2244","article-title":"Landslide susceptibility evaluation based on deep learning along kangding-litang section of cz railway","volume":"23","author":"Wang","year":"2022","journal-title":"J. Eng. Geol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111014","DOI":"10.1016\/j.jenvman.2020.111014","article-title":"A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment","volume":"271","author":"Tang","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Oh, H.J., Lee, S., and Hong, S.M. (2017). Landslide Susceptibility Assessment Using Frequency Ratio Technique with Iterative Random Sampling. J. Sens., 3730913.","DOI":"10.1155\/2017\/3730913"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112948","DOI":"10.1016\/j.ecolind.2024.112948","article-title":"Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods","volume":"169","author":"Wang","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2392778","DOI":"10.1080\/19475705.2024.2392778","article-title":"Considering the effect of non-landslide sample selection on landslide susceptibility assessment","volume":"15","author":"Zhu","year":"2024","journal-title":"Geomat. Nat. Hazards Risk."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2327463","DOI":"10.1080\/10106049.2024.2327463","article-title":"Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique","volume":"39","author":"Zhou","year":"2024","journal-title":"Geocarto Int."},{"key":"ref_24","first-page":"1492","article-title":"Evaluation of landslide susceptibility based on sample optimization strategy","volume":"49","author":"Wu","year":"2024","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.cageo.2012.01.002","article-title":"Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China","volume":"46","author":"Xu","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.enggeo.2014.02.002","article-title":"Development of a globally applicable model for near real-time prediction of seismically induced landslides","volume":"173","author":"Nowicki","year":"2014","journal-title":"Eng. Geol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107222","DOI":"10.1016\/j.geomorph.2020.107222","article-title":"Effects of sampling intensity and non-slide\/slide sample ratio on the occurrence probability of coseismic landslides","volume":"363","author":"Shao","year":"2020","journal-title":"Geomorphology"},{"key":"ref_28","first-page":"1122","article-title":"Probability of coseismic landslides: A new generation of earthquake-triggered landslide hazard model","volume":"27","author":"Xu","year":"2019","journal-title":"J. Eng. Geol."},{"key":"ref_29","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":"Pourghasemi","year":"2020","journal-title":"Catena"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1007\/s12583-020-1072-9","article-title":"An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China","volume":"31","author":"Sun","year":"2020","journal-title":"J. Earth Sci."},{"key":"ref_31","first-page":"20","article-title":"Landslide susceptibility evaluation considering sample sensitivity","volume":"11","author":"Lv","year":"2022","journal-title":"Bull. Surv. Map."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.gr.2022.05.012","article-title":"Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples","volume":"123","author":"Yang","year":"2023","journal-title":"Gondwana Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.enggeo.2009.12.004","article-title":"Techniques for evaluating the performance of landslide susceptibility models","volume":"111","author":"Frattini","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_34","first-page":"1156","article-title":"Contribution of strata lithology and slope gradient to landslides triggered by Wenchuan Ms 8 earthquake, Sichuan, China","volume":"28","author":"Yao","year":"2009","journal-title":"Geol. Bull. China"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wei, Y.M., Wang, Q.J., Chen, F., Lu, C.Y., and Lei, S.H. (2020). Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach. Remote Sens., 12.","DOI":"10.3390\/rs12172767"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100037","DOI":"10.1016\/j.pdisas.2019.100037","article-title":"Assessment of landslide susceptibility along the Araniko Highway in Poiqu\/Bhote Koshi\/Sun Koshi Watershed, Nepal Himalaya","volume":"3","author":"Nepal","year":"2019","journal-title":"Prog. Disaster Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xiao, L.M., Zhang, Y.H., and Peng, G.Z. (2018). Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway. Sensors, 18.","DOI":"10.3390\/s18124436"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1038\/ngeo940","article-title":"An integrated perspective of the continuum between earthquakes and slow-slip phenomena","volume":"3","author":"Peng","year":"2010","journal-title":"Nat. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10346-013-0391-7","article-title":"Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression","volume":"11","author":"Kavzoglu","year":"2014","journal-title":"Landslides"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Theobald, D.M., Harrison Atlas, D., Monahan, W.B., and Albano, C.M. (2015). Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0143619"},{"key":"ref_41","first-page":"11","article-title":"Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan","volume":"22","author":"Khan","year":"2019","journal-title":"Egypt J. Remote Sens. Space Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.geomorph.2008.03.003","article-title":"GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region","volume":"101","author":"Kamp","year":"2008","journal-title":"Geomorphology"},{"key":"ref_43","first-page":"111","article-title":"The preliminary study on rapid assessment method of seismic landslide hazard based on GIS platform","volume":"36","author":"Li","year":"2016","journal-title":"Earthq. Eng. Eng. Vib."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geomorph.2007.06.001","article-title":"Susceptibility Assessment of Earthquake-Triggered Landslides in El Salvador Using Logistic Regression","volume":"95","author":"Malpica","year":"2008","journal-title":"Geomorphology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1785\/gssrl.79.1.103","article-title":"The USGS Earthquake Notification Service (ENS): Customizable notifications of earthquakes around the globe","volume":"79","author":"Wald","year":"2008","journal-title":"Seism. Res. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104833","DOI":"10.1016\/j.catena.2020.104833","article-title":"GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods","volume":"196","author":"Chen","year":"2021","journal-title":"Catena"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/j.1600-0587.2012.07348.x","article-title":"Collinearity: A review of methods to deal with it and a simulation study evaluating their performance","volume":"36","author":"Dormann","year":"2013","journal-title":"Ecography"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/A:1018054314350","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_49","first-page":"157","article-title":"Random Forests","volume":"45","author":"Cutler","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s00521-016-2700-2","article-title":"Design of Memristor-Based Image Convolution Calculation in Convolutional Neural Network","volume":"30","author":"Zeng","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_51","first-page":"98","article-title":"Application Research of Improved XGBoost in Imbalanced Data Processing","volume":"47","author":"Song","year":"2020","journal-title":"Comput. Sci."},{"key":"ref_52","first-page":"86","article-title":"Fuzzy comprehensive evaluation of the vulnerability of geological disasters in min county","volume":"19","author":"Wu","year":"2021","journal-title":"Geospat. Inf."},{"key":"ref_53","first-page":"14","article-title":"Risk assessment of landslide geological hazards based on information method model and gis","volume":"32","author":"Wang","year":"2021","journal-title":"J. Geol. Hazards Environ. Preserv."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/8\/309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:26:03Z","timestamp":1760034363000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/8\/309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,13]]},"references-count":53,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["ijgi14080309"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14080309","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2025,8,13]]}}}