Abstract
The rational selection of the proportion between geological disasters (positive samples) and non-geological disasters (negative samples) holds significant importance in enhancing the precision of geological disaster susceptibility assessment and maintaining the sustainable development of the ecological environment. This paper, using Liulin County as an example, employs correlation analysis to select appropriate evaluation factors. A Random Forest (RF) model, based on GIS technology, is used for susceptibility map**. Sample ratios of 1:1, 1:1.5, 1:3, 1:5, and 1:10 are applied. The results indicate that, through a confusion matrix test, the model’s predictive performance reaches a “tip** point” at a sample ratio of 1:5. The receiver operating characteristic (ROC) curve test shows that the 1:5 model performs best. Combining the proportion of susceptibility zones and disaster points, 1:5 is identified as the most suitable ratio for assessing geological disaster susceptibility in the study area. High and very high susceptibility zones are primarily concentrated in the central and northern regions alongside roads and rivers, making these areas key focuses for disaster prevention and reduction in Liulin County. The accuracy of the model’s predictions increases with a greater number of samples, but it does not continue to rise indefinitely; accuracy declines after a critical threshold is crossed. These research findings complement prior studies, promote advances in geological disaster prevention technology, and maintain geological environmental stability, all of which are crucial for the local economy’s stability and sustainable development.
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Data availability
The data that support the findings of this study are available from the corresponding author, Zepeng Wang, upon reasonable request.
References
Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part II. GIS-based susceptibility map** with comparisons of results from two methods and verifications. Eng Geol 81:432–445. https://doi.org/10.1016/j.enggeo.2005.08.004
Bragagnolo L, da Silva RV, Grzybowski JMV (2020) Landslide susceptibility map** with r.landslide: a free open-source GIS-integrated tool based on Artificial Neural Networks. Environ Modell Softw 123:104565. https://doi.org/10.1016/j.envsoft.2019.104565
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazard Earth Sys 13:2815–2831. https://doi.org/10.5194/nhess-13-2815-2013
Chen W, **e XS, Wang JL, Pradhan B, Hong HY, Bui DT, Duan Z, Ma JQ (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Chen W, Chen YZ, Tsangaratos P, Ilia I, Wang XJ (2020) Combining evolutionary algorithms and machine learning models in landslide susceptibility assessments. Remote Sens-Basel 12:3854. https://doi.org/10.3390/rs12233854
Chen M, Tang C, Li MW, **ong J, Luo YT, Shi QY, Zhang XZ, Tie Y, Feng Q (2022) Changes of surface recovery at coseismic landslides and their driving factors in the Wenchuan earthquake-affected area. Catena 210:105871. https://doi.org/10.1016/j.catena.2021.105871
Chen XY, Shi Y, Wen YX, Shi RY, Mi XM (2023) Landslide susceptibility evaluation based on certainty factor and support vector machines. Sci Techn Eng 23:518–527
Chen XY, Shi Y, Zhao K, Wen YX (2023) Landslide susceptibility evaluation based on CF integrated with SSA to optimize SVM and RF Models. J ** using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya. Environ Monit Assess 194:234. https://doi.org/10.1007/s10661-022-09851-7
Deng RJ, Fang ZB (2019) Yield analysis of financial products based on Spearman correlation analysis. Stat Decis 35:164–167. https://doi.org/10.13546/j.cnki.tjyjc.2019.16.036
Duan YY, Tang J, Liu YG, Gao XJ, Duan YX (2022) Spatial sensitivity evaluation of loess landslide in Liulin County, Shanxi Based on Random Forest. S Geog Sin 42:343–351. https://doi.org/10.13249/j.cnki.sgs.2022.02.016
Erener A, Duzgun HBS (2013) A regional scale quantitative risk assessment for landslides: case of Kumluca watershed in Bartin, Turkey. Landslides 10:55–73. https://doi.org/10.1007/s10346-012-0317-9
Gao H, Zhang X (2022) Landslide susceptibility assessment considering landslide volume: a case study of Yangou Watershed on the Loess Plateau (China). Appl Sci-Basel 12:4381. https://doi.org/10.3390/app12094381
Ge GBT, Shi ZJ, Zhu YJ, Yang XH, Hao YG (2020) Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: performance assessment of four machine learning algorithms. Glob Ecol Conserv 22:e00971. https://doi.org/10.1016/j.gecco.2020.e00971
Getachew N, Meten M (2021) Weights of evidence modeling for landslide susceptibility map** of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia. Geoenvironmental Dis 8:1–22. https://doi.org/10.1186/s40677-021-00177-z
Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184. https://doi.org/10.1016/j.geomorph.2006.04.007
He Q, Wang M, Liu K (2021) Rapidly assessing earthquake-induced landslide susceptibility on a global scale using random forest. Geomorphology 391:107889. https://doi.org/10.1016/j.geomorph.2021.107889
Heckmann T, Gegg K, Gegg A, Becht M (2014) Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Nat Hazard Earth Sys 14:259–278. https://doi.org/10.5194/nhess-14-259-2014
Hu Q, Zhou Y, Wang SX, Wang FT (2020) Machine learning and fractal theory models for landslide susceptibility map**: case study from the **sha River Basin. Geomorphology 351:106975. https://doi.org/10.1016/j.geomorph.2019.106975
Hussain MA, Chen ZL, Wang R, Shoaib M (2021) PS-InSAR-based validated landslide susceptibility map** along Karakorum Highway. Pakistan Remote Sens-Basel 13:4129. https://doi.org/10.3390/rs13204129
Kanwal S, Atif S, Shafiq M (2017) GIS based landslide susceptibility map** of northern areas of Pakistan, a case study of Shigar and Shyok Basins. Geomat Nat Haz Risk 8:348–366. https://doi.org/10.1080/19475705.2016.1220023
Li YY, Sheng YF, Chai B, Zhang W, Zhang TL, Wang JJ (2020) Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks. Geomat Nat Haz Risk 11:510–534. https://doi.org/10.1080/19475705.2020.1734101
Li LF (2022) Comprehensive evaluation and intelligent prediction of water resource carrying capacity—taking Zhengzhou city and Wuhan city as examples. Dissertation, North China University of Water Resources and Electric Power, Zhengzhou. https://doi.org/10.27144/d.cnki.ghbsc.2022.000363
Lin GW, Hung C, Chien YFC, Chu CR, Liu CH, Chang CH, Chen H (2020) Towards automatic landslide-quake identification using a Random Forest Classifier. Appl Sci-Basel 10:3670. https://doi.org/10.3390/app10113670
Lin JH, Chen WH, Qi XH, Hou HR (2021) Risk assessment and its influencing factors analysis of geological hazards in typical mountain environment. J Clean Prod 309:127077. https://doi.org/10.1016/j.jclepro.2021.127077
Liu J, Li SL, Chen T (2018) Landslide susceptibility assesment based on optimized Random Forest Model. Geomat Inf Sci Wuhan Univ 43:1085–1091. https://doi.org/10.13203/j.whugis20160515
Ma X, Wang NQ, Li XK, Yan D, Li JL (2022) Assessment of landslide susceptibility based on RF-FR Model: taking Lueyang County as an example. Northwest Geo 55:335–344. https://doi.org/10.19751/j.cnki.61-1149/p.2022.03.028
Mandal B, Mandal S (2018) Analytical hierarchy process (AHP) based landslide susceptibility map** of Lish river basin of eastern Darjeeling Himalaya, India. AdSpR 62:3114–3132. https://doi.org/10.1016/j.asr.2018.08.008
Meinhardt M, Fink M, Tunschel H (2015) Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80–97. https://doi.org/10.1016/j.geomorph.2014.12.042
Meng T, Xu XL, Liu HH (2021) Landslide risk assessment in high altitude areas based on slope unit optimization: taking the Baige landslide in **sha River as an example. J Henan Polytech Univ (Nat Sci) 40:65–73. https://doi.org/10.16186/j.cnki.1673-9787.2019100024
Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225
Ozdemir A (2020) A comparative study of the frequency ratio, analytical hierarchy process, artificial neural networks and fuzzy logic methods for landslide susceptibility map**: Taskent (Konya), Turkey. Geotech Geol Eng 38:4129–4157. https://doi.org/10.1007/s10706-020-01284-8
Panchal S, Shrivastava AK (2022) Landslide hazard assessment using analytic hierarchy process (AHP): a case study of National Highway 5 in India. Ain Shams Eng J 13:101626. https://doi.org/10.1016/j.asej.2021.10.021
Parzinger M, Hanfstaengl L, Sigg F, Spindler U, Wellisch U, Wirnsberger M (2022) Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection - using machine learning methods Random Forest and LASSO. Build Environ 223:109313. https://doi.org/10.1016/j.buildenv.2022.109313
Rabby YW, Ishtiaque A, Rahman MS (2020) Evaluating the effects of digital elevation models in landslide susceptibility map** in Rangamati District, Bangladesh. Remote Sens-Basel 12:2718. https://doi.org/10.3390/rs12172718
Sahin EK, Colkesen I, Kavzoglu T (2020) A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility map**. GeoIn 35:341–363. https://doi.org/10.1080/10106049.2018.1516248
Salcedo D, Almeida OP, Morales B, Toulkeridis T (2022) Smart City planning based on landslide susceptibility map** using fuzzy logic and multi-criteria evaluation techniques in the City of Quito, Ecuador. Doctoral Sym Inf Commun Technol- Dsict 846:89–103. https://doi.org/10.1007/978-3-030-93718-8_8
Shirzadi A, Solaimani K, Roshan MH, Kavian A, Chapi K, Shahabi H, Keesstra S, Bin Ahmad B, Bui DT (2019) Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution. CATENA 178:172–188. https://doi.org/10.1016/j.catena.2019.03.017
Shou KJ, Lin JF (2020) Evaluation of the extreme rainfall predictions and their impact on landslide susceptibility in a sub-catchment scale. Eng Geol 265:105434. https://doi.org/10.1016/j.enggeo.2019.105434
Stephanou M, Varughese M (2021) Sequential estimation of Spearman rank correlation using Hermite series estimators. J Multivariate Anal 186:104783. https://doi.org/10.1016/j.jmva.2021.104783
Sun DL, Xu JH, Wen HJ, Wang Y (2020) An optimized random forest model and its generalization ability in landslide susceptibility map**: application in two areas of Three Gorges Reservoir, China. J Earth Sci-China 31:1068–1086. https://doi.org/10.1007/s12583-020-1072-9
Sun DL, Xu JH, Wen HJ, Wang DZ (2021) Assessment of landslide susceptibility map** based on Bayesian hyperparameter optimization: a comparison between logistic regression and random forest. Eng Geol 281:105972. https://doi.org/10.1016/j.enggeo.2020.105972
Sun DL, Chen DL, Mi CL, Chen XY, Mi SW, Li XQ (2023) Evaluation of landslide susceptibility in the gentle hill-valley areas based on the interpretable random forest-recursive feature elimination model. J Geomech 29:202–219. https://doi.org/10.12090/j.issn.1006-6616.2022128
Tang RX, Yan EC, Wen T, Yin XM, Tang W (2021) Comparison of logistic regression, information value, and comprehensive evaluating model for landslide susceptibility map**. Sustainability-Basel 13:3803. https://doi.org/10.3390/su13073803
Vasu NN, Lee SR (2016) A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea. Geomorphology 263:50–70. https://doi.org/10.1016/j.geomorph.2016.03.023
Wang Y, Wu XL, Chen ZJ, Ren F, Feng LW, Du QY (2019) Optimizing the predictive ability of machine learning methods for landslide susceptibility map** using SMOTE for Lishui City in Zhejiang Province, China. Int J Env Res Pub He 16:368. https://doi.org/10.3390/ijerph16030368
Wang Y, Sun DL, Wen HJ, Zhang H, Zhang FT (2020) Comparison of random forest model and frequency ratio model for landslide susceptibility map** (LSM) in Yunyang County (Chongqing, China). Int J Env Res Pub He 17:4206. https://doi.org/10.3390/ijerph17124206
Wang XD, Zhang CB, Wang C, Liu GW, Wang HX (2021) GIS-based for prediction and prevention of environmental geological disaster susceptibility: from a perspective of sustainable development. Ecotox Environ Safe 226:112881. https://doi.org/10.1016/j.ecoenv.2021.112881
Wang W, Yuan WL, Zhou LF, Chen HJ, Cheng RS, Xu WY (2022) Comprehensive regional-scale early warning of water-induced landslides in reservoir areas based on landslide susceptibility assessment. Chinese J Rock Mech Eng 41:479–491. https://doi.org/10.13722/j.cnki.jrme.2021.0655
Wang CM (2019) Study on characteristics of Lv Liang Loess landslide and rainfall early warning model in Shanxi Province. Dissertation, Chengdu University of Technology, Chengdu. https://doi.org/10.26986/d.cnki.gcdlc.2019.000804
Wu XY, Song YB, Chen W, Kang GC, Qu R, Wang ZF, Wang JX, Lv PY, Chen H (2023) Analysis of geological hazard susceptibility of landslides in muli county based on random forest algorithm. Sustainability-Basel 15:4328. https://doi.org/10.3390/su15054328
Xue Q, Zhang MS, Gao B (2020) Hazard assessment of loess landslide based on soil moisture content and supported by slope unit in Qingjian City, Shaanxi Province. Geol China 47:1904–1914. https://kns.cnki.net/kcms/detail/11.1167.P.20200205.1442.002.html
Yan YQ, SYang ZH, Zhang XJ, Meng SW, Guo CB, Wu RA, Zhang YY (2021) Landslide susceptibility assessment based on weight-of-evidence modeling of the Batang Fault Zone, Eastern Tibetan Plateau. Geosci 35: 26–37. https://doi.org/10.19657/j.geoscience.1000-8527.2020.091
Yao WM, Li CD, Zuo QJ, Zhan HB, Criss RE (2019) Spatiotemporal deformation characteristics and triggering factors of Baijiabao landslide in Three Gorges Reservoir region, China. Geomorphology 343:34–47. https://doi.org/10.1016/j.geomorph.2019.06.024
Zhai YG (2010) Study of the impact of remote sensing image classification accuracy based on multi-scales. dissertation, Inner Mongolia Agricultural University, Huhehot. https://kns.cnki.net/kcms2/article/abstract?v=rNedIcCUbLBSVmdiYRcgGHOCUtpuHQ_aelvyikO9A7zBb7Y_aekwLoqP4ohlbwGDYE7qvzta4w0kP70Pb9KXOLrUvz1biCxnQTw2qvXcTJ_y6DvLiC6YYZ87HMZ5oNqX4U7mARh596eO9jbsh8M4pw==&uniplatform=NZKPT&language=CHS
Zhang YH, Ge TT, Tian W, Liou YA (2019) Debris flow susceptibility map** using machine-learning techniques in Shigatse Area, China. Remote Sens-Basel 11:2801. https://doi.org/10.3390/rs11232801
Zhao JQ, Zhang Q, Wang DZ, Wu WH, Yuan RY (2022) Machine learning-based evaluation of susceptibility to geological hazards in the Hengduan Mountains Region, China. Int J Disast Risk Sc 13:305–316. https://doi.org/10.1007/s13753-022-00401-w
Zhao ZA, Wang JZ, Mao X, Ma WJ, Lu WJ, He Y, Gao XY (2023) A multi-dimensional CNN coupled landslide susceptibility assessment method. Geomat Inf Sci Wuhan Univ pp 1–19. https://doi.org/10.13203/j.whugis20220325
Zhou XT, Wu WC, Lin ZY, Zhang GL, Chen RX, Song Y, Wang ZL, Lang T, Qin YZ, Ou PH, Wenchao HF, Zhang Y, **e LF, Huang XL, Fu X, Li J, Jiang JH, Zhang M, Liu YX, Peng SL, Shao CJ, Bai YH, Zhang XF, Liu XT, Liu WH (2021) Zonation of landslide susceptibility in Rui**, Jiangxi, China. Int J Env Res Pub He 18:5906. https://doi.org/10.3390/ijerph18115906
Zhou P, Deng H, Zhang WJ, Xue DJ, Wu XT, Zhuo WH (2022) Landslide susceptibility evaluation based on information value model and machine learning method: a case study of Lixian County, Sichuan Province. S Geog Sin 42:1665–1675. https://doi.org/10.13249/j.cnki.sgs.2022.09.016
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This work was supported by the National Natural Science Foundation of China (Grant number: 51604140).
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Conceptualization: Jian** Chen and Zepeng Wang; methodology: Jian** Chen: software; Zepeng Wang: validation; Wei Chen: formal analysis: Changyuan Wan and Yunyan Liu; investigation: Junjie Huang; resources: Jian** Chen; data curation: Zepeng Wang and Yunyan Liu: writing—original draft preparation: Jian** Chen and Zepeng Wang; writing—review and editing: Jian** Chen and Zepeng Wang; visualization: Zepeng Wang; supervision: Junjie Huang; project administration: Yunyan Liu; funding acquisition: Jian** Chen. All the authors have read and agreed to the published version of the manuscript.
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Wang, Z., Chen, J., Chen, W. et al. Susceptibility assessment of environmental geological disasters in Liulin County based on RF: from the perspective of positive and negative sample proportion. Environ Sci Pollut Res 30, 122245–122261 (2023). https://doi.org/10.1007/s11356-023-30778-0
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DOI: https://doi.org/10.1007/s11356-023-30778-0