Landslide dynamic hazard prediction based on precipitation variation trend and backpropagation neural network

References

• Achour Y, Saidani Z, Touati R, Pham QB, Pal SC, Mustafa F, Sanli FB. 2021. Assessing landslide susceptibility using a machine learning-based approach to achieving land degradation neutrality. Environ Earth Sci. 80(17):575–594. doi:10.1007/s12665-021-09889-9.
   Web of Science ®Google Scholar
• 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. doi:10.1016/j.geomorph.2018.06.006.
   Web of Science ®Google Scholar
• Afungang RN, De Meneses Bateira CV, Nkwemoh CA. 2017. Assessing the spatial probability of landslides using GIS and informative value model in the Bamenda highlands. Arab J Geosci. 10(17):384–398. doi:10.1007/s12517-017-3155-1.
   Web of Science ®Google Scholar
• Arabameri A, Pourghasemi HR, Yamani M. 2017. Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ Earth Sci. 76(24):832–851. doi:10.1007/s12665-017-7177-5.
   Web of Science ®Google Scholar
• Arabameri A, Pradhan B, Rezaei K, Sohrabi M, Kalantari Z. 2019. GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mt Sci. 16(3):595–618. doi:10.1007/s11629-018-5168-y.
   Web of Science ®Google Scholar
• Ba Q, Chen Y, Deng S, Yang J, Li H. 2018. A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci Inform. 11(3):373–388. doi:10.1007/s12145-018-0335-9.
   Web of Science ®Google Scholar
• Baeza C, Corominas J. 2001. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Processes Landf. 26(12):1251–1263. doi:10.1002/esp.263.
   Web of Science ®Google Scholar
• Basharat MU, Khan JA, Abdo HG, Almohamad, H. 2023. An integrated approach based landslide susceptibility mapping: case of Muzaffarabad region, Pakistan. Geomatics Nat Hazards Risk. 14(1): 2210255. doi:10.1080/19475705.2023.2210255.
   PubMed Web of Science ®Google Scholar
• Carrara A, Cardinali M, Guzzetti F. 1995. GIS technology in mapping landslide hazard. Geogr Inf Syst Assess Nat Hazards. 5:135–175. doi:10.1007/978-94-015-8404-3_8.
   Google Scholar
• Chang Z, Catani F, Huang F, Liu G, Meena SR, Huang J, Zhou C. 2023. Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors. J Rock Mech Geotech Eng. 15(5):1127–1143. doi:10.1016/j.jrmge.2022.07.009.
   Web of Science ®Google Scholar
• Chawla A, Pasupuleti S, Chawla S, Rao ACS, Sarkar K, Dwivedi R. 2019. Landslide susceptibility zonation mapping: a case study from Darjeeling District, Eastern Himalayas, India. J Indian Soc Remote Sens. 47(3):497–511.), doi:10.1007/s12524-018-0916-6.
   Web of Science ®Google Scholar
• Chen F, Cai C, Li X, Sun T, Qian Q. 2020. Evaluation of landslide susceptibility based on information volume and neural network model. Chin J Rock Mech Eng. 39(S1):2859–2870. doi:10.13722/j.cnki.jrme.2019.1094.
   Google Scholar
• Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu A, Pei X, Duan Z. 2018. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ. 626:1121–1135. doi:10.1016/j.scitotenv.2018.01.124.
   PubMed Web of Science ®Google Scholar
• Das J, Saha P, Mitra R, Alam A, Kamruzzaman M. 2023. GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India. Heliyon. 9(5):e16186. doi:10.1016/j.heliyon.2023.e16186.
   PubMed Web of Science ®Google Scholar
• Farooq S, Akram MS. 2021. Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas. Arab J Geosci. 14(10):824–839. doi:10.1007/s12517-021-07147-7.
   Google Scholar
• Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ. 2008. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol. 102(3–4):85–98. doi:10.1016/j.enggeo.2008.03.022.
   Web of Science ®Google Scholar
• Froude MJ, Petley DN. 2018. Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci. 18(8):2161–2181. doi:10.5194/nhess-18-2161-2018.
   Web of Science ®Google Scholar
• Gokceoglu MEC. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol. 41(6):720–730. doi:10.1007/s00254-001-0454-2.
   Google Scholar
• Griffiths JS, Mather AE, Hart AB. 2002. Landslide susceptibility in the Río Aguas catchment, SE Spain. QJEGH. 35(1):9–17. doi:10.1144/qjegh.35.1.9.
   Google Scholar
• Guzzetti F, Carrara A, Cardinali M, Reichenbach P. 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology. 31(1-4):181–216. doi:10.1016/S0169-555X(99)00078-1.
   Web of Science ®Google Scholar
• Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F. 2005. Probabilistic landslide hazard assessment at the basin scale. Geomorphology. 72(1-4):272–299. doi:10.1016/j.geomorph.2005.06.002.
   Web of Science ®Google Scholar
• Hader PRP, Reis FAGV, Peixoto ASP. 2022. Landslide risk assessment considering socionatural factors: methodology and application to Cubatão municipality, São Paulo, Brazil. Nat Hazards. 110(2):1273–1304. doi:10.1007/s11069-021-04991-4.
   Web of Science ®Google Scholar
• Huang F, Cao Z, Guo J, Jiang S, Li S, Guo Z. 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena. 191:104580. doi:10.1016/j.catena.2020.104580.
   Web of Science ®Google Scholar
• Huang F, Cao Z, Jiang S, Zhou C, Huang J, Guo Z. 2020. Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model. Landslides. 17(12):2919–2930. doi:10.1007/s10346-020-01473-9.
   Web of Science ®Google Scholar
• Huang F, Chen J, Liu W, Huang J, Hong H, Chen W. 2022. Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold. Geomorphology. 408:108236. doi:10.1016/j.geomorph.2022.108236.
   Web of Science ®Google Scholar
• Huang L, Hao J, Li W, Zhou Z, Jia P. 2021. Landslide susceptibility assessment by the coupling method of RBF neural network and information value: a case study in Min Xian, Gansu Province. Chin J Geol Hazard Control. 32(6):116–126. doi:10.16031/j.cnki.issn.1003-8035.2021.06-14.
   Google Scholar
• Huang F, Pan L, Yao C, Zhou C, Jiang Q, Chang Z. 2021. Landslide susceptibility prediction modelling based on semi-supervised machine learning. J. Zhejiang Univ. (Eng. Sci.). 55(9):1705–1713. doi:10.3785/j.issn.1008-973X.2021.09.012.
   Google Scholar
• Huang Y, Zhao L. 2018. Review on landslide susceptibility mapping using support vector machines. Catena. 165:520–529. doi:10.1016/j.catena.2018.03.003.
   Web of Science ®Google Scholar
• Ji J, Zhou Y, Cheng Q, Jiang S, Liu S. 2023. Landslide susceptibility mapping based on deep learning algorithms using information value analysis optimization. Land. 12(6):1125. doi:10.3390/land12061125.
   Web of Science ®Google Scholar
• Jiménez-Perálvarez JD, El Hamdouni R, Palenzuela JA, Irigaray C, Chacón J. 2017. Landslide-hazard mapping through multi-technique activity assessment: an example from the Betic Cordillera (southern Spain). Landslides. 14(6):1975–1991. doi:10.1007/s10346-017-0851-6.
   Web of Science ®Google Scholar
• Jun Z, Kunlong Y, Jiajia W, Lei L, Faming H. 2016. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir [Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir]. Chinese Journal of Rock Mechanics and Engineering. 35(2):284–296. doi:10.13722/j.cnki.jrme.2015.0318.
   Google Scholar
• Kai W, Shaojie Z, Fangqiang W. 2020. Slope unit extraction methods: advances and prospects. J Yangtze River Sci Res Inst. 37(6):85–93. doi:10.11988/ckyyb.20190210.
   Google Scholar
• Karakas G, Kocaman S, Gokceoglu C. 2023. A hybrid multi-hazard susceptibility assessment model for a basin in Elazig Province, Türkiye. Int J Disaster Risk Sci. 14(2):326–341. doi:10.1007/s13753-023-00477-y.
   Web of Science ®Google Scholar
• Kawagoe S, Kazama S, Sarukkalige PR. 2010. Probabilistic modelling of rainfall induced landslide hazard assessment. Hydrol Earth Syst Sci. 14(6):1047–1061. doi:10.5194/hess-14-1047-2010.
   Web of Science ®Google Scholar
• Khan H, Shafique M, Khan MA, Bacha MA, Shah SU, Calligaris C. 2019. Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan. Egypt J Remote Sens Space Sci. 22(1):11–24. doi:10.1016/j.ejrs.2018.03.004.
   Web of Science ®Google Scholar
• Kim J, Lee S, Jung H, Lee S. 2018. Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto Int. 33(9):1000–1015. doi:10.1080/10106049.2017.1323964.
   Web of Science ®Google Scholar
• Lee S, Hong S, Jung H. 2017. A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability. 9(1):48. doi:10.3390/su9010048.
   Web of Science ®Google Scholar
• Lee S, Lee M, Jung H. 2017. Data mining approaches for landslide susceptibility mapping in Umyeonsan, Seoul, South Korea. Appl Sci-Basel. 7(7):683. doi:10.3390/app7070683.
   Web of Science ®Google Scholar
• Li Y, Liu X, Han Z, Dou J. 2020. Spatial proximity-based geographically weighted regression model for landslide susceptibility assessment: A case study of Qingchuan Area, China. Appl Sci. 10(3):1107. doi:10.3390/app10031107.
   Google Scholar
• Li D, Miao F, Xie Y, Leo C. 2019. Hazard prediction for Baishuihe landslide in the three Gorges reservoir during the extreme rainfall return period. KSCE J Civ Eng. 23(12):5021–5031. doi:10.1007/s12205-019-1025-y.
   Web of Science ®Google Scholar
• Lin J, Chen W, Qi X, Hou H. 2021. Risk assessment and its influencing factors analysis of geological hazards in typical mountain environment. J Cleaner Prod. 309:127077. doi:10.1016/j.jclepro.2021.127077.
   Web of Science ®Google Scholar
• Liu JL, Chen X, Zhang TJ. 2011. Application of time series-exponential smoothing model on urban water demand forecasting. AMR. 183–185:1158–1162. doi:10.4028/www.scientific.net/AMR.183-185.1158.
   Google Scholar
• Liu Z, Sun L, Zhang Y, Yu Z. 2022. Landslide risk evaluation based on slope unit:a case on the Western Hubei area, China. Arab J Geosci. 15(11):1072–1084. doi:10.1007/s12517-022-10319-8.
   Google Scholar
• Li X, Yang S, Li Y, Yin K, Wang W. 2023. Improved slope unit method for fine evaluation of regional landslide susceptibility. Bull Geol Sci Technol. 42(03):81–92. doi:10.19509/j.cnki.dzkq.tb20210707.
   Google Scholar
• Mehrabi M. 2022. Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy. Nat Hazards. 111(1):901–937. doi:10.1007/s11069-021-05083-z.
   Web of Science ®Google Scholar
• Niu HT. 2020. Smart safety early warning model of landslide geological hazard based on BP neural network. Saf Sci. 123:104572. doi:10.1016/j.ssci.2019.104572.
   Web of Science ®Google Scholar
• Niu H, Shao S, Gao J, Jing H. 2024. Research on GIS-based information value model for landslide geological hazards prediction in soil-rock contact zone in southern Shaanxi. Phys Chem Earth. 133:103515. doi:10.1016/j.pce.2023.103515.
   Google Scholar
• Pan W, Fu L, Xiao H, Yu X, Li X, Zhang X, Zhao T. 2021. Risk assessment for landslide of FAST site based on GIS and fuzzy hierarchical method. Environ Earth Sci. 80(8):320–334. doi:10.1007/s12665-021-09571-0.
   Web of Science ®Google Scholar
• Pardeshi SD, Autade SE, Pardeshi SS. 2013. Landslide hazard assessment: recent trends and techniques. Springerplus. 2(1):11. doi:10.1186/2193-1801-2-523.
   PubMedGoogle Scholar
• Peruccacci S, Gariano SL, Melillo M, Solimano M, Guzzetti F, Brunetti MT. 2023. The ITAlian rainfall-induced LandslIdes CAtalogue, an extensive and accurate spatio-temporal catalogue of rainfall-induced landslides in Italy. Earth Syst Sci Data. 15(7):2863–2877. doi:10.5194/essd-15-2863-2023.
   Web of Science ®Google Scholar
• Petley D. 2012. Global patterns of loss of life from landslides. Geology. 40(10):927–930. doi:10.1130/G33217.1.
   Web of Science ®Google Scholar
• Pham BT, Nguyen MD, Bui KT, Prakash I, Chapi K, Bui DT. 2019. A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena. 173:302–311. doi:10.1016/j.catena.2018.10.004.
   Web of Science ®Google Scholar
• Qasimi AB, Isazade V, Enayat E, Nadry Z, Majidi AH. 2023. Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms. Geocarto International. 38(1):2248082. doi:10.1080/10106049.2023.2248082.
   Web of Science ®Google Scholar
• Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F. 2018. A review of statistically-based landslide susceptibility models. Earth Sci Rev. 180:60–91. doi:10.1016/j.earscirev.2018.03.001.
   Web of Science ®Google Scholar
• Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating error. Nature. 323(6088):533–536. doi:10.1038/323533a0.
   Web of Science ®Google Scholar
• Shahzad N, Ding X, Abbas S. 2022. A comparative assessment of machine learning models for landslide susceptibility mapping in the rugged terrain of Northern Pakistan. Appl Sci -Basel. 12(5):2280. doi:10.3390/app12052280.
   Web of Science ®Google Scholar
• Sheng J, Yu P, Zhang H, Wang Z. 2021. Spatial variability of soil Cd content based on IDW and RBF in Fujiang River, Mianyang, China. J Soils Sediments. 21(1):419–429. doi:10.1007/s11368-020-02758-1.
   Web of Science ®Google Scholar
• Singh K, Kumar V. 2018. Hazard assessment of landslide disaster using information value method and analytical hierarchy process in highly tectonic Chamba region in bosom of Himalaya. J Mt Sci. 15(4):808–824. doi:10.1007/s11629-017-4634-2.
   Web of Science ®Google Scholar
• Skrzypczak I, Kokoszka W, Zientek D, Tang Y, Kogut J. 2021. Landslide hazard assessment map as an element supporting spatial planning: the Flysch Carpathians region study. Remote Sens. 13(2):317. doi:10.3390/rs13020317.
   Google Scholar
• Sui H, Su T, Hu R, Wang D, Zheng Z. 2022. Study on the risk assessment method of rainfall landslide. Water. 14(22):3678. doi:10.3390/w14223678.
   Web of Science ®Google Scholar
• Sun X, Chen J, Han X, Bao Y, Zhan J, Peng W. 2020. Application of a GIS-based slope unit method for landslide susceptibility mapping along the rapidly uplifting section of the upper Jinsha River, South-Western China. Bull Eng Geol Environ. 79(1):533–549. doi:10.1007/s10064-019-01572-5.
   Web of Science ®Google Scholar
• Sun D, Gu Q, Wen H, Xu J, Zhang Y, Shi S, Xue M, Zhou X. 2023. Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization. Gondwana Res. 123:89–106. doi:10.1016/j.gr.2022.07.013.
   Web of Science ®Google Scholar
• Tan Q, Bai M, Zhou P, Hu J, Qin X. 2021. Geological hazard risk assessment of line landslide based on remotely sensed data and GIS. Measurement. 169:108370. doi:10.1016/j.measurement.2020.108370.
   Web of Science ®Google Scholar
• Tang Y, Yin K, Liu L, Zhang L, Fu X. 2017. Dynamic assessment of rainfall-induced shallow landslide hazard. J Mt Sci. 14(7):1292–1302. doi:10.1007/s11629-016-4353-0.
   Web of Science ®Google Scholar
• Tsunetaka H. 2021. Comparison of the return period for landslide-triggering rainfall events in Japan based on standardization of the rainfall period. Earth Surf Processes Landf. 46(14):2984–2998. doi:10.1002/esp.5228.
   Web of Science ®Google Scholar
• Tyagi A, Kamal Tiwari R, James N. 2022. A review on spatial, temporal and magnitude prediction of landslide hazard. J Asian Earth Sci: x. 7:100099. doi:10.1016/j.jaesx.2022.100099.
   Web of Science ®Google Scholar
• Versain LD, Banshtu RS, Pandey DD. 2019. Comparative evaluation of GIS based landslide hazard zonation maps using different approaches. J Geol Soc India. 93(6):684–692. doi:10.1007/s12594-019-1247-0.
   Web of Science ®Google Scholar
• Wang T, Dahal A, Fang Z, van Westen C, Yin K, Lombardo L. 2024. From spatio-temporal landslide susceptibility to landslide risk forecast. Geosci Front. 15(2):101765. doi:10.1016/j.gsf.2023.101765.
   Web of Science ®Google Scholar
• Wang H, Ji F, Zhan X, Tan C, Feng C. 2022. Sensitivity evaluation of landslide geological hazards based on multi-source remote sensing data. Optik (Stuttgart):170481. doi:10.1016/j.ijleo.2022.170481.
   Google Scholar
• Wang M, Liu M, Yang S, Shi P. 2014. Incorporating triggering and environmental factors in the analysis of earthquake-induced landslide hazards. Int J Disaster Risk Sci. 5(2):125–135. doi:10.1007/s13753-014-0020-7.
   Web of Science ®Google Scholar
• Wang F, Xu P, Wang C, Wang N, Jiang N. 2017. Application of a GIS-based slope unit method for landslide susceptibility mapping along the Longzi River, Southeastern Tibetan Plateau, China. IJGI. 6(6):172. doi:10.3390/ijgi6060172.
   Web of Science ®Google Scholar
• Wubalem A. 2020. Modeling of landslide susceptibility in a part of Abay Basin, northwestern Ethiopia. Open Geosciences. 12(1):1440–1467. doi:10.1515/geo-2020-0206.
   Web of Science ®Google Scholar
• Wubalem A, Meten M. 2020. Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci. 2(5):807. doi:10.1007/s42452-020-2563-0.
   Web of Science ®Google Scholar
• Xu X. 2017. The spatial interpolation data set of the annual state of meteorological elements in China: resource and Environment Science and Data Registration and publishing system.
   Google Scholar
• Xu K, Guo Q, Li Z, Xiao J, Qin Y, Chen D, Kong C. 2015. Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area. Int J Geogr Inf Sci. 29(7):1111–1124. doi:10.1080/13658816.2014.992436.
   Web of Science ®Google Scholar
• Xu R, Li X, Hu K, Nie Y. 2019. A dynamic hazard assessment for mountain hazards in Hengduan mountain area. J Catastrophol. 34(03):196–201. doi:10.3969/j.issn.1000-811X.2019.03.036.
   Google Scholar
• Yan G, Cheng H, Jiang Z, Teng L, Tang M, Shi T, Jiang Y, Yang G, Zhou Q. 2022. Recognition of Fluvial Bank erosion along the main stream of the Yangtze River. Engineering. 19:50–61. doi:10.1016/j.eng.2021.03.027.
   Web of Science ®Google Scholar
• Yang S. 2017. Assessment of rainfall-induced landslide susceptibility using GIS-based slope unit approach. J Perform Constr Facil. 31(4):04017026. doi:10.1061/(ASCE)CF.1943-5509.0000997.
   Web of Science ®Google Scholar
• Yang H, Dong J, Guo X. 2023. Geohazards and risk assessment along highway in Sichuan Province, China. J Mt Sci. 20(6):1695–1711. doi:10.1007/s11629-022-7500-9.
   Web of Science ®Google Scholar
• Yu C, Chen J. 2020. Application of a GIS-based slope unit method for landslide susceptibility mapping in Helong City: comparative assessment of ICM, AHP, and RF model. Symmetry-Cult Sci. 12(11):1848. doi:10.3390/sym12111848.
   Google Scholar
• Yu W, Li X, Yao J, Zheng L. 2022. Dynamic evaluation of the risk of landslide disasters in the China⁃Pakistan economic corridor under the background of climate change. Cience Technol Eng. 22(35):15518–15527.
   Google Scholar
• Zangmene FL, Ngapna MN, Ateba MCB, Mboudou GMM, Defo PLW, Kouo RT, Dongmo AK, Owona S. 2023. Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Bafoussam-Dschang region (West Cameroon). Adv Space Res. 71(12):5282–5301. doi:10.1016/j.asr.2023.02.014.
   Web of Science ®Google Scholar
• Zeng T, Gong Q, Wu L, Zhu Y, Yin K, Dario P. 2023. Double-index rainfall warning and probabilistic physically based model for fast-moving landslide hazard analysis in subtropical-typhoon area. Landslides. doi:10.1007/s10346-023-02187-4.
   Web of Science ®Google Scholar
• Zhang S, Li C, Peng J, Zhou Y, Wang S, Chen Y, Tang Y. 2023. Fatal landslides in China from 1940 to 2020: occurrences and vulnerabilities. Landslides. 20(6):1243–1264. doi:10.1007/s10346-023-02034-6.
   Web of Science ®Google Scholar
• Zhang Y, Tang J, Liao R, Zhang M, Zhang Y, Wang X, Su Z. 2021. Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction. Stoch Environ Res Risk Assess. 35(6):1273–1291. doi:10.1007/s00477-020-01920-y.
   Web of Science ®Google Scholar
• Zhao Z, Liu ZY, Xu C. 2021. Slope unit-based landslide susceptibility mapping using certainty factor, support vector machine, random forest, CF-SVM and CF-RF models. Front Earth Sci. 9:589630. doi:10.3389/feart.2021.589630.
   Web of Science ®Google Scholar
• Zhao J, Zhang Q, Wang D, Wu W, Yuan R. 2022. Machine learning-based evaluation of susceptibility to geological hazards in the Hengduan Mountains Region, China. Int J Disaster Risk Sci. 13(2):305–316. doi:10.1007/s13753-022-00401-w.
   Web of Science ®Google Scholar
• Zou F, Che E, Long M. 2023. Quantitative assessment of geological hazard risk with different hazard indexes in mountainous areas. J Cleaner Prod. 413:137467. doi:10.1016/j.jclepro.2023.137467.
   Web of Science ®Google Scholar