318
Views
6
CrossRef citations to date
0
Altmetric
Research Articles

Application of novel framework approach for assessing rainfall induced future landslide hazard to world heritage sites in Indo-Nepal-Bhutan Himalayan region

, ORCID Icon, ORCID Icon, , ORCID Icon &
Pages 17742-17776 | Received 01 May 2022, Accepted 04 Oct 2022, Published online: 19 Oct 2022

References

  • Alexandrakis G, Manasakis C, Kampanis NA. 2019. Economic and societal impacts on cultural heritage sites, resulting from natural effects and climate change. Heritage. 2(1):279–305.
  • Al‐Ghussain L. 2019. Global warming: review on driving forces and mitigation. Environ Progress Sustainable Energ. 38:13–21.
  • Arabameri A, Chandra Pal S, Costache R, Saha A, Rezaie F, Seyed Danesh A, Pradhan B, Lee S, Hoang N-D. 2021. Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomatics Natural Hazards Risk. 12(1):469–498.
  • Arabameri A, Saha S, Roy J, Chen W, Blaschke T, Tien Bui D. 2020. Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed, Iran. Remote Sensing. 12(3):475.
  • Baeza C, Lantada N, Amorim S. 2016. Statistical and spatial analysis of landslide susceptibility maps with different classification systems. Environ Earth Sci. 75(19):1318.
  • Bai S, Lü G, Wang J, Zhou P, Ding L. 2011. GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci. 62(1):139–149.
  • Bhardwaj A, Wasson RJ, Ziegler AD, Chow WT, Sundriyal YP. 2019. Characteristics of rain-induced landslides in the Indian Himalaya: a case study of the Mandakini catchment during the 2013 flood. Geomorphology. 330:100–115.
  • Bilham R. 2019. Himalayan earthquakes: a review of historical seismicity and early 21st century slip potential. SP. 483(1):423–482.
  • Bogaard TA, Greco R. 2016. Landslide hydrology: from hydrology to pore pressure. Wiley Interdisciplinary Reviews Water. 3(3):439–459.
  • Breiman L. 1996. Bagging predictors. Mach Learn. 24(2):123–140.
  • Breiman L. 2001. Random forests. Machine Learning. 45(1):5–32.
  • Bui DT, Tsangaratos P, Nguyen V-T, Liem NV, Trinh PT. 2020. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. CATENA. 188:104426.
  • Buonincontri P, Marasco A, Ramkissoon H. 2017. Visitors’ experience, place attachment and sustainable behaviour at cultural heritage sites: a conceptual framework. Sustainability. 9(7):1112.
  • Can R, Kocaman S, Gokceoglu C. 2021. A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk Dam, Turkey. Appl Sci. 11(11):4993.
  • Centre UWH. 2021. India [WWW Document]. UNESCO World Heritage Centre. URL https://whc.unesco.org/en/statesparties/in. (accessed 8.29.21).
  • Chatterjee S, Goswami A, Scotese CR. 2013. The longest voyage: tectonic, magmatic, and paleoclimatic evolution of the Indian plate during its northward flight from Gondwana to Asia. Gondwana Res. 23(1):238–267.
  • Chaudhri RS. 1972. Heavy minerals from the Siwalik formations of the northwestern Himalayas. Sedimentary Geol. 8(1):77–82.
  • Chen T, Guestrin C. 2016. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794. https://doi.org/10.1145/2939672.2939785
  • Chen Y, Chen W, Pal SC, Saha A, Chowdhuri I, Adeli B, Janizadeh S, Dineva AA, Wang X, Mosavi A. 2022. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto Intern. 37(19):5564–5584.
  • Chen W, Li W, Chai H, Hou E, Li X, Ding X. 2016. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ Earth Sci. 75(1):14.
  • Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, Wang X, Bian H, Zhang S, Pradhan B, et al. 2020. Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Sci Total Environ. 701:134979.
  • Chowdhuri I, Pal SC, Arabameri A, Ngo PTT, Chakrabortty R, Malik S, Das B, Roy P. 2020a. Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India. Environ Earth Sci. 79(20):1–28.
  • Chowdhuri I, Pal SC, Chakrabortty R, Malik S, Das B, Roy P, Sen K. 2021b. Spatial prediction of landslide susceptibility using projected storm rainfall and land use in Himalayan region. Bull Eng Geol Environ. 80(7):5237–5258.
  • Chowdhuri I, Pal SC, Chakrabortty R, Malik S, Das B, Roy P. 2021a. Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya. Nat Hazards. 107(1):697–722.
  • Chowdhuri I, Pal SC, Chakrabortty R. 2020b. Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res. 65(5):1466–1489.
  • Costache R, Ali SA, Parvin F, Pham QB, Arabameri A, Nguyen H, Crăciun A, Anh DT. 2021. Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridisation with FAHP, XGBoost, and deep learning neural network. Geocarto Intern. 1–36.
  • Dehn M, Bürger G, Buma J, Gasparetto P. 2000. Impact of climate change on slope stability using expanded downscaling. Engng Geol. 55(3):193–204.
  • Di Napoli M, Carotenuto F, Cevasco A, Confuorto P, Di Martire D, Firpo M, Pepe G, Raso E, Calcaterra D. 2020. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides. 17(8):1897–1914.
  • Dikshit A, Sarkar R, Pradhan B, Segoni S, Alamri AM. 2020. Rainfall induced landslide studies in Indian Himalayan region: a critical review. Appl Sci. 10(7):2466.
  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Khosravi K, Yang Y, Pham BT. 2019a. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ. 662:332–346.
  • Dou J, Yunus AP, Tien Bui D, Sahana M, Chen C-W, Zhu Z, Wang W, Thai Pham B. 2019b. Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sensing. 11(6):638.
  • Dragičević N, Karleuša B, Ožanić N. 2018. Improvement of drainage density parameter estimation within erosion potential method. In: Multidisciplinary digital publishing institute proceedings; p. 620.
  • Du J, Glade T, Woldai T, Chai B, Zeng B. 2020. Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Engng Geol. 270:105572.
  • Gansser A. 1981. The geodynamic history of the Himalaya. Zagros Hindu Kush Himalaya Geodynamic Evol. 3:111–121.
  • Gariano SL, Guzzetti F. 2016. Landslides in a changing climate. Earth-Sci Rev. 162:227–252.
  • Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. 2019. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing. 11(2):196.
  • Ghorbanzadeh O, Crivellari A, Ghamisi P, Shahabi H, Blaschke T. 2021. A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan). Sci Rep. 11(1):14629.
  • Ghorbanzadeh O, Xu Y, Ghamis P, Kopp M, Kreil D. 2022. Landslide4Sense: reference benchmark data and deep learning models for landslide detection. arXiv Preprint arXiv:2206.00515.
  • Gislason PO, Benediktsson JA, Sveinsson JR. 2006. Random forests for land cover classification. Pattern Recog Letter. 27(4):294–300.
  • Global Warming of 1.5 °C —,. 2021. URL https://www.ipcc.ch/sr15/ [accessed 2021 August 29].
  • Gnyawali KR, Zhang Y, Wang G, Miao L, Pradhan AMS, Adhikari BR, Xiao L. 2020. Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways. Bull Eng Geol Environ. 79(2):587–601.
  • Gorum T, Fan X, van Westen CJ, Huang RQ, Xu Q, Tang C, Wang G. 2011. Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake. Geomorphology. 133(3–4):152–167.
  • 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.
  • Haigh M, Rawat JS. 2011. Landslide causes: human impacts on a Himalayan landslide swarm. Belgeo Revue belge de géographie. (3–4):201–220.
  • Hastie T, Tibshirani R, Friedman J. 2009. Boosting and additive trees. In: The elements of statistical learning. Berlin: Springer; p. 337–387.
  • Hervás J. 2013. Landslide inventory. In: Bobrowsky PT, editor. Encyclopedia of natural hazards. Netherlands: Springer; p. 610–611.
  • Hewitt K, Mehta M. 2012. Rethinking risk and disasters in mountain areas. RGA. (100-1)
  • Himalayas | Definition, Location, History, Countries, Mountains, Map, & Facts [www Document]. 2021. Encyclopedia Britannica. URL https://www.britannica.com/place/Himalayas [accessed 2021 August 29].
  • Jogdand OK. 2020. Study on the effect of global warming and greenhouse gases on environmental system. In: Green chemistry and sustainable technology. New Jersey: Apple Academic Press, p. 275–306.
  • Joshi V, Kumar K. 2006. Extreme rainfall events and associated natural hazards in Alaknanda valley, Indian Himalayan region. J Mt Sci. 3(3):228–236.
  • Karakas G, Kocaman S, Gokceoglu C. 2022. Comprehensive performance assessment of landslide susceptibility mapping with MLP and random forest: a case study after Elazig earthquake (24 Jan 2020, Mw 6.8), Turkey. Environ Earth Sci. 81(5):144.
  • Kriegler E, O’Neill BC, Hallegatte S, Kram T, Lempert RJ, Moss RH, Wilbanks T. 2012. The need for and use of socio-economic scenarios for climate change analysis: a new approach based on shared socio-economic pathways. Global Environ Change. 22(4):807–822.
  • Kumar A, Asthana AKL, Priyanka RS, Jayangondaperumal R, Gupta AK, Bhakuni SS. 2017. Assessment of landslide hazards induced by extreme rainfall event in Jammu and Kashmir Himalaya, northwest India. Geomorphology. 284:72–87.
  • Kumar V, Gupta V, Jamir I. 2018. Hazard evaluation of progressive Pawari landslide zone, Satluj valley, Himachal Pradesh, India. Nat Hazards. 93(2):1029–1047.
  • LeCun Y, Bottou L, Bengio Y, Haffner P. 1998. Gradient-based learning applied to document recognition. Proc IEEE. 86(11):2278–2324.
  • Li H, Cao Y, Li S, Zhao J, Sun Y. 2020. XGBoost model and its application to personal credit evaluation. IEEE Intell Syst. 35(3):52–61.
  • Li Z, Chen J, Tan C, Zhou X, Li Y, Han M. 2021. Debris flow susceptibility assessment based on topo-hydrological factors at different unit scales: a case study of Mentougou district, Beijing. Environ Earth Sci. 80(9):1–19.
  • Liu R, Li L, Pirasteh S, Lai Z, Yang X, Shahabi H. 2021. Correction to: the performance quality of LR, SVM, and RF for earthquake-induced landslides susceptibility mapping incorporating remote sensing imagery. Arab J Geosci. 14(5):1–1.
  • Liu Y, Fan B, Wang L, Bai J, Xiang S, Pan C. 2018. Semantic labeling in very high resolution images via a self-cascaded convolutional neural network. ISPRS J Photogrammetry Remote Sensing. 145:78–95.
  • Malik S, Pal SC, Sattar A, Singh SK, Das B, Chakrabortty R, Mohammad P. 2020. Trend of extreme rainfall events using suitable Global Circulation Model to combat the water logging condition in Kolkata Metropolitan Area. Urban Climate. 32:100599.
  • Megeirhi HA, Woosnam KM, Ribeiro MA, Ramkissoon H, Denley TJ. 2020. Employing a value-belief-norm framework to gauge Carthage residents’ intentions to support sustainable cultural heritage tourism. J Sustainable Tourism. 28(9):1351–1370.
  • Menard S. 2002. Applied logistic regression analysis. Thousand Oaks: Sage.
  • Mohammady M, Pourghasemi HR, Pradhan B. 2012. Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J Asian Earth Sci. 61:221–236.
  • Mondal S, Mandal S. 2018. RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model. Georisk Assess Manage Risk Engineered Syst Geohazards. 12(1):29–44.
  • Mondal S, Mandal S. 2019. Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model. Appl Geomat. 11(2):129–146.
  • Naithani AK, Kumar D, Prasad C. 2002. The catastrophic landslide of 16 July 2001 in Phata Byung area, Rudraprayag District, Garhwal Himalaya, India. Current Sci. 82:921–923.
  • Ngo PTT, Panahi M, Khosravi K, Ghorbanzadeh O, Kariminejad N, Cerda A, Lee S. 2021. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Frontier. 12(2):505–519.
  • Nhu V-H, Mohammadi A, Shahabi H, Ahmad BB, Al-Ansari N, Shirzadi A, Geertsema M, R. Kress V, Karimzadeh S, Valizadeh Kamran K, et al. 2020a. Landslide detection and susceptibility modeling on Cameron highlands (Malaysia): a comparison between random forest, logistic regression and logistic model tree algorithms. Forests. 11(8):830.
  • Nhu V-H, Shirzadi A, Shahabi H, Chen W, Clague JJ, Geertsema M, Jaafari A, Avand M, Miraki S, Talebpour Asl D, et al. 2020b. Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests. 11(4):421.
  • Novellino A, Cesarano M, Cappelletti P, Di Martire D, Di Napoli M, Ramondini M, Sowter A, Calcaterra D. 2021. Slow-moving landslide risk assessment combining machine learning and InSAR techniques. Catena. 203:105317.
  • O’Neill BC, Carter TR, Ebi K, Harrison PA, Kemp-Benedict E, Kok K, Kriegler E, Preston BL, Riahi K, Sillmann J, et al., 2020. Achievements and needs for the climate change scenario framework. Nat Clim Chang. 10(12):1074–1084.
  • O’Neill BC, Tebaldi C, van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, Knutti R, Kriegler E, Lamarque J-F, Lowe J, et al., 2016. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci Model Dev. 9(9):3461–3482.
  • Onagh M, Kumra VK, Rai PK. 2012. Landslide susceptibility mapping in a part of Uttarkashi district (India) by multiple linear regression method. Intern J Geol Earth Environ Sci. 2:102–120.
  • Pal SC, Chowdhuri I. 2019. GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Appl Sci. 1(5):416.
  • Pandey VK, Sharma KK, Pourghasemi HR, Bandooni SK. 2019. Sedimentological characteristics and application of machine learning techniques for landslide susceptibility modelling along the highway corridor Nahan to Rajgarh (Himachal Pradesh), India. CATENA. 182:104150.
  • Panthi J, Dahal P, Shrestha ML, Aryal S, Krakauer NY, Pradhanang SM, Lakhankar T, Jha AK, Sharma M, Karki R. 2015. Spatial and temporal variability of rainfall in the Gandaki River Basin of Nepal Himalaya. Climate. 3(1):210–226.
  • Pham BT, Prakash I, Singh SK, Shirzadi A, Shahabi H, Tran T-T-T, Bui DT. 2019. Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: hybrid machine learning approaches. Catena. 175:203–218.
  • Pham QB, Mukherjee K, Norouzi A, Linh NTT, Janizadeh S, Ahmadi K, Cerdà A, Doan TNC, Anh DT. 2020. Head-cut gully erosion susceptibility modelling based on ensemble random forest with oblique decision trees in Fareghan watershed, Iran. Geomatics Natural Hazards Risk. 11(1):2385–2410.
  • Pham QB, Pal SC, Chakrabortty R, Norouzi A, Golshan M, Ogunrinde AT, Janizadeh S, Khedher KM, Anh DT. 2021. Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas. Geomatics Natural Hazards Risk. 12(1):2607–2628.
  • Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C. 2013. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci. 122(2):349–369.
  • Pourghasemi HR, Pradhan B, Gokceoglu C. 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards. 63(2):965–996.
  • Pradhan AMS, Kim Y-T. 2020. Rainfall-induced shallow landslide susceptibility mapping at two adjacent catchments using advanced machine learning algorithms. IJGI. 9(10):569.
  • Pradhan B, Lee S. 2010. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides. 7(1):13–30.
  • Pradhan B. 2011a. Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci. 63(2):329–349.
  • Pradhan B. 2011b. An assessment of the use of an advanced neural network model with five different training strategies for the preparation of landslide susceptibility maps. J Data Sci. 9:65–81.
  • Pradhan SP, Panda SD, Roul AR, Thakur M. 2019. Insights into the recent Kotropi landslide of August 2017, India: a geological investigation and slope stability analysis. Landslides. 16(8):1529–1537.
  • Rahmati O, Yousefi S, Kalantari Z, Uuemaa E, Teimurian T, Keesstra S, Pham TD, Tien Bui D. 2019. Multi-hazard exposure mapping using machine learning techniques: a case study from Iran. Remote Sensing. 11(16):1943.
  • Ramos-Cañón AM, Prada-Sarmiento LF, Trujillo-Vela MG, Macías JP, Santos-r AC. 2016. Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia. Landslides. 13(4):671–681.
  • Roy P, Chandra Pal S, Arabameri A, Chakrabortty R, Pradhan B, Chowdhuri I, Lee S, Tien Bui D. 2020a. Novel ensemble of multivariate adaptive regression spline with spatial logistic regression and boosted regression tree for gully erosion susceptibility. Remote Sensing. 12(20):3284.
  • Roy P, Chandra Pal S, Chakrabortty R, Chowdhuri I, Malik S, Das B. 2020b. Threats of climate and land use change on future flood susceptibility. J Cleaner Product. 272:122757.
  • Roy P, Martha TR, Jain N, Kumar KV. 2018. Reactivation of minor scars to major landslides: a satellite-based analysis of Kotropi landslide (13 August 2017) in Himachal Pradesh, India. Curr Sci. 115(3):395–398.
  • Saha A, Pal SC, Arabameri A, Chowdhuri I, Rezaie F, Chakrabortty R, Roy P, Shit M. 2021a. Optimisation modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements. J Environ Manage. 287:112284.
  • Saha A, Pal SC, Santosh M, Janizadeh S, Chowdhuri I, Norouzi A, Roy P, Chakrabortty R. 2021b. Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: the present and future scenarios. J Cleaner Product. 320:128713.
  • 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 mapping. Geocarto Intern. 35(4):341–363.
  • Sahin EK. 2020. Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl Sci. 2(7):1308.
  • Sameen MI, Pradhan B, Lee S. 2020. Application of convolutional neural networks featuring Bayesian optimisation for landslide susceptibility assessment. Catena. 186:104249.
  • Sardana S, Verma AK, Singh A. 2019. Comparative analysis of rockmass characterisation techniques for the stability prediction of road cut slopes along NH-44A, Mizoram, India. Bull Eng Geol Environ. 78(8):5977–5989.
  • Sarkar S, Kanungo DP, Patra AK, Kumar P. 2012. GIS Based Landslide Susceptibility Mapping–A Case Study in Indian Himalaya.
  • Schandl H, Lu Y, Che N, Newth D, West J, Frank S, Obersteiner M, Rendall A, Hatfield-Dodds S. 2020. Shared socio-economic pathways and their implications for global materials use. Resource Conserv Recyc. 160:104866.
  • Shahri AA, Spross J, Johansson F, Larsson S. 2019. Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena. 183:104225.
  • Shakoor A, Ashraf F, Shakoor S, Mustafa A, Rehman A, Altaf MM. 2020. Biogeochemical transformation of greenhouse gas emissions from terrestrial to atmospheric environment and potential feedback to climate forcing. Environ Sci Pollut Res. 27(31):38513–38536.
  • Singh P, Sharma A, Sur U, Rai PK. 2021. Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India. Environ Dev Sustain. 23(4):5233–5250.
  • Sun D, Shi S, Wen H, Xu J, Zhou X, Wu J. 2021. A hybrid optimisation method of factor screening predicated on geodetector and random forest for landslide susceptibility mapping. Geomorphology. 379:107623.
  • Sun D, Wen H, Wang D, Xu J. 2020. A random forest model of landslide susceptibility mapping based on hyperparameter optimisation using Bayes algorithm. Geomorphology. 362:107201.
  • Sur U, Singh P, Rai PK, Thakur JK. 2021. Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India. Environ Dev Sustain. 23(9):13526–13554.
  • Temme AJ. 2021. Relations between soil development and landslides. Hydrogeol Chem Weather Soil Formation. 177–185.
  • Thai Pham B, Shirzadi A, Shahabi H, Omidvar E, Singh SK, Sahana M, Talebpour Asl D, Bin Ahmad B, Kim Quoc N, Lee S. 2019. Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability. 11(16):4386.
  • Tseng CM, Lin CW, Hsieh WD. 2015. Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories. Natural Hazards Earth Syst Sci Discussions. 3:1137–1173.
  • Umrao RK, Singh R, Sharma LK, Singh TN. 2017. Soil slope instability along a strategic road corridor in Meghalaya, north-eastern India. Arab J Geosci. 10(12):1–9.
  • van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, et al. 2011. The representative concentration pathways: an overview. Climatic Change. 109(1–2):5–31.
  • Van Westen CJ, Rengers N, Soeters R. 2003. Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards. 30(3):399–419.
  • Van Westen CJ. 2000. The modelling of landslide hazards using GIS. Surveys in Geophysics. 21(2/3):241–255.
  • Varnes DJ. 1984. Landslide hazard zonation: a review of principles and practice. Natural Hazards.
  • Wang S, Dong P, Tian Y. 2017. A novel method of statistical line loss estimation for distribution feeders based on feeder cluster and modified XGBoost. Energies. 10(12):2067.
  • Wang Y, Fang Z, Hong H. 2019. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ. 666:975–993.
  • Xu X, Shan D, Wang G, Jiang X. 2016. Multimodal medical image fusion using PCNN optimised by the QPSO algorithm. Appl Soft Comput. 46:588–595.
  • Zhang C, Sargent I, Pan X, Li H, Gardiner A, Hare J, Atkinson PM. 2018. An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sensing Environ. 216:57–70.
  • Zhang Y, Ge T, Tian W, Liou Y-A. 2019a. Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sensing. 11(23):2801.
  • Zhao P, Masoumi Z, Kalantari M, Aflaki M, Mansourian A. 2022. A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods. Remote Sensing. 14(1):211.
  • Zheng H, Yuan J, Chen L. 2017. Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies. 10(8):1168.
  • Zhongming Z, Linong L, Wangqiang Z, Wei L. 2021. AR6 Climate Change 2021: The Physical Science Basis.
  • Zhuang X, Yao Y, Li JJ. 2019. Sociocultural impacts of tourism on residents of world cultural heritage sites in China. Sustainability. 11(3):840.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.