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2024 | OriginalPaper | Buchkapitel

4. An Advanced Hybrid Machine Learning Technique for Assessing the Susceptibility to Landslides in the Upper Meenachil River Basin of Kerala, India

verfasst von : Anik Saha, Bishnu Roy, Sunil Saha, Ankit Chaudhary, Raju Sarkar

Erschienen in: Geomorphic Risk Reduction Using Geospatial Methods and Tools

Verlag: Springer Nature Singapore

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Abstract

The ambition of the current study was to generate landslide susceptibility maps (LSMs) for the Meenachil river basin’s upper catchment using the ensemble NBT-RTF, Naive Bayes tree (NBT), and rotation forest (RTF). For landslide susceptibility modelling, 189 landslide sites and 12 landslide conditioning factors (LCFs) were gathered. Multi-collinearity analysis was done among the LCFs to determine the best LCFs to use. The metrics utilized to assess the predictive power of the employed models are ROC-AUC, mean-absolute-error (MAE), root-mean-square-error (RMSE), and kappa coefficient. Almost 14% of the studied region has very high landslide susceptibility, according to the results of the best-performed model. The NBT-RTF model got the lowest RMSE and the greatest ROC-AUC (0.867) and kappa index (0.884) during the validation phase (0.234). The anticipated model is reliable for minimizing the impact of landslides in the research region and planning land development.

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Vorheriges Kapitel Artificial Neural Network Ensemble with General Linear Model for Modeling the Landslide Susceptibility in Mirik Region of West Bengal, India

Nächstes Kapitel Novel Ensemble of M5P and Deep Learning Neural Network for Predicting Landslide Susceptibility: A Cross-Validation Approach

Alcantara-Ayala I (2002) Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphology 47(2–4):107–124. https://doi.org/10.1016/S0169-555X(02)00083-1CrossRef

Althuwaynee OF, Pradhan B, Lee S (2016) A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. Int J Remote Sens 37(5):1190–1209. https://doi.org/10.1080/01431161.2016.1148282CrossRef

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 16:595–618CrossRef

Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31CrossRef

Chen W, Li W, Hou E, Bai H, Chai H, Wang D et al (2015) Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong region of Baoji, China. Arab J Geosci 8(4):1829–1841. https://doi.org/10.1007/s12517-014-1554-0CrossRef

Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29:103–130CrossRef

Du G, Zhang Y, Iqbal J (2017) Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. J Mt Sci 14:249CrossRef

Fan X, Scaringi G, Korup O, West AJ, Westen CJ, Tanyas H et al (2019) Earthquake-induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev Geophys 57(2):421–503. https://doi.org/10.1029/2018rg000626CrossRef

Fang Z, Wang Y, Duan G, Peng L (2021) Landslide susceptibility mapping using rotation forest ensemble technique with different decision trees in the three gorges reservoir area, China. Remote Sens 13(2):238. https://doi.org/10.3390/rs13020238CrossRef

Ghasemian B, Shahabi H, Shirzadi A, Al-Ansari N, Jaafari A, Kress VR et al (2022) A robust deep-learning model for landslide susceptibility mapping: a case study of Kurdistan Province, Iran. Sensors 22(4):1573. https://doi.org/10.3390/s22041573CrossRef

GSI (2002) Geology and minerals, District Resource Map, Kottayam District. Geological Survey of India, Kolkata

Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36CrossRef

He Q, Xu Z, Li S, Li R, Zhang S, Wang N et al (2019) Novel entropy and rotation forest-based credal decision tree classifier for landslide susceptibility modeling. Entropy 21(2):106. https://doi.org/10.3390/e21020106CrossRef

Hellerstein J, Thathachar J, Rish I (2000) Recognizing end-user transactions in performance management. In: Proceedings of AAAI-2000

Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Xing Zhu A, Chen W, Ahmad BB (2018) Landslide susceptibility mapping using J48 decision tree with Adaboost, bagging and rotation forest ensembles in the Guangchang area (China). CATENA 163:399–413CrossRef

Huppert HE, Sparks RSJ (2006) Extreme natural hazards: population growth, globalization and environmental change. Phil Trans R Soc A 364(1845):1875–1888. https://doi.org/10.1098/rsta.2006.1803CrossRef

Jaafari A, Rezaeian J, Omrani MS (2017) Spatial prediction of slope failures in support of forestry operations safety. Croat J for Eng 38(1):107–118

Kadirhodjaev A, Rezaie F, Lee M-J, Lee S (2020) Landslide susceptibility assessment using an optimized group method of data handling model. ISPRS Int J Geo-Inf 9(10):566. https://doi.org/10.3390/ijgi9100566CrossRef

KSLUB (1996) Watershed atlas. Kerala State Land Use Board. Govt. of Kerala. Publ

Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87:271–286CrossRef

Lucchese LV, De Oliveira GG, Pedrollo OC (2021) Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping. Nat Hazards 106(3):2381–2405. https://doi.org/10.1007/s11069-021-04547-6CrossRef

Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Process Landforms 29(6):687–711. https://doi.org/10.1002/esp.1064CrossRef

Meena S, Mishra B, Tavakkoli Piralilou S (2019) A hybrid spatial multicriteria evaluation method for mapping landslide susceptible areas in Kullu Valley, Himalayas. Geosciences 9(4):156. https://doi.org/10.3390/geosciences9040156

Merghadi A, Yunus AP, Dou J, Whiteley J, Thaipham B, Bui DT et al (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.2020.103225CrossRef

Nguyen QK, Bui DT, Hoang ND, Trinh PT, Nguyen VH, Yilmaz I (2017) A novel hybrid approach based on instance based learning classifier and rotation forest ensemble for spatial prediction of rainfall induced shallow landslides using GIS. Sustain Switz 9(5):813. https://doi.org/10.3390/su9050813CrossRef

Nhu V-H, Shirzadi A, Shahabi H, Chen W, Clague JJ, Geertsema M et al (2020) Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests 11(4):421. https://doi.org/10.3390/f11040421CrossRef

Park S, Kim J (2019) Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl Sci 9(5):942. https://doi.org/10.3390/app9050942CrossRef

Pham BT, Bui DT, Dholakia MB, Prakash I, Pham HV, Mehmood K et al (2017) A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen District, Yen Bai Province (Viet Nam) using GIS. Geomatics Nat Hazards Risk 8(2), 649–671. https://doi.org/10.1080/19475705.2016.1255667

Pradhan B, Seeni M, Kalantar B (2017) Performance evaluation and sensitivity analysis of expert-based, statistical, machine learning, and hybrid models for producing landslide susceptibility maps. Laser scanning applications in landslide assessment, pp 193–232

Pradhan AMS, Kim YT (2016) Spatial data analysis and application of evidential belief functions to shallow landslide susceptibility mapping at Mt. Umyeon, Seoul Korea. B Eng Geol Environ 76:1263–1279CrossRef

Razavizadeh S, Solaimani K, Massironi M, Kavian A (2017) Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in Northern Iran. Environ Earth Sci 76(14):1–16. https://doi.org/10.1007/s12665-017-6839-7CrossRef

Rish I, Hellerstein J, Jayram T (2001) An analysis of data characteristics that affect naive Bayes performance. IBM TJ Watson Research Center, New York (NY)

Saha S, Saha A, Roy B, Sarkar R, Bhardwaj D, Kundu B (2022b) Integrating the Particle Swarm Optimization (PSO) with machine learning methods for improving the accuracy of the landslide susceptibility model. Earth Sci Inf 15(4):2637–2662CrossRef

Saha A, Saha S (2022) Landslide susceptibility assessment and management using advanced hybrid machine learning algorithms in Darjeeling Himalaya, India. In: Applied geomorphology and contemporary issues. Springer, Cham, pp 667–681

Saha S, Saha A, Hembram TK, Kundu B, Sarkar R (2022a) Novel ensemble of deep learning neural network and support vector machine for landslide susceptibility mapping in Tehri region, Garhwal Himalaya. Geocarto Int 1–26

Soman K (2002) Geology of Kerala, 2nd edn. Geological Society of India, Bangalore, pp 117–204

Sun D, Xu J, Wen H, Wang D (2021) Assessment of landslide susceptibility mapping based on: a comparison between logistic regression and random forest. Eng Geol 281:105972. https://doi.org/10.1016/j.enggeo.2020.105972CrossRef

Tran QC, Minh DD, Jaafari A, Al-Ansari N, Minh DD, Van DT et al (2020) Novel ensemble landslide predictive models based on the hyperpipes algorithm: a case study in the Nam Dam Commune, Vietnam. Appl Sci 10(11):3710. https://doi.org/10.3390/app10113710CrossRef

Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582. https://doi.org/10.1016/j.geomorph.2008.02.011CrossRef

Zhang H, Su J (2004) Naive Bayesian classifiers for ranking. Mach Learn 3201:501–512

Zhou X, Wen H, Zhang Y, Xu J, Zhang W (2021) Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization. Geosci Front 12(5):101211. https://doi.org/10.1016/j.gsf.2021.101211CrossRef

Titel: An Advanced Hybrid Machine Learning Technique for Assessing the Susceptibility to Landslides in the Upper Meenachil River Basin of Kerala, India
verfasst von: Anik Saha
Bishnu Roy
Sunil Saha
Ankit Chaudhary
Raju Sarkar
Verlag: Springer Nature Singapore
Buch: Geomorphic Risk Reduction Using Geospatial Methods and Tools
Print ISBN: 978-981-9977-06-2

Electronic ISBN: 978-981-9977-07-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-7707-9_4

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