Skip to main content

Advertisement

SpringerLink
Log in

Integrating deep learning neural network and M5P with conventional statistical models for landslide susceptibility modelling

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

Landslides are among the devastating geological hazards that cause immense damage in hilly regions. The Indian Himalayan region is plagued by numerous major landslides. Here we present results of landslide susceptibility mapping in a representative area of Jakholi region of Indian Himalaya based on a novel approach of integrating deep learning neural network (DLNN) and M5 Prime (M5P) with the conventional statistical model weight of evidence (WoE). These models were trained using 70% of inventory landslides and evaluated using 30%, considering 14 factors divided into topographical, hydrological, geological, and environmental landslide conditioning factors (LCFs). The appropriateness of factors was judged using the multicollinearity test, and the WoE model was used to evaluate the degree of association between LCFs and landslide occurrences. Precision, accuracy, Kappa coefficient, root mean square error (RMSE), and area under the receiver operating characteristic (AUC-ROC) curve were used to assess the models’ efficiency. The proposed WoE, M5P, WoE-M5P, DLNN, and WoE-DLNN models quantified 16.68%, 19.48%, 18.98%, 19.68%, and 16.67% of the area as very highly landslide-prone region, respectively. Ensemble of WoE-DLNN model with 89.96% success rate and 92.51% prediction rate in terms of AUC outperformed the WoE-M5P and individual models. It was also found that the DLNN alone performs more efficiently than WoE-M5P. Our study thus reveals that enhanced performance of WoE-DLNN ensemble could be used in other regions with similar geo-environmental settings. The results from this study can be of potential use to regional planners and governmental agencies in formulating effective landslide management plans.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1 
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

Derived data supporting the findings of this study are available from the corresponding author on request.

References 

  • Aghda SF, Bagheri V, Razifard M (2018) Landslide susceptibility mapping using fuzzy logic system and its influences on mainlines in lashgarak region, Tehran Iran. Geotech and Geolog Eng 36(2):915–937

    Google Scholar 

  • Arya, I.W., Wiraga, I.W., Dwipa, I.G.A.G.S. and Pramana, I.M.W., 2020, February. Effect of pore water pressure on soil crack against safety factor of slope stability. In Jou. of Phys: Conference Series (Vol. 1450, No. 1: 012014). IOP Publishing.

  • Ávila, F.F., Alvalá, R.C., Mendes, R.M. and Amore, D.J., 2020. The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models. Nat. Haz.: 1–23

  • Ayaz Y, Kocamaz AF, Karakoç MB (2015) Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers. Constr Build Mater 94:235–240

  • Band SS, Janizadeh S, Chandra Pal S, Saha A, Chakrabortty R, Shokri M, Mosavi A (2020) Novel ensemble approach of deep learning neural network (DLNN) model and particle swarm optimization (PSO) algorithm for prediction of gully erosion susceptibility. Sens 20(19):5609

    Article  Google Scholar 

  • Basu T, Das A. and Pal S, 2020. Application of geographically weighted principal component analysis and fuzzy approach for unsupervised landslide susceptibility mapping on Gish River Basin, India. Geocar. Inter.: 1–24.

  • Benchelha S, Aoudjehane HC, Hakdaoui M, El Hamdouni R, Mansouri H, Benchelha T, Layelmam M, Alaoui M (2019) Landslide susceptibility mapping: a comparison between logistic regression and multivariate adaptive regression spline models in the municipality of Oudka, Northern of Morocco. Inter Jou of Geotec and Geolog Eng 13(5):381–393

    Google Scholar 

  • Berhane G, Kebede M, Alfarah N, Hagos E, Grum B, Giday A, Abera T (2020) Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial data-sets in the Adwa-Adigrat mountain chains, northern Ethiopia. Jou of African Ear Sci 164:103795

    Article  Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. In: Bonham-Carter F (ed) Computer methods in the geosciences, vol 13. Pergamon, Oxford, p 398

    Google Scholar 

  • Bordoni M, Cislaghi A, Vercesi A, Bischetti GB, Meisina C (2020) Effects of plant roots on soil shear strength and shallow landslide proneness in an area of northern Italian Apennines. Bull Eng Geol Environ 79:3361–3381

  • Bowers AJ, Zhou X (2019) Receiver operating characteristic (ROC) area under the curve (AUC): a diagnostic measure for evaluating the accuracy of predictors of education outcomes. Jou of Edu Stud Placed at Risk (JESPAR) 24(1):20–46

    Article  Google Scholar 

  • Bui DTT, Nguyen VT, Van Liem N, Trinh T (2020) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. Catena 188:104426

    Article  Google Scholar 

  • Cała M (2007) Convex and concave slope stability analyses with numerical methods. Arch of Min Sci 52(1):75–89

    Google Scholar 

  • Chai Z, Zhao C (2019) Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification. IEEE Tran on Indus Infor 16(1):54–66

    Article  Google Scholar 

  • Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85

    Article  Google Scholar 

  • Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu AX, Pei X, Duan Z (2018a) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province. China Sci of the Tot Env 626:1121–1135

  • Chen W, Shahabi H, Zhang S, Khosravi K, Shirzadi A, Chapi K, Pham BT, Zhang T, Zhang L, Chai H, Ma J (2018b) Landslide susceptibility modeling based on gis and novel bagging-based kernel logistic regression. App Sci 8(12):2540

  • Chen W, Shahabi H, Shirzadi A, Hong H, Akgun A, Tian Y, Liu J, Zhu AX, Li S (2019) Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling. Bull of Eng Geol and the Env 78(6):4397–4419

    Article  Google Scholar 

  • Chen L, Su W, Feng Y, Wu M, She J, Hirota K (2020) Two-layer fuzzy multiple random forest for speech emotion recognition in human-robot interaction. Info Sci 509:150–163

    Article  Google Scholar 

  • Chen Y, Chen W, Janizadeh S, Bhunia GS, Bera A, Pham QB, Linh NTT, Balogun AL and Wang X (2021) Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region. Geocar. Inter.: 1–27.

  • Chowdhuri I, Pal SC, Saha A, Roy , Chakrabortty R, & Shit, M.(2022). Application of novel framework approach for assessing rainfall induced future landslide hazard to world heritage sites in Indo-Nepal-Bhutan Himalayan region. Geocarto International, 1–35.

  • Cobos FA, Alquicira-Hernandez J, Powell JE, Mestdagh: and De Preter, K. (2020) Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commu 11(1):1–14

    Google Scholar 

  • Coelho IM, Coelho VN, da Luz EJS, Ochi LS, Guimarães FG, Rios E (2017) A GPU deep learning metaheuristic based model for time series forecasting. Appl. Energy 201:412–418

    Article  Google Scholar 

  • Costache R, Arabameri A, Blaschke T, Pham QB, Pham BT, Pandey M, Arora A, Linh NTT, Costache I (2021) Flash-flood potential mapping using deep learning, alternating decision trees and data provided by remote sensing sensors. Sen 21(1):280

    Article  Google Scholar 

  • Dar T, Rai N and Bhat A (2020) Delineation of potential groundwater recharge zones using analytical hierarchy process (AHP). Geol., Ecol., and Lands.: 1–16.

  • de Oliveira GG, Ruiz LFC, Guasselli LA, Haetinger C (2019) Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin. Southern Brazil Nat Haz 99(2):1049–1073

    Article  Google Scholar 

  • Deline , Gruber S., Amann F, Bodin X, Delaloye R, Failletaz J, Fischer L, Geertsema M, Giardino M, Hasler A and Kirkbride M, (2021) Ice loss from glaciers and permafrost and related slope instability in high-mountain regions. In Snow and Ice-Rel. Haz., Risks, and Dis. (pp. 501–540). Elsevier.

  • Deo RC, Ghorbani MA, Samadianfard S, Maraseni T, Bilgili M, Biazar M (2018) Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renew Ene 116:309–323

    Article  Google Scholar 

  • Dev VA, Eden MR (2019) Formation lithology classification using scalable gradient boosted decision trees. Com & Che Eng 128:392–404

    Article  Google Scholar 

  • Dolojan NLJ, Moriguchi S, Hashimoto M, Terada K (2021) Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis: A case study in Marumori, Miyagi, Japan, during the October 2019 Typhoon Hagibis. Landslides 18:2039–2057

  • Dorairaj D, Osman N (2021) Present practices and emerging opportunities in bioengineering for slope stabilization in Malaysia: an overview. PeerJ 9:e10477

    Article  Google Scholar 

  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen CW, Khosravi K, Yang Y, Pham BT (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island. Japan Sci of the Tot Env 662:332–346

    Article  Google Scholar 

  • Dumperth, C., 2019. Three-dimensional numerical investigations on Huangtupo landslide (PR China): from the slope reconstruction to the implementation into an integrative early warning system.

  • Esposito G, Carabella C, Paglia G, Miccadei E (2021) Relationships between morphostructural/geological framework and landslide types: historical landslides in the hilly piedmont area of Abruzzo Region (Central Italy). Land 10(3):287

    Article  Google Scholar 

  • Fang Z, Wang Y, Peng L, Hong H (2020) Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Comput Geosci 139:104470

  • Farhat H, Sakr GE, Kilany R (2020) Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. Machine Vision and Appls 31(6):1–42

    Google Scholar 

  • Gariano SL, Melillo M, Peruccacci S, Brunetti MT (2020) How much does the rainfall temporal resolution affect rainfall thresholds for landslide triggering? Nat Haz 100(2):655–670

    Article  Google Scholar 

  • Gholam V, Sahour H, Amri MAH (2021) Soil erosion modeling using erosion pins and artificial neura l networks. Catena 196:104902

    Article  Google Scholar 

  • Ghorbanzadeh O, Blaschke T, Aryal J, Gholaminia K (2020) A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. Jou of Spat Sci 65(3):401–418

    Article  Google Scholar 

  • Gracchi T (2019) Wireless sensor networks for landslide monitoring: application and optimization by visibility analysis on 3D point clouds (Doc. Diss., Uni. of Florence).

  • GudiyangadaNachappa T, TavakkoliPiralilou S, Ghorbanzadeh O, Shahabi H, Blaschke T (2019) Landslide susceptibility mapping for austria using geons and optimization with the dempster-shafer theory. Appl Scis 9(24):5393

    Article  Google Scholar 

  • He L, Coggan J, Francioni M, Eyre M (2021) Maximizing impacts of remote sensing surveys in slope stability—a novel method to incorporate discontinuities into machine learning landslide prediction. ISPRS Inter Jou of Geo-Infor 10(4):232

    Article  Google Scholar 

  • Hembram TK, Saha S (2020) Prioritization of sub-watersheds for soil erosion based on morphometric attributes using fuzzy AHP and compound factor in Jainti River basin, Jharkhand Eastern India. Envi Dev and Sus 22(2):1241–1268

    Article  Google Scholar 

  • Hembram TK, Saha S, Pradhan B, Abdul Maulud KN, Alamri AM (2021) Robustness analysis of machine learning classifiers in predicting spatial gully erosion susceptibility with altered training samples. Geo Nat Haz and Risk 12(1):794–828

    Google Scholar 

  • Hoa V, Giang NV, Binh NA, Hai LVH, Pham TD, Hasanlou M, Tien Bui D (2019) Soil salinity mapping using SAR sentinel-1 data and advanced machine learning algorithms: a case study at Ben Tre Province of the Mekong River Delta (Vietnam). Rem. Sens 11(2):128

    Article  Google Scholar 

  • Hong H, Tsangaratos P, Ilia I, Liu J, Zhu AX, Chen W (2018) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County. China. Sci. of the tot. env 625:575–588

    Article  Google Scholar 

  • Huang F, Zhang J, Zhou C, Wang Y, Huang J, Zhu L (2020) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17(1):217–229

    Article  Google Scholar 

  • Jaafari A, Najafi A, Rezaeian J, Sattarian A, Ghajar I (2015) Planning road networks in landslide-prone areas: a case study from the northern forests of Iran. Land Use Policy 47:198–208

    Article  Google Scholar 

  • Jia G, Tang Q, Xu X (2020) Evaluating the performances of satellite-based rainfall data for global rainfall-induced landslide warnings. Landslides 17(2):283–299

    Article  Google Scholar 

  • Jiang JC, Baker R, Yamagami T (2003) The effect of strength envelope nonlinearity on slope. Can Geotec Jou 40:308–325

    Article  Google Scholar 

  • Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hübl J (2019) Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District. Uzbekistan Sci of the Tot Env 653:801–814

    Article  Google Scholar 

  • Kainthura P, Sharma N (2022) Machine learning driven landslide susceptibility prediction for the Uttarkashi region of Uttarakhand in India. Georisk: Assess Manag Risk Eng Syst Geohazards 16(3):570–583

    Google Scholar 

  • Kavzoglu T, Sahin EK, Colkesen I (2015) An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat Haz 76(1):471–496

    Article  Google Scholar 

  • Kelman I (2020) Disaster by choice: how our actions turn natural hazards into catastrophes. Press, Oxf. Uni

    Google Scholar 

  • Khalaf JA, Majeed AA, Aldlemy MS, Ali ZH, Al Zand AW, Adarsh S et al (2021) Hybridized deep learning model for perfobond rib shear strength connector prediction. Complexity 2021:1–21

  • Khanduri S (2019) Natural hazards in the townships of Nainital, Uttarakhand in Inida. Inter Jou of Eng Appl Sci and Tech 3(12):42–49

    Google Scholar 

  • Kindiga SW (2017) Environmental and land use impacts of quarrying along Ngong river in Embakasi (Doc. Diss., Univ.of Nairobi).

  • Konstantakopoulos IC, Barkan AR, He S, Veeravalli T, Liu H, Spanos C (2019) A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure. App Ene 237:810–821

    Article  Google Scholar 

  • Lee EM, Giles DP (2020) Landslide and slope stability hazard in the UK. Geol Soci London Eng Geo Sp l Pubs 29(1):81–162

    Google Scholar 

  • Lee, D. H., Kim, Y. T., & Lee, S. R. (2020). Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sensing, 12(7), 1194

  • Li Y, Chen W (2020) Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water 12(1):113

    Article  Google Scholar 

  • Li C, Fu Z, Wang Y, Tang H, Yan J, Gong W, Yao W, Criss RE (2019) Susceptibility of reservoir-induced landslides and strategies for increasing the slope stability in the Three Gorges Reservoir Area: Zigui Basin as an example. Eng Geol 261:105279

    Article  Google Scholar 

  • Li Y, Jiao Q, Hu X, Li Z, Li B, Zhang J, Jiang W, Luo Y, Li Q, Ba R (2020) Detecting the slope movement after the 2018 Baige Landslides based on ground-based and space-borne radar observations. Inter Jou of Appl Earth Obser and Geoinfor 84:101949

    Google Scholar 

  • Liu Z, Gilbert G, Cepeda JM, Lysdahl AOK, Piciullo L, Hefre H, Lacasse S (2021) Modelling of shallow landslides with machine learning algorithms. Geosci Front 12(1):385–393

    Article  Google Scholar 

  • Liu Y, Liu D, Qin Z, Liu F, Liu L (2016) Rainfall data feature extraction and its verification in displacement prediction of Baishuihe landslide in China. Bull Eng Geol Environ 75:897–907

  • Löbmann, M.T., Geitner, C., Wellstein, C. and Zerbe, S., 2020. The influence of herbaceous vegetation on slope stability–a review. Earth-Sci Rev 103328.

  • Luo X, Lin F, Zhu S, Yu M, Zhang Z, Meng L, Peng J (2019) Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors. PLoS ONE 14(4):e0215134

    Article  Google Scholar 

  • Ma S, Qiu H, Hu S, Yang D, Liu Z (2021) Characteristics and geomorphology change detection analysis of the Jiangdingya landslide on July 12, 2018. China Landslides 18(1):383–396

    Article  Google Scholar 

  • Mandal K, Saha S, Mandal S (2021) Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya. India Geosci Front 12(5):101203

    Article  Google Scholar 

  • Meena SR, Mishra BK, TavakkoliPiralilou S (2019) A hybrid spatial multi-criteria evaluation method for mapping landslide susceptible areas in kullu valley, himalayas. Geosci 9(4):156

    Article  Google Scholar 

  • Meghanadh D, Maurya VK, Tiwari A, Dwivedi R (2022) A multi-criteria landslide susceptibility mapping using deep multi-layer perceptron network: a case study of Srinagar-Rudraprayag region (India). Adv Space Res 69(4):1883–1893

    Article  Google Scholar 

  • Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R and Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci Rev 103225.

  • Mersha T, Meten M (2020) GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenv Dis 7(1):1–22

    Google Scholar 

  • Mokarram M, Zarei AR (2018) Landslide susceptibility mapping using fuzzy-AHP. Geotec and Geol Eng 36(6):3931–3943

    Article  Google Scholar 

  • Mondal S, Mandal S (2019) Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model. Appl Geom 11(2):129–146

    Article  Google Scholar 

  • Moragues S, Lenzano MG, Lanfri M, Moreiras S, Lannutti E, Lenzano L (2021) Analytic hierarchy process applied to landslide susceptibility mapping of the North Branch of Argentino Lake. Argentina Nat Haz 105(1):915–941

    Article  Google Scholar 

  • Muhammad, I.U., Muhammad, A. and Muhammad, M.I.U., 2020. Package ‘mctest’.

  • Myronidis D, Papageorgiou C, Theophanous S (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards 81:245–263

  • Naidu S, Sajinkumar KS, Oommen T, Anuja VJ, Samuel RA, Muraleedharan C (2018) Early warning system for shallow landslides using rainfall threshold and slope stability analysis. Geo Front 9(6):1871–1882

    Article  Google Scholar 

  • Nguyen VN, Yariyan P, Amiri M, Dang Tran A, Pham TD, Do MP, Thi Ngo PT, Nhu VH, Quoc Long N, Tien Bui D (2020) A new modeling approach for spatial prediction of flash flood with biogeography optimized chaid tree ensemble and remote sensing data. Rem. Sens. 12(9):1373

    Article  Google Scholar 

  • Nhu VH, Hoang ND, Nguyen H, Ngo PTT, Bui TT, Hoa PV, Samui P, Bui DT (2020) Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. Catena 188:104458

    Article  Google Scholar 

  • Nhu VH, Shirzadi A, Shahabi H, Chen W, Clague JJ, Geertsema M, Jaafari A, Avand M, Miraki S, TalebpourAsl D, Pham BT (2020) Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests 11(4):421

    Article  Google Scholar 

  • Nhu VH, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, Jaafari A, Chen W, Miraki S, Dou J, Luu C (2020) Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. Inter J of Env Res and Pub Health 17(8):2749

    Article  Google Scholar 

  • Nsengiyumva JB, Luo G, Nahayo L, Huang X, Cai P (2018) Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. Inter Jou of Env Res and Pub Health 15(2):243

    Article  Google Scholar 

  • Pandey, A.K., 2020. Responsible factors of environmental degradation in Nandakini River Valley. In Advances in Air Pollution Profiling and Control (17–23). Springer, Singapore

  • Park SJ, Lee CW, Lee S, Lee MJ (2018) Landslide susceptibility mapping and comparison using decision tree models: a case study of Jumunjin Area. Korea Rem Sens 10(10):1545

    Article  Google Scholar 

  • Peethambaran B, Anbalagan R, Kanungo DP, Goswami A, Shihabudheen KV (2020) A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas. CATENA 195:104751

    Article  Google Scholar 

  • Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250

    Article  Google Scholar 

  • Pham BT, Tien Bui D, Prakash I (2017a) Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study. Geotech Geol Eng 35:2597–2611

  • Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2017b) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology 128:255–273

  • Pham BT, Shirzadi A, Bui DT, Prakash I, Dholakia MB (2018) A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: a case study in the Himalayan area. India Int J Sediment Res 33(2):157–170

    Article  Google Scholar 

  • Quinlan JR (1992) November. Learning with continuous classes. In 5th Australian joint conference on artificial intelligence (Vol. 92: 343–348).

  • Rane PR, Vincent S (2021) Landslide susceptibility mapping using machine learning algorithms for Nainital, India. Engineered Science 17:142–155

    Google Scholar 

  • Rawat MS, Uniyal DP, Dobhal R, Joshi V, Rawat BS, Bartwal A, Singh D, Aswal A (2015) Study of landslide hazard zonation in Mandakini Valley, Rudraprayag district, Uttarakhand using remote sensing and GIS. Cur Sci 158–170.

  • Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N (2019) Deep learning and process understanding for data-driven Earth system science. Nat 566(7743):195–204

    Article  Google Scholar 

  • Rousseva S, Banov M, Kolev N (2003) Some aspects of the present status of land degradation in Bulgaria. In: The JRC enlargement action, workshop, pp 149–164

  • Różycka M, Migoń P, Michniewicz A (2017) Topographic Wetness Index and Terrain Ruggedness Index in geomorphic characterisation of landslide terrains, on examples from the Sudetes, SW Poland. Zeitschriftfür Geomorphol Supple Issu 61:61–80

    Article  Google Scholar 

  • Saha A, Saha S (2020) Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: a study on Kurseong region of Darjeeling Himalaya, India. Rem Sens Appl: Soci and Env 19:100323

  • Saha A, Mandal S, Saha S (2020) Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods. SN Appl Sci 2(10):1–21

  • Saha S, Arabameri A, Saha A, Blaschke T, NgoP TT, Nhu VH, Band SS (2021) Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method. Sci Total Environ 764:142928

    Article  Google Scholar 

  • Sahana M, Pham BT, Shukla M, Costache R, Thu DX, Chakrabortty R, Satyam N, Nguyen HD, Phong TV, Le HV, Pal SC (2020) Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier. Geocar Inter 1–25.

  • Sattari MT, Rezazadeh Joudi A, Nahrein F (2014) Monthly rainfall prediction using artificial neural networks and M5 model tree (case study: station of Ahar). Physical Geography Research Quarterly 46(2):247–260

  • Schmidhuber J (2015) Deep learning in neural networks: An overview. Neu Net 2015(61):85–117

    Article  Google Scholar 

  • Segoni S, Pappafico G, Luti T, Catani F (2020) Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization. Landslides: 1–11.

  • Senaviratna NAMR, Cooray TMJA (2019) Diagnosing multicollinearity of logistic regression model. Asi Jo Prob Stat 1–9.

  • Sevgen E, Kocaman S, Nefeslioglu HA, Gokceoglu C (2019) A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression. ANN and Random Forest Sens 19(18):3940

    Google Scholar 

  • Sharma A, Sur U, Singh P, Rai PK, SrivastavaPK (2020) Probabilistic landslide hazard assessment using statistical information value (SIV) and GIS techniques: a case study of Himachal Pradesh, India. Tech.for Dis Risk Manag and Mitig 197–208.

  • Shirani K, Pasandi M, Arabameri A (2018) Landslide susceptibility assessment by dempster–shafer and index of entropy models, Sarkhoun basin, southwestern Iran. Nat Hazards 93(3):1379–1418

    Article  Google Scholar 

  • Siami-Namini S, Tavakoli N, Namin AS (20180 December. A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE Inter. Confer. on Mach. Learn. and Appl.(ICMLA) (1394–1401). IEEE.

  • Singh A, Nautiyal MC, Kunwar RM, Bussmann RW (2017) Ethnomedicinal plants used by local inhabitants of Jakholi block, Rudraprayag district, western Himalaya. India J of Ethno and Ethnome 13(1):1–29

    Google Scholar 

  • Sun D, Wen H, Wang D, Xu J (2020) A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology 362:107201

    Article  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. Measur 169:108370

    Google Scholar 

  • Thiebes B (2012) Landslide analysis and early warning systems: local and regional case study in the Swabian Alb, Germany. Springer Sci. & Bus. Med.

  • Tian Y, Xu C, Hong H, Zhou Q, Wang D (2019) Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event. Geo Nat Hazard and Risk 10(1):1–25

    Article  Google Scholar 

  • Tiwari AK, Suozzi E, Silva C, De Maio M, Zanetti M (2021) Role of integrated approaches in water resources management: Antofagasta Region. Chile Sustain 13(3):1297

    Google Scholar 

  • Tutz G (2021) Ordinal trees and random forests: score-free recursive partitioning and improved ensembles. arXiv preprint arXiv:2102.00415.

  • Van Dao D, Jaafari A, Bayat M, Mafi-Gholami D, Qi C, Moayedi H, Van Phong T, Ly HB, Le TT, Trinh T, Luu C (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA 188:104451

    Article  Google Scholar 

  • Wallach D, Martre P, Liu B, Asseng S, Ewert F, Thorburn PJ, van Ittersum M, Aggarwal PK, Ahmed M, Basso B, Biernath C (2018) Multimodel ensembles improve predictions of crop–environment–management interactions. Glob Chan Bio 24(11):5072–5083

    Article  Google Scholar 

  • Wang Y, Witten IH (1996) Induction of model trees for predicting continuous classes: proc of the poster papers of the European conference on machine learning, university of economics, Faculty of Infor and Statis, Prague

  • Wang LJ, Guo M, Sawada K, Lin J, Zhang J (2016) A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci J 20(1):117–136

    Article  Google Scholar 

  • Wang Y, Wang X, Jian, J (2019) Remote sensing landslide recognition based on convolutional neural network. Math Prob Eng 2019.

  • Wang J, Shi T, Yu D, Teng D, Ge X, Zhang Z, Yang X, Wang H, Wu G (2020) Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: a case study in an arid oasis. NW China Env Poll 266:115412

    Article  Google Scholar 

  • Wang S, Zhang K, van Beek LP, Tian X, Bogaard TA (2020) Physically-based landslide prediction over a large region: scaling low-resolution hydrological model results for high-resolution slope stability assessment. Env Model & Soft 124:104607

    Article  Google Scholar 

  • Wang Z, Liu Q, Liu Y (2020) Mapping landslide susceptibility using machine learning algorithms and GIS: a case study in Shexian County, Anhui Province. China Symmet 12(12):1954

    Article  Google Scholar 

  • Weigand M, Staab J, Wurm M, Taubenböck H (2020) Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Inter Jou of Appl Earth Obser and Geoinfor 88:102065

    Google Scholar 

  • Wen, S. (2018). Automatic tongue contour segmentation using deep learning (Doctoral dissertation, Universitéd' Ottawa/University of Ottawa).

  • Wu Y, Ke Y, Chen Z, Liang S, Zhao H, Hong H (2020) Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. CATENA 187:104396

    Article  Google Scholar 

  • Xiong X, Shi Z, Xiong Y, Peng M, Ma X, Zhang F (2019) Unsaturated slope stability around the Three Gorges Reservoir under various combinations of rainfall and water level fluctuation. Eng Geol 261:105231

    Article  Google Scholar 

  • Yan F, Zhang Q, Ye S, Ren B (2019) A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model. Geomorphology 327:170–187

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR (2021) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region. Saudi Arabia Geosci Front 12(2):639–655

    Article  Google Scholar 

  • Zettler AH, Poisel R, Roth W, Preh A (1999) Slope stability analysis based on the shear reduction technique in 3D. FLAC and numerical modeling in geomechanics (Detournay& Hart). A.A. Balk. Rotter 11–16.

  • Zettler AH, Poisel R, Preh A, Roth W (1999) Stability analysis using finite difference method. In: ISRM Congress (pp. ISRM-9CONGRESS). ISRM

  • Zhang X, Wang J (2018) A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns. Appl Soft Comp 65:478–494

    Article  Google Scholar 

  • Zhang F, Chen W, Liu G, Liang S, Kang C, He F (2012) Relationships between landslide types and topographic attributes in a loess catchment. China Jou of Mount Sci 9(6):742–751

    Article  Google Scholar 

  • Zhang TY, Han L, Zhang H, Zhao YH, Li XA, Zhao L (2019) GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine. Jou Mount Sci 16(6):1275–1288

    Article  Google Scholar 

  • Zhang W, Wu C, Zhong H, Li Y, Wang L (2021) Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci Front 12(1):469–477

    Article  Google Scholar 

  • Zhao X, Chen W (2020) GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques. Appl Sci 10(1):16

    Article  Google Scholar 

  • Zhu N, Liu X, Liu Z, Hu K, Wang Y, Tan J, Huang M, Zhu Q, Ji X, Jiang Y, Guo Y (2018) Deep learning for smart agriculture: concepts, tools, applications, and opportunities. Int J Agric Biol Eng 11(4):32–44

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, University of Gour Banga, Malda-732103, Malda, West Bengal, India

    Sunil Saha, Anik Saha & Barnali Kundu

  2. School of Earth Sciences and Resources, China University of Geosciences, Beijing, Beijing, China

    M. Santosh

  3. Department of Earth Sciences, University of Adelaide, Adelaide, South Australia, Australia

    M. Santosh

  4. Department of Civil Engineering, Delhi Technological University, Delhi, India

    Raju Sarkar

  5. Department of Geography, Nistarini College, Purulia, West Bengal, India

    Tusar Kanti Hembram

Authors
  1. Sunil Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anik Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Barnali Kundu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Raju Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tusar Kanti Hembram
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Sunil Saha: methodology, format analysis, original draft preparation, review and editing; Anik Saha: methodology, format analysis, investigation, original draft preparation, software; M. Santosh: review and editing; Barnali Kundu: methodology, format analysis, investigation, original draft preparation, software; Raju Sarkar: review and editing; Tusar Kanti Hembram: methodology, format analysis, draft preparation, software.

Corresponding author

Correspondence to Sunil Saha.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saha, S., Saha, A., Santosh, M. et al. Integrating deep learning neural network and M5P with conventional statistical models for landslide susceptibility modelling. Bull Eng Geol Environ 83, 12 (2024). https://doi.org/10.1007/s10064-023-03498-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10064-023-03498-5

Keywords

Search

Navigation