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Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region

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Abstract

This research aims to generate a landslide susceptibility map (LSM) for the Bhagirathi river basin located in the Tehri Garhwal district of Uttarakhand state in India. For this study, we incorporated and utilized machine learning novel ensemble models, namely: Generalize Linear Model (GLM), Random Forest (RF), Convolutional Neural Network (CNN), GLM-RF, CNN-RF, CNN-GLM and CNN-GLM-RF. The above-specified ensemble models were incorporated for the preparation of LSM. A total of 171 landslide locations were chosen and studied for the preparation of landslide inventory. From the landslide inventory, 70% of landslides were utilized for the training purpose, whereas the rest, 30%, were used for the validation purpose. In the ongoing research study, a total of 17 landslide conditioning factors (LCFs) were utilized, and the analysis for multi-collinearity was performed by tolerance (TOL) as well as variance inflation factor (VIF) methods. These LSM were then validated through receiver operating characteristic curve (ROC), mean absolute error (MAE), root mean square error (RMSE) and Chi-Square methods. Finally, the outcomes of the analysis, as well as validation models, show that the CNN model proves to be the most effective for predicting landslide susceptibility in the study area. The study found that the methods incorporated for the area under consideration can be applied to the regions having similar geo-environmental factors globally.

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References

  • Altaf S, Meraj G, Romshoo SA (2014) Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed. Environ Monit Assess 186(12):8391–8412

    Google Scholar 

  • Atkinson P, Jiskoot H, Massari R, Murray T (1998) Generalized linear modelling in geomorphology. Earth Surface Process Landforms 23(13):1185–1195

    Google Scholar 

  • 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

    CAS  Google Scholar 

  • Bornaetxea T, Rossi M, Marchesini I, Alvioli M (2018) Effective surveyed area and its role in statistical landslide susceptibility assessments. Nat Hazard 18(9):2455–2469

    Google Scholar 

  • Breiman L, Cutler A (2004) Random forest-manual. Online: http://www.stat.berkeley.edu/breiman/RandomForests/cc_manual.htm

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Google Scholar 

  • Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Process Landforms 16(5):427–445

    Google Scholar 

  • Chen W, Pourghasemi HR, Kornejady A, Xie X (2019a) GIS-based landslide susceptibility evaluation using certainty factor and index of entropy ensembled with alternating decision tree models. In: Natural hazards GIS-based spatial modeling using data mining techniques, pp 225–251. Springer, Cham.

  • Chen W, Shahabi H, Shirzadi A, Li T, Guo C, Hong H, Li W, Pan D, Hui J, Ma M, Xi M (2018a) A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int 33(12):1398–1420

    Google Scholar 

  • Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H (2017) GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Hazards Risk 8(2):950–973

    Google Scholar 

  • Chen W, Zhang S, Li R, Shahabi H (2018b) Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Sci Total Environ 10(644):1006–1018

    Google Scholar 

  • Chen W, Zhao X, Shahabi H, Shirzadi A, Khosravi K, Chai H, Zhang S, Zhang L, Ma J, Chen Y, Wang X (2019b) Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree. Geocarto Int 34(11):1177–1201

    Google Scholar 

  • Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA 196:104833

    Google Scholar 

  • Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472

    Google Scholar 

  • Darminto MR, Chu HJ (2019) Mapping landslide release area using random forest model. Earth Eviron Sci 389(1):012038

    Google Scholar 

  • Darmintoa MR, Widodob A, Alfatinahc A, Chuc HJ (2021) High-resolution landslide susceptibility map generation using machine learning (Case Study in Pacitan, Indonesia). Int J Adv Sci Eng Inf Technol 11(1):369–379

    Google Scholar 

  • Depicker A, Jacobs L, Delvaux D, Havenith HB, Mateso JC, Govers G, Dewitte O (2020) The added value of a regional landslide susceptibility assessment: the western branch of the East African Rift. Geomorphology 353:106886

    Google Scholar 

  • Faizi S, Rashid T, Sałabun W, Zafar S, Wątróbski J (2018) Decision making with uncertainty using hesitant fuzzy sets. Int J Fuzzy Syst 20(1):93–103

    Google Scholar 

  • Falaschi F, Giacomelli F, Federici PR, Puccinelli A, Avanzi GA, Pochini A, Ribolini A (2009) Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards 50(3):551–569

    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

    Google Scholar 

  • Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazard Earth Syst Sci 18 (8):2161-2181

  • Gaprindashvili G, Van Westen CJ (2016) Generation of a national landslide hazard and risk map for the country of Georgia. Nat Hazards 80(1):69–101

    Google Scholar 

  • Gariano SL, Sarkar R, Dikshit A, Dorji K, Brunetti MT, Peruccacci S, Melillo M (2019) Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag. Bhutan Bull Eng Geol Environ 78(6):4325–4332

    Google Scholar 

  • Geertsema M, Highland L, Vaugeouis L (2009) Environmental impact of landslides. In: Landslides–disaster risk reduction. Springer, Berlin, pp 589–607

  • Ghosh JK, Bhattacharya D (2010) Knowledge-based landslide susceptibility zonation system. J Comput Civ Eng 24(4):325–334

    Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth-Sci Rev 112(1–2):42–66

    Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184

    Google Scholar 

  • Haque U, Da Silva PF, Devoli G, Pilz J, Zhao B, Khaloua A, Wilopo W, Andersen P, Lu P, Lee J, Yamamoto T (2019) The human cost of global warming: deadly landslides and their triggers (1995–2014). Sci Total Environ 10(682):673–684

    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. Geomat Nat Hazards Risk 12(1):794–828

    Google Scholar 

  • Kalantar B, Ueda N, Saeidi V, Ahmadi K, Halin AA, Shabani F (2020) Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data. Remote Sens 12(11):1737

    Google Scholar 

  • Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85(3–4):347–366

    Google Scholar 

  • Kavzoglu T, Colkesen I, Sahin EK (2019) Machine learning techniques in landslide susceptibility mapping: a survey and a case study. Landslides, pp 283–301

  • Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly HB, Gróf G, Ho HL, Hong H (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323

    Google Scholar 

  • Kim JC, Lee S, Jung HS, Lee S (2018) Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang. Korea Geocarto Int 33(9):1000–1015

    Google Scholar 

  • Klose M, Damm B, Kreuzer T (2016) Landslide hazards and systems analysis: a Central European perspective. In: EGU general assembly conference abstracts, pp EPSC2016–11344

  • Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271–286

    Google Scholar 

  • Kuriakose SL, Van Beek LP, Van Westen CJ (2009) Parameterizing a physically based shallow landslide model in a data poor region. Earth Surface Process Landforms 34(6):867–881

    Google Scholar 

  • LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(436–444):68

    Google Scholar 

  • Lee S, Kim JC, Jung HS, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city. Korea Geomat Nat Hazards Risk 8(2):1185–1203

    Google Scholar 

  • Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3–4):289–302

    Google Scholar 

  • Lei X, Chen W, Pham BT (2020) Performance evaluation of gis-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS Int J Geo-Inform 9(7):443

    Google Scholar 

  • Liang Z, Wang C, Duan Z, Liu H, Liu X, Ullah Jan Khan K (2021) A hybrid model consisting of supervised and unsupervised learning for landslide susceptibility mapping. Remote Sens 13(8):1464

    Google Scholar 

  • Liu W, Yang Z, He S (2021) Modeling the landslide-generated debris flow from formation to propagation and run-out by considering the effect of vegetation. Landslides 18(1):43–58

    Google Scholar 

  • Liu X, Miao C (2018) Large-scale assessment of landslide hazard, vulnerability and risk in China. Geomat Nat Hazards Risk 9(1):1037–1052

    Google Scholar 

  • López P, Qüense J, Henríquez C, Martínez C (2021) Applicability of spatial prediction models for landslide susceptibility in land-use zoning instruments: a guideline in a coastal settlement in South-Central Chile. Geocarto Int, pp 1–20

  • 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

    Google Scholar 

  • Mandal S, Mandal K (2018) Bivariate statistical index for landslide susceptibility mapping in the Rorachu river basin of eastern Sikkim Himalaya. India Spat Inform Res 26(1):59–75

    Google Scholar 

  • Maxwell AE, Pourmohammadi P, Poyner JD (2020) Mapping the topographic features of mining-related valley fills using mask R-CNN deep learning and digital elevation data. Remote Sens 12(3):547

    Google Scholar 

  • Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci Rev 3:103225

    Google Scholar 

  • Ngo PT, 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 Front 12(2):505–519

    Google Scholar 

  • Nikita E (2014) The use of generalized linear models and generalized estimating equations in bioarchaeological studies. Am J Phys Anthropol 153(3):473–483

    Google Scholar 

  • Oh HJ, Kadavi PR, Lee CW, Lee S (2018) Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models. Geomat Nat Hazards Risk 9(1):1053–1070

    Google Scholar 

  • Pham BT, Jaafari A, Prakash I, Bui DT (2019a) A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bull Eng Geol Environ 78(4):2865–2886

    Google Scholar 

  • Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Tran HT, Le TM, Van Phong T, Khoi DK, Shirzadi A, Bui DT (2020a) A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int 35(12):1267–1292

    Google Scholar 

  • Pham BT, Prakash I, Singh SK, Shirzadi A, Shahabi H, Bui DT (2019b) Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches. CATENA 175:203–218

    Google Scholar 

  • Pham V, Nguyen NV, Dang T (2020b) Scagcnn: estimating visual characterizations of 2d scatterplots via convolution neural network. In: Proceedings of the 11th international conference on advances in information technology, pp 1–9

  • Pham BT, Prakash I (2019b) Evaluation and comparison of LogitBoost Ensemble, Fisher’s Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping. Geocarto Int 34(3):316–333

    Google Scholar 

  • Pham BT, Van Dao D, Acharya TD, Van Phong T, Costache R, Van Le H, Prakash I (2021) Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis. Environ Earth Sci 80(20):1–13

    Google Scholar 

  • 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 Hazard 63(2):965–996

    Google Scholar 

  • Pourghasemi HR, Yousefi S, Kornejady A, Cerdà A (2017) Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci Total Environ 609:764–775

    CAS  Google Scholar 

  • Raman R, Punia M (2012) The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley, Western Himalaya, India. Georisk Assess Manage Risk Engine Syst Geohaz 6(3):145–161

    Google Scholar 

  • Rasyid AR, Bhandary NP, Yatabe R (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain. Indonesia Geoenviron Disasters 3(1):1–6

    Google Scholar 

  • Regmi AD, Yoshida K, Dhital MR, Pradhan B (2014) Weathering and mineralogical variation in gneissic rocks and their effect in Sangrumba Landslide. East Nepal Environ Earth Sci 71(6):2711–2727

    CAS  Google Scholar 

  • Roy J, Saha S, Arabameri A, Blaschke T, Bui DT (2019) A novel ensemble approach for landslide susceptibility mapping (LSM) in Darjeeling and Kalimpong districts, West Bengal. India Remote Sens 11(23):2866

    Google Scholar 

  • Saha A, Mandal S, Saha S (2020a) 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

    Google Scholar 

  • Saha S, Arabameri A, Saha A, Blaschke T, Ngo PT, Nhu VH, Band SS (2021a) 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

    CAS  Google Scholar 

  • Saha S, Saha A, Hembram TK, Pradhan B, Alamri AM (2020b) Evaluating the performance of individual and novel ensemble of machine learning and statistical models for landslide susceptibility assessment at Rudraprayag District of Garhwal Himalaya. Appl Sci 10(11):3772.

  • Saha S, Roy J, Hembram TK, Pradhan B, Dikshit A, Abdul Maulud KN, Alamri AM (2021b) Comparison between deep learning and tree-based machine learning approaches for landslide susceptibility mapping. Water 13(19):2664

    Google Scholar 

  • Sahin EK, Colkesen I, Acmali SS, Akgun A, Aydinoglu AC (2020) Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack. Comput Geosci 144:104592

    Google Scholar 

  • Sameen MI, Sarkar R, Pradhan B, Drukpa D, Alamri AM, Park HJ (2020) Landslide spatial modelling using unsupervised factor optimization and regularised greedy forests. Comput Geosci 134:104336

    Google Scholar 

  • Shahabi H, Shirzadi A, Ghaderi K, Omidvar E, Al-Ansari N, Clague JJ, Geertsema M, Khosravi K, Amini A, Bahrami S, Rahmati O (2020) Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens 12(2):266

    Google Scholar 

  • Sharma LP, Patel N, Ghose MK, Debnath P (2014) Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India. Geocart Int 29(2):128–146

    Google Scholar 

  • Shirzadi A, Bui DT, Pham BT, Solaimani K, Chapi K, Kavian A, Shahabi H, Revhaug I (2017) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Ear Sci 76(2):60

    Google Scholar 

  • Simard PY, Steinkraus D, Platt JC (2003) Best practices for convolutional neural networks applied to visual document 696 analysis. In Icdar 2003:3

    Google Scholar 

  • Song KY, Oh HJ, Choi J, Park I, Lee C, Lee S (2012) Prediction of landslides using ASTER imagery and data mining models. Adv Space Res 49(5):978–993

    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

    Google Scholar 

  • Tallarida RJ, Murray RB (1987) Chi-square test. In: Manual of pharmacologic calculations. Springer, New York, pp 140–142

  • Tiwari A, Shoab M, Dixit A (2021) GIS-based forest fire susceptibility modeling in Pauri Garhwal, India: a comparative assessment of frequency ratio, analytic hierarchy process and fuzzy modeling techniques. Nat Hazards 105(2):1189–1230

    Google Scholar 

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

    Google Scholar 

  • Yesilnacar E, Topal TA (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266

    Google Scholar 

  • Yi Y, Zhang Z, Zhang W, Jia H, Zhang J (2020) Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: a case study in Jiuzhaigou region. CATENA 2020(195):104851. https://doi.org/10.1016/j.catena.104851

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region. Saudi Arabia Landslides 13(5):839–856

    Google Scholar 

  • Zhu L, Huang L, Fan L, Huang J, Huang F, Chen J, Zhang Z, Wang Y (2020) Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network. Sensors 20(6):1576

    Google Scholar 

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Authors and Affiliations

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

    Sunil Saha, Anik Saha & Kanu Mandal

  2. Department of Geography, Nistarini Women’s College, Purulia, West Bengal, India

    Tusar Kanti Hembram

  3. Department of Civil Engineering, Delhi Technological University, Delhi, 110 042, India

    Raju Sarkar & Dhruv Bhardwaj

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  1. Sunil Saha
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Saha, S., Saha, A., Hembram, T.K. et al. Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region. Stoch Environ Res Risk Assess 36, 3597–3616 (2022). https://doi.org/10.1007/s00477-022-02212-3

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