Abstract
Improving the predictive accuracy of models based on machine learning techniques for assessing landslide susceptibility is an area that attracts researchers’ attention. In this study, we applied, evaluated, and compared three machine learning models: random forest (RF), artificial neuron network (ANN), and decision tree (DT), for spatial prediction of landslide susceptibility in the N’fis watershed located in the High Atlas (Morocco). A database from an inventory of 156 historical landslides, randomly split into a training (70%) and validation (30%) set, was combined with thematic maps of fourteen causal factors (elevation, slope angle, slope aspect, curvature, lithology, land use, distance to faults, distance to rivers, distance to roads, topographic position index, topographic wetness index, precipitation, soil type, and normalized vegetation index) to build and validate the models. The performance of these models was evaluated using several statistical indices and the ROC curve method. The results show that all three models have worked well. The analysis of these results indicates that the RF model is the best performing (AUC = 96.75%), followed by ANN (AUC = 94.40%), and finally DT (AUC = 85.65%).
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References
Al-Najjar HH, Pradhan B (2021) Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks. Geosci Front 12(2):625–637
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
Amaya A, Algouti A, Algouti A, El Aaggad N (2014) Mapping of mass movements hazard in the N’fis watershed, High Atlas, Morocco. Int J Innov Appl Stud 8(2):645
Bragagnolo L, da Silva RV, Grzybowski JMV (2020) Landslide susceptibility mapping with r landslide: a free open-source GIS-integrated tool based on artificial neural networks. Environ Modell Software 123:104565
Capparelli G, Tiranti D (2010) Application of the MoniFLaIR early warning system for rainfall-induced landslides in Piedmont region (Italy). Landslides 7(4):401–410
Chen T, Niu R, Jia X (2016a) A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environ Earth Sci 75(10):867
Chen W, Chai H, Sun X, Wang Q, Ding X, Hong H (2016b) A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping. Arabian J Geosci 9(3):204
Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Duan Z (2018a) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135
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 644:1006–1018
Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63(2):397–406
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. https://doi.org/10.3390/app10072466
Dikshit A, Pradhan B, Alamri AM (2021) Pathways and challenges of the application of artificial intelligence to geohazards modelling. Gondwana Res 100:290–301
Elmoulat M, Brahim LA, Mastere M, Jemmah AI (2015) Mapping of mass movements susceptibility in the Zoumi Region using satellite image and GIS technology (Moroccan Rif). Int J Sci Eng Res 6(2):210–217
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
Fang Z, Wang Y, Peng L, Hong H (2021) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geog Inf Sci 35(2):321–347
Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazard Earth Sys 18(8):2161–2181
Garosi Y, Sheklabadi M, Conoscenti C, Pourghasemi HR, Van Oost K (2019) Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Sci Total Environ 664:1117–1132
Gourfi A, Daoudi L, Rhoujjati A, Benkaddour A, Fagel N (2020) Use of bathymetry and clay mineralogy of reservoir sediment to reconstruct the recent changes in sediment yields from a mountain catchment in the Western High Atlas region. Morocco Catena 191:104560
Grelle G, Soriano M, Revellino P, Guerriero L, Anderson MG, Diambra A, Guadagno FM (2014) Space–time prediction of rainfall-induced shallow landslides through a combined probabilistic/deterministic approach, optimized for initial water table conditions. Bull Eng Geol Environ 73(3):877–890
Guo C, Montgomery DR, Zhang Y, Wang K, Yang Z (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110
Hadji R, Limani Y, Baghem M, Demdoum A (2013) Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling: a case study of Souk Ahras region, NE Algeria. Quat Int 302:224–237
Hollard H, Choubert G, Bronner G, Marchand J, Sougy J (1985) Carte géologique du Maroc, scale 1: 1,000,000. Serv Carte géol Maroc, 260(2)
Hong H, Pourghasemi HR, Pourtaghi ZS (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118
Jaafari A, Panahi M, Pham BT, Shahabi H, Bui DT, Rezaie F, Lee S (2019) Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. CATENA 175:430–445
Jia K, Liang S, Zhang L, Wei X, Yao Y, Xie X (2014) Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data. Int J Appl Earth Obs Geoinf 33:32–38
Kanungo D, Arora M, Gupta R, Sarkar S (2008) Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas. Landslides 5:407–416
Kausar N, Majid A (2016) Random forest-based scheme using feature and decision levels information for multi-focus image fusion. Pattern Anal Appl 19(1):221–236
Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network(ANN). Geomorphology 113(1–2):97–109
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
Kumar R, Anbalagan R (2015) Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS. J Earth Syst Sci 124(2):431–448
Kumar D, Thakur M, Dubey CS, Shukla DP (2017) Landslide susceptibility mapping & prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology 295:115–125
Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 159–174
Manchar N, Benabbas C, Hadji R, Bouaicha F, Grecu F (2018) Landslide susceptibility assessment in Constantine region (NE Algeria) by means of statistical models. Studia Geotechnica Et Mechanica 40(3):208–219
Mandal S, Mandal K (2018) Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya. India Modeling Earth Syst Environ 4:69–88
Mathieu P (2002) Caractérisation des sols et de leurs propriétés hydrodynamiques pourla modélisation hydrologique en milieu semi-aride, Bassin versant du Tensift–Maroc, Mémoire defind’étude ENSAM DAA « Physique des surfaces naturelles et géniehydrologique » (ENSAR) Avril 2002-Septembre 2002
Meinhardt M, Fink M, Tünschel H (2015) Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80–97
Merkhi A, Laftouhi NE, Soulaimani A, Fniguire F (2015) Quantification et évaluation de l’érosion hydrique en utilisant le modèle RUSLE et déposition intégrés dans un SIG. Application dans le bassin versant n’fis dans le haut atlas de Marrakech (Maroc). Eur Sci J 11(29):340–356
Michard A, Hœpffner C, Soulaimani A, Baidder L (2008) The variscan belt. In Continental evolution: The geology of Morocco (pp. 65–132). Springer, Berlin, Heidelberg
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
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
Park S, Hamm SY, Kim J (2019) Performance evaluation of the GIS-based data-mining techniques decision tree, random forest, and rotation forest for landslide susceptibility modeling. Sustainability 11(20):5659
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). EnvironModell Software 84:240–250
Pham BT, Nguyen-Thoi T, Qi C, Van Phong T, Dou J, Ho LS, Prakash I (2020) Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA 195:104805
Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province. Iran Environ Earth Sci 75(3):185
Zhao Y, Zhang Y (2008) Comparison of decision tree methods for finding active objects. Adv Space Res 41(12):1955–1959
Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? Catena 162:177–192
Pourghasemi HR, Moradi HR, Aghda SF (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazard 69(1):749–779
Pradhan B, Sameen MI, Al-Najjar HAH, Sheng D, Alamri AM, Park HJ (2021) A meta-learning approach of optimisation for spatial prediction of landslides. Remote Sensing 13(22):4521
Rana N, Bisht P, Bagri DS, Wasson RJ, Sundriyal Y (2017) Identification of landslide-prone zones in the geomorphically and climatically sensitive Mandakini Valley, (central Himalaya), for disaster governance using the weights of evidence method. Geomorphology 284:41–52
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):19
Salvatici T, Tofani V, Rossi G, D’Ambrosio M, Tacconi Stefanelli C, Casagli MEB, N, (2018) Application of a physically based model to forecast shallow landslides at a regional scale. Nat Hazards Earth Syst Sci 18(7):1919–1935
Sandric I, Ionita C, Chitu Z, Dardala M, Irimia R, Furtuna FT (2019) Using CUDA to accelerate uncertainty propagation modelling for landslide susceptibility assessment. Environ Modell Softwaree 115:176–186
Santoso AM, Phoon KK, Quek ST (2011) Effects of soil spatial variability on rainfall-induced landslides. Comput Struct 89(11–12):893–900
Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70
Song Y, Niu R, Xu S, Ye R, Peng L, Guo T, Chen T (2019) Landslide susceptibility mapping based on weighted gradient boosting decision tree in Wanzhou section of the Three Gorges Reservoir Area (China). ISPRS Int J Geo-Inf 8(1):4
Tsangaratos P, Ilia I (2016) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection. Greece Landslides 13(2):305–320
Witten IH, Frank E (2002) Data mining: practical machine learning tools and techniques with Java implementations. ACM SIGMOD Rec 31(1):76–77
Yeon YK, Han JG, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116(3–4):274–283
Yilmaz I (2009) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68(3):297–306
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
Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area. Korea. Environ Earth Sci 68(5):1443–1464
Yusof NM, Pradhan B (2014) Landslide susceptibility mapping along PLUS expressways in Malaysia using probabilistic based model in GIS. Iop C Ser Earth Env 1(20):012031
Zhang K, Wu X, Niu R, Yang K, Zhao L (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area. China Environ Earth Sci 76(11):1–20
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Ait Naceur, H., Igmoulan, B., Namous, M. et al. A comparative study of different machine learning methods coupled with GIS for landslide susceptibility assessment: a case study of N’fis basin, Marrakesh High Atlas (Morocco). Arab J Geosci 15, 1100 (2022). https://doi.org/10.1007/s12517-022-10349-2
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DOI: https://doi.org/10.1007/s12517-022-10349-2