Volume 11 Issue 3
Aug.  2020
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Yacine Achour, Hamid Reza Pourghasemi. How do machine learning techniques help in increasing accuracy oflandslide susceptibility maps?[J]. Geoscience Frontiers, 2020, (3): 871-883. doi: 10.1016/j.gsf.2019.10.001
Citation: Yacine Achour, Hamid Reza Pourghasemi. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?[J]. Geoscience Frontiers, 2020, (3): 871-883. doi: 10.1016/j.gsf.2019.10.001

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

doi: 10.1016/j.gsf.2019.10.001
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The authors would like to thank the National Highways Agency (ANA) of Algeria for providing data used in the current study.

  • Received Date: 2019-07-29
  • Rev Recd Date: 2019-08-31
  • Publish Date: 2020-08-26
  • Landslides are abundant in mountainous regions. They are responsible for substantial damages and losses in those areas. The A1 Highway, which is an important road in Algeria, was sometimes constructed in mountainous and/or semi-mountainous areas. Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results. In this research, we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor. To do this, an important section at Ain Bouziane (NE, Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods, namely, random forest (RF), support vector machine (SVM), and boosted regression tree (BRT). First, an inventory map and nine input factors were prepared for landslide susceptibility mapping (LSM) analyses. The three models were constructed to find the most susceptible areas to this phenomenon. The results were assessed by calculating the receiver operating characteristic (ROC) curve, the standard error (Std. error), and the confidence interval (CI) at 95%. The RF model reached the highest predictive accuracy (AUC ¼ 97.2%) comparatively to the other models. The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section. In addition, their application gives an improvement of the accuracy of LSMs near the road corridor. The machine learning models may become an important prediction tool that will identify landslide alleviation actions.
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  • [1]
    Achour, Y., Boumezbeur, A., Hadji, R., Chouabbi, A., Cavaleiro, V., Bendaoud, E.A., 2017. Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arabian J. Geosci. 10 (8), 194.
    [2]
    Achour, Y., Garçia, S., Cavaleiro, V., 2018. GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Z^ezere River basin and its surrounding area, Northwest Covilh~a, Portugal. Arabian J. Geosci. 11 (18), 550.
    [3]
    Ada, M., San, B.T., 2018. Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey. Nat. Hazards 90 (1), 237–263.
    [4]
    Ali, S.A., Khatun, R., Ahmad, A., Ahmad, S.N., 2019. Application of GIS-Based Analytic Hierarchy Process and Frequency Ratio Model to Flood Vulnerable Mapping and Risk Area Estimation at Sundarban Region. Modeling Earth Systems and Environment, India, pp. 1–20.
    [5]
    Arabameri, A., Pourghasemi, H.R., Yamani, M., 2017. Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ. Earth Sci. 76 (24), 832.
    [6]
    Arabameri, A., Pradhan, B., Rezaei, K., Conoscenti, C., 2019a. Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques. Catena 180, 282–297.
    [7]
    Arabameri, A., Pradhan, B., Rezaei, K., Sohrabi, M., Kalantari, Z., 2019b. 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 (3), 595–618.
    [8]
    Arabameri, A., Pradhan, B., Rezaei, K., Lee, S., Sohrabi, M., 2019c. An ensemble model for landslide susceptibility mapping in a forested area. Geocarto Int. 1–25.
    [9]
    Auret, L., Aldrich, C., 2012. Interpretation of nonlinear relationships between process variables by use of random forests. Miner. Eng. 35, 27–42.
    [10]
    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 (1–2), 15–31.
    [11]
    Billah, M., Arcos Gonz alez, P., Castro Delgado, R., 2019. Patterns of mortality caused by natural disasters and human development level: a south Asian analysis. Indian Journal of Public Health Research & Development 10 (2).
    [12]
    Bortoloti, F.D., Junior, R.C., Araújo, L.C., de Morais, M.G.B., 2015. Preliminary landslide susceptibility zonation using GIS-based fuzzy logic in Vit oria, Brazil. Environ. Earth Sci. 74 (3), 2125–2141.
    [13]
    Brenning, A., 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat. Hazards Earth Syst. Sci. 5 (6), 853–862.
    [14]
    Breiman, L., Friedman, J., Olshen, R., Stone, C., 1984. Classification and regression trees. Wadsworth Int. Group 37 (15), 237–251.
    [15]
    Breiman, L., 2001. Random forests. Mach. Learn. 45 (1), 5–32.
    [16]
    Briassoulis, H., 2019. Combating land degradation and desertification: the land-use planning quandary. Land 8 (2), 27.
    [17]
    Budimir, M.E.A., Atkinson, P.M., Lewis, H.G., 2015. A systematic review of landslide probability mapping using logistic regression. Landslides 12 (3), 419–436.
    [18]
    Bui, D.T., Tuan, T.A., Hoang, N.D., Thanh, N.Q., Nguyen, D.B., Van Liem, N., Pradhan, B., 2017. Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14 (2), 447–458.
    [19]
    Calle, M.L., Urrea, V., 2010. Letter to the editor: stability of random forest importance measures. Briefings Bioinf. 12 (1), 86–89.
    [20]
    Can, A., Dagdelenler, G., Ercanoglu, M., Sonmez, H., 2019. Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms. Bull. Eng. Geol. Environ. 78 (1), 89–102.
    [21]
    Cerd a, A., Rodrigo-Comino, J., Gim enez-Morera, A., Keesstra, S.D., 2018. Hydrological and erosional impact and farmer’s perception on catch crops and weeds in citrus organic farming in Canyoles river watershed, Eastern Spain. Agric. Ecosyst. Environ. 258, 49–58.
    [22]
    Chen, W., Pourghasemi, H.R., Kornejady, A., Xie, X., 2019. 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. Springer, Cham, pp. 225–251.
    [23]
    Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., Mosavi, A., 2019. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci. Total Environ. 651, 2087–2096.
    [24]
    Colkesen, I., Sahin, E.K., Kavzoglu, T., 2016. Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J. Afr. Earth Sci. 118, 53–64.
    [25]
    Di Prima, S., Rodrigo-Comino, J., Novara, A., Iovino, M., Pirastru, M., Keesstra, S., Cerd a, A., 2018. Soil physical quality of citrus orchards under tillage, herbicide, and organic managements. Pedosphere 28 (3), 463–477.
    [26]
    Dou, J., Yunus, A.P., Bui, D.T., Merghadi, A., Sahana, M., Zhu, Z., Chen, C.W., Khosravi, K., Yang, Y., Pham, B.T., 2019. 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.
    [27]
    Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V., 1997. Support vector regression machines. In: Advances in Neural Information Processing Systems, pp. 155–161.
    [28]
    Duman, T.Y., Can, T., Gokceoglu, C., Nefeslioglu, H.A., Sonmez, H., 2006. Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ. Geol. 51 (2), 241–256.
    [29]
    Elith, J., Leathwick, J.R., Hastie, T., 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77 (4), 802–813.
    [30]
    Fiorucci, F., Ardizzone, F., Mondini, A.C., Viero, A., Guzzetti, F., 2019. Visual interpretation of stereoscopic NDVI satellite images to map rainfall-induced landslides. Landslides 16 (1), 165–174.
    [31]
    Friedman, J.H., 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38 (4), 367–378.
    [32]
    Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., Reichenbach, P., 2008. Comparing landslide inventory maps. Geomorphology 94 (3–4), 268–289.
    [33]
    Gayen, A., Pourghasemi, H.R., Saha, S., Keesstra, S., Bai, S., 2019. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Sci. Total Environ. 668, 124–138.
    [34]
    Gholami, M., Ghachkanlu, E.N., Khosravi, K., Pirasteh, S., 2019. Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. Journal of Earth System Science 128 (2), 42.
    [35]
    Gislason, P.O., Benediktsson, J.A., Sveinsson, J.R., 2006. Random forests for land cover classification. Pattern Recognit. Lett. 27 (4), 294–300.
    [36]
    Goetz, J.N., Brenning, A., Petschko, H., Leopold, P., 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput. Geosci. 81, 1–11.
    [37]
    Gupta, R.P., Joshi, B.C., 1990. Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment, Himalayas. Eng. Geol. 28 (1–2), 119–131.
    [38]
    Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., Ardizzone, F., 2005. Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72 (1–4), 272–299.
    [39]
    Hadji, R., errahmane Boumazbeur, A., Limani, Y., Baghem, M., el Madjid Chouabi, A., 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.
    [40]
    Hadji, R., Achour, Y., Hamed, Y., 2017. Using GIS and RS for slope movement susceptibility mapping: comparing AHP, LI and LR methods for the Oued Mellah Basin, NE Algeria. In: Euro-Mediterranean Conference for Environmental Integration. Springer, Cham, pp. 1853–1856.
    [41]
    Hong, H., Pradhan, B., Xu, C., Bui, D.T., 2015. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133, 266–281.
    [42]
    Hutchinson, M.F., Gallant, J.C., 2000. Digital Elevation Models and Representation of Terrain Shape.
    [43]
    Jaafari, A., Pourghasemi, H.R., 2019. Factors influencing regional-scale wildfire probability in Iran: an application of random forest and support vector machine. In: Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier, pp. 607–619.
    [44]
    Kalantar, B., Pradhan, B., Naghibi, S.A., Motevalli, A., Mansor, S., 2018. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Nat. Hazards Risk 9 (1), 49–69.
    [45]
    Kalantari, Z., Ferreira, C.S.S., Keesstra, S., Destouni, G., 2018. Nature-based solutions for flood-drought risk mitigation in vulnerable urbanizing parts of East-Africa. Current Opinion in Environmental Science & Health 5, 73–78.
    [46]
    Karim, Z., Hadji, R., Hamed, Y., 2019. GIS-based approaches for the landslide susceptibility prediction in Setif Region (NE Algeria). Geotech. Geol. Eng. 37 (1), 359–374.
    [47]
    Keesstra, S.D., Bouma, J., Wallinga, J., Tittonell, P., Smith, P., Cerd a, A., Montanarella, L., Quinton, J.N., Pachepsky, Y., van der Putten, W.H., Bardgett, R.D., Moolenaar, S., Mol, G., Jansen, B., Fresco, L.O., 2016. The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil, 2, 111–128.
    [48]
    Keesstra, S., Mol, G., de Leeuw, J., Okx, J., de Cleen, M., Visser, S., 2018. Soil-related sustainable development goals: four concepts to make land degradation neutrality and restoration work. Land 7 (4), 133.
    [49]
    Khosravi, K., Shahabi, H., Pham, B.T., Adamawoski, J., Shirzadi, A., Pradhan, B., Hong, H., 2019. A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J. Hydrol.
    [50]
    Kumar, D., Thakur, M., Dubey, C.S., Shukla, D.P., 2017. Landslide susceptibility mapping & prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology 295, 115–125.
    [51]
    Lee, M.J., Park, I., Lee, S., 2015. Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea. Environ. Earth Sci. 74 (1), 413–429.
    [52]
    Mandal, S., Mondal, S., 2019. Knowledge-driven statistical approach for landslide susceptibility assessment using GIS and fuzzy logic (FL) approach. In: Statistical Approaches for Landslide Susceptibility Assessment and Prediction. Springer, Cham, pp. 163–180.
    [53]
    Marjanovi c, M., Kova cevi c, M., Bajat, B., Vo zenílek, V., 2011. Landslide susceptibility assessment using SVM machine learning algorithm. Eng. Geol. 123 (3), 225–234.
    [54]
    Mohammady, M., Pourghasemi, H.R., Amiri, M., 2019. Land subsidence susceptibility assessment using random forest machine learning algorithm. Environ. Earth Sci. 78 (16), 503.
    [55]
    Moore, I.D., Grayson, R.B., Ladson, A.R., 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol. Process. 5 (1), 3–30.
    [56]
    Moore, I.D., Grayson, R.B., 1991. Terrain-based catchment partitioning and runoff prediction using vector elevation data. Water Resour. Res. 27 (6), 1177–1191.
    [57]
    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.
    [58]
    Pham, B.T., Prakash, I., Bui, D.T., 2018. Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology 303, 256–270.
    [59]
    Pourghasemi, H.R., 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.
    [60]
    Pourghasemi, H., Pradhan, B., Gokceoglu, C., Moezzi, K.D., 2013a. A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics, Nat. Hazards Risk 4 (2), 93–118.
    [61]
    Pourghasemi, H.R., Jirandeh, A.G., Pradhan, B., Xu, C., Gokceoglu, C., 2013b. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science 122 (2), 349–369.
    [62]
    Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., Mohammadi, M., Moradi, H.R., 2013c. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian J. Geosci. 6 (7), 2351–2365.
    [63]
    Pourghasemi, H.R., Kerle, N., 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ. Earth Sci. 75 (3), 185.
    [64]
    Pourghasemi, H.R., Rahmati, O., 2018. Prediction of the landslides susceptibility: which algorithm, which precision? Catena 162, 177–192.
    [65]
    Pourghasemi, H.R., Gayen, A., Panahi, M., Rezaie, F., Blaschke, T., 2019. Multi-hazard probability assessment and mapping in Iran. Sci. Total Environ. 692, 556–571.
    [66]
    Pradhan, B., 2013a. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput. Geosci. 51, 350–365.
    [67]
    Pradhan, B., 2013b. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput. Geosci. 51, 350–365.
    [68]
    Pradhan, B., Sameen, M.I., 2019. Modeling traffic accident severity using neural networks and support vector machines. In: Laser Scanning Systems in Highway and Safety Assessment. Springer, Cham, pp. 111–117.
    [69]
    Rahmati, O., Golkarian, A., Biggs, T., Keesstra, S., Mohammadi, F., Daliakopoulos, I.N., 2019. Land subsidence hazard modeling: machine learning to identify predictors and the role of human activities. J. Environ. Manag. 236, 466–480.
    [70]
    Razifard, M., Shoaei, G., Zare, M., 2019. Application of fuzzy logic in the preparation of hazard maps of landslides triggered by the twin Ahar-Varzeghan earthquakes (2012). Bull. Eng. Geol. Environ. 78 (1), 223–245.
    [71]
    Ridgeway, G., 2006. gbm: generalized boosted regression models. R package version 1 (3), 55.
    [72]
    Rodriguez-Galiano, V.F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P.M., Jeganathan, C., 2012. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens. Environ. 121, 93–107.
    [73]
    Remondo, J., Gonz alez, A., De Ter an, J.R.D., Cendrero, A., Fabbri, A., Chung, C.J.F., 2003. Validation of landslide susceptibility maps; examples and applications from a case study in Northern Spain. Nat. Hazards 30 (3), 437–449.
    [74]
    Schapire, R.E., 2003. The boosting approach to machine learning: an overview. In: Nonlinear Estimation and Classification. Springer, New York, NY, pp. 149–171.
    [75]
    Sharma, S., Mahajan, A.K., 2018. A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. Bull. Eng. Geol. Environ. 1–18.
    [76]
    Singh, K., Kumar, V., 2017. Landslide hazard mapping along national highway-154A in Himachal Pradesh, India using information value and frequency ratio. Arabian J. Geosci. 10 (24), 539.
    [77]
    Skilodimou, H., Bathrellos, G., Koskeridou, E., Soukis, K., Rozos, D., 2018. Physical and anthropogenic factors related to landslide activity in the Northern Peloponnese, Greece. Land 7 (3), 85.
    [78]
    Smola, A.J., Sch€olkopf, B., 2004. A tutorial on support vector regression. Stat. Comput. 14 (3), 199–222.
    [79]
    Solomun, M.K., Barger, N., Cerda, A., Keesstra, S., Markovi c, M., 2018. Assessing land condition as a first step to achieving land degradation neutrality: a case study of the Republic of Srpska. Environ. Sci. Policy 90, 19–27.
    [80]
    Soma, A.S., Kubota, T., Mizuno, H., 2019. Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed, South Sulawesi Indonesia. J. Mt. Sci. 16 (2), 383–401.
    [81]
    Sterlacchini, S., Ballabio, C., Blahut, J., Masetti, M., Sorichetta, A., 2011. Spatial agreement of predicted patterns in landslide susceptibility maps. Geomorphology 125 (1), 51–61.
    [82]
    Wang, Q., Guo, Y., Li, W., He, J., Wu, Z., 2019. Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-ofevidence, and certainty factor. Geomatics, Nat. Hazards Risk 10 (1), 820–835.
    [83]
    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.
    [84]
    Yang, W., Wang, M., Shi, P., 2013. Using MODIS NDVI time series to identify geographic patterns of landslides in vegetated regions. IEEE Geosci. Remote Sens. Lett. 10 (4), 707–710.
    [85]
    Yang, R.M., Zhang, G.L., Liu, F., Lu, Y.Y., Yang, F., Yang, F., Yang, M., Zhao, Y.G., Li, D.C., 2016. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol. Indicat. 60, 870–878.
    [86]
    Yeon, Y.K., Han, J.G., Ryu, K.H., 2010. Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng. Geol. 116 (3–4), 274–283.
    [87]
    Yilmaz, I., 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput. Geosci. 35 (6), 1125–1138.
    [88]
    Yousefi, S., Keesstra, S., Pourghasemi, H.R., Surian, N., Mirzaee, S., 2017. Interplay between river dynamics and international borders: the Hirmand River between Iran and Afghanistan. Sci. Total Environ. 586, 492–501.
    [89]
    Yousefi, S., Moradi, H.R., Keesstra, S., Pourghasemi, H.R., Navratil, O., Hooke, J., 2019. Effects of urbanization on river morphology of the Talar river, Mazandarn province, Iran. Geocarto Int. 34 (3), 276–292.
    [90]
    Youssef, A.M., Pourghasemi, H.R., Pourtaghi, Z.S., Al-Katheeri, M.M., 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.
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