Developing landslide susceptibility map using Artificial Neural Network (ANN) method for mitigation of land degradation


  • Heni Masruroh Geography Department, Universitas Negeri Malang
  • Amin Setyo Leksono Biology Department, Faculty of Mathematics and Natural Science Universitas Brawijaya
  • Syahrul Kurniawan Soil Department, Faculty of Agriculture, Universitas Brawijaya
  • Soemarno Soemarno Soil Department, Faculty of Agriculture, Universitas Brawijaya



artificial neural network, landslide susceptibility, remote sensing data, RStudio


Landslides are one of the crucial problems that have an impact on land degradation and human life. This study aimed to develop vulnerability maps using ANN to mitigate land degradation in the Bromo Tengger Semeru with the extending area of Universal Transverse Mercator (UTM) Coordinate System Top 91277639, Bottom 911569, Left 692860, and Right 706860. The method applied the Artificial Neural Network (ANN) model using RStudio machine learning. Landslides were mapped using Sentinel Image and Orthomozaic photo interpretation from data acquisition using Unmanned Aerial Vehicle (UAV). The landslide control factor data was obtained through DEMNAS (National Digital Elevation Model) with a spatial resolution of 8 meters. Data normalisation was conducted using the Mix-Max method before it was processed using RStudio. The landslide existing for ANN workflow was processed using the Bioclim model. The results showed landslide susceptibility was categorised into four classes i.e., low susceptibility (29.83%), which was spatially spread on most in the lower slopes, moderate susceptibility (3.11%), high susceptibility (2.99%), and very high susceptibility (15.94) which is scattered on the upper slope to the middle slope of the watershed. The most significant factor influencing the landslide is the topography factor, with a Relative Importance (RI) value of 0.86; the hydrological factor, with an RI of 0.833 and the surface feature, with an RI of 0.355. The results of the landslide susceptibility model are very proper for land degradation mitigation strategies. It has high accuracy through an Area Under Curve (AUC) of 0.965 and a Precision Recall Curve (PRC) of 0.976.

Author Biographies

Heni Masruroh, Geography Department, Universitas Negeri Malang

Doctorate Program of Environmental Studies, Universitas Brawijaya

Amin Setyo Leksono, Biology Department, Faculty of Mathematics and Natural Science Universitas Brawijaya

Biology Department

Syahrul Kurniawan, Soil Department, Faculty of Agriculture, Universitas Brawijaya

Soil Department


Abbaszadeh Shahri, A., Spross, J., Johansson, F. and Larsson, S. 2019. Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena 183:104225, doi:10.1016/j.catena.2019.104225.

Amato, G., Palombi, L. and Raimondi, V. 202. Data-driven classification of landslide ty pes at a national scale by using artificial neural networks. International Journal of Applied Earth Observation and Geoinformation 104:102549, doi:10.1016/j.jag.2021.102549.

Bachri, S., Utomo, K.S.B., Sumarmi, S., Fathoni, M.N. and Aldianto, Y.E. 2021. Optimization of the artificial neural network model using certainty factor (C-ANN) for semi-detail scale landslide hazard mapping in the Bendo Watershed, Banyuwangi Regency. Majalah Geografi Indonesia 35(1):1-8, doi:10.22146/mgi.57869 (in Indonesian).

Bachri, S., Shrestha, R.P., Yulianto, F., Sumarmi, S., Utomo, K.S.B. and Aldianto, Y.E. 2020. Mapping landform and landslide susceptibility using remote sensing, GIS and field observation in the southern cross road, Malang Regency, East Java, Indonesia. Geosciences 11(1):4, doi:10.3390/geosciences11010004.

Geology Agency. 2013. Geological Map Scale 1:50.000 (in Indonesian).

Bièvre, G., Jongmans, D., Goutaland, D., Pathier, E. and Zumbo, V. 2016. Geophysical characterization of the lithological control on the kinematic pattern in a large clayey landslide (Avignonet, French Alps). Landslides 13(3):423-436, doi:10.1007/s10346-015-0579-0.

BIG. 2008. DEMNAS (Digital Elevation Model Nasional).

BIG. 2020. Indonesia Geospatial Portal.

Biswajeet, P. and Saro, L. 2007. Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model. Earth Science Frontiers 14(6):143-151, doi:10.1016/s1872-5791(08)60008-1.

Bragagnolo, L., da Silva, R.V. and Grzybowski, J.M.V. 2020a. Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on artificial neural networks. Environmental Modelling and Software 123:104565, doi:10.1016/j.envsoft.2019.104565.

Bragagnolo, L., da Silva, R.V. and Grzybowski, J.M.V. 2020b. Artificial neural network ensembles applied to the mapping of landslide susceptibility. Catena 184:104240, doi:10.1016/j.catena.2019.104240.

Bui, D.T., Lofman, O., Revhaug, I. and Dick, O. 2011. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards 59(3):1413-1444, doi:10.1007/s11069-011-9844-2.

Burton, D. and Wood, L.J. 2010. Seismic geomorphology and tectonostratigraphic fill of half grabens, West Natuna Basin, Indonesia. AAPG Bulletin, 94(11):16951712, doi:10.1306/06301010003.

Capitani, M., Ribolini, A. and Bini, M. 2013. The slope aspect: A predisposing factor for landsliding? Comptes Rendus Geoscience 345(11-12):427-438. doi:10.1016/j.crte.2013.11.002.

Carvalho, B.M., Rangel, E.F., Ready, P.D. and Vale, M.M. 2015. Ecological niche modelling predicts southward expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), Vector of Leishmania (Leishmania) amazonensis in South America, under climate change. PLOS ONE 10(11):e0143282, doi:10.1371/journal.pone.0143282.

Chalise, D., Kumar, L. and Kristiansen, P. 2019. Land degradation by soil erosion in Nepal: A review. Soil Systems 3(1):1-18, doi:10.3390/soilsystems3010012.

Chasek, P. 2022. From land degradation to land restoration Policy Brief #29. 2. International Institute for Sustainable Development.

Chen, S., Zhang, L., She, D. and Chen, J. 2019. Spatial downscaling of tropical rainfall measuring mission (TRMM) annual and monthly precipitation data over the middle and lower reaches of the Yangtze River Basin, China. Water 11(3):568, doi:10.3390/w11030568.

Climate Engine. 2014. Climate Engine App.

Conforti, M. 2014. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236-250, doi:10.1016/j.catena.2013.08.006.

Conforti, M., Pascale, S., Robustelli, G. and Sdao, F. 2014. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236-250, doi:10.1016/j.catena.2013.08.006.

Davis, J. and Goadrich, M. 2006. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning - ICML ’06, 233-240, doi:10.1145/1143844.1143874.

Dehnavi, A., Aghdam, I.N., Pradhan, B. and Morshed Varzandeh, M.H. 2015. A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena 135:122-148. doi:10.1016/j.catena.2015.07.020.

Donnarumma, A., Revellino, P., Grelle, G. and Guadagno, F.M. 2013. Slope angle as indicator parameter of landslide susceptibility in a geologically complex area. In: Landslide Science and Practice (pp. 425–433). Springer Berlin Heidelberg, doi:10.1007/978-3-642-31325-7_56.

Dou, J., Yamagishi, H., Pourghasemi, H.R., Yunus, A.P., Song, X., Xu, Y. and Zhu, Z. 2015. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards 78(3):1749-1776, doi:10.1007/s11069-015-1799-2.

European Space Agency. 2015. Copernicus: Sentinel-2.

Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27(8):861-874, doi:10.1016/j.patrec.2005.10.010.

Francipane, A., Arnone, E., Lo Conti, F., Puglisi, C., Noto, L.V. and Scarbaci, A. 2014. A comparison between heuristic, statistical, and data-driven methods in landslide susceptibility assessment: an application to the briga and giampilieri catchments. 11o International Conference on Hydroinformations 9.

Gameiro, S., Riffel, E.S., de Oliveira, G.G. and Guasselli, L.A. 2021a. Artificial neural networks applied to landslide susceptibility: The effect of sampling areas on model capacity for generalization and extrapolation. Applied Geography 137(November):102598, doi:10.1016/j.apgeog.2021.102598.

Gessler, P., Gorsevski, P.V, Jankowski, P. and Gessler, P.E. 2006. An heuristic approach for mapping landslide hazard integrating fuzzy logic with analytic hierarchy process. Control and Cybernetics 35(1):121-146.

Hairiah, K., Widianto, W., Suprayogo, D. and Van Noordwijk, M. 2020. Tree roots anchoring and binding soil: Reducing landslide risk in Indonesian agroforestry. Land 9(8):1-19, doi:10.3390/LAND9080256.

He, H., Hu, D., Sun, Q., Zhu, L. and Liu, Y. 2019. A landslide susceptibility assessment method based on GIS technology and an AHP-weighted information content method: A case study of southern Anhui, China. ISPRS International Journal of Geo-Information 8(6):226, doi:10.3390/ijgi8060266.

Hidayat, R., Sutanto, S.J., Hidayah, A., Ridwan, B. and Mulyana, A. 2019. Development of a landslide early warning system in Indonesia. Geosciences (Switzerland) 9(10):1-17. doi:10.3390/geosciences9100451.

Hijmans, R.J. and Graham, C.H. 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12(12):2272-2281, doi:10.1111/j.1365-2486.2006.01256.x.

Hijmans, R.J., Phillips, S., Leathwick, J. and Elith, J. 2011. Package “dismo. https://rdrr.ioâ€.

Hong, H., Miao, Y., Liu, J. and Zhu, A.-X. 2019. Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping. Catena 176:45-64, doi:10.1016/j.catena.2018.12.035.

Jacquemart, M. and Tiampo, K. 2021. Leveraging time series analysis of radar coherence and normalized difference vegetation index ratios to characterize pre-failure activity of the Mud Creek landslide, California. Natural Hazards and Earth System Sciences 21(2):629-642, doi:10.5194/nhess-21-629-2021.

Kavzoglu, T., Sahin, E.K. and Colkesen, I. 2014. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425-439, doi:10.1007/s10346-013-0391-7.

Kirui, O.K., Mirzabaev, A. and von Braun, J. 2021. Assessment of land degradation ‘on the ground’ and from ‘above.’ SN Applied Sciences 3(3):1-13, doi:10.1007/s42452-021-04314-z.

Lee, J., Kim, C.G., Lee, J.E., Kim, N.W. and Kim, H. 2018. Application of artificial neural networks to rainfall forecasting in the Geum River Basin, Korea. Water (Switzerland) 10(10), doi:10.3390/w10101448.

Lee, S., Ryu, J.-H., Lee, M.-J., and Won, J.-S. 2003. Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology 44(7):820-833, doi:10.1007/s00254-003-0825-y.

Lineback Gritzner, M., Marcus, W.A., Aspinall, R. and Custer, S.G. 2001. Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology 37(1-2):149-165, doi:10.1016/S0169-555X(00)00068-4.

Metternicht, G., Zinck, J.A., Blanco, P.D. and del Valle, H.F. 2010. Remote sensing of land degradation: experiences from Latin America and the Caribbean. Journal of Environmental Quality 39(1):42-61, doi:10.2134/jeq2009.0127.

Moharrami, M., Naboureh, A., Gudiyangada Nachappa, T., Ghorbanzadeh, O., Guan, X. and Blaschke, T. 2020. National-scale landslide susceptibility mapping in Austria using Fuzzy Best-Worst Multi-Criteria decision-making. ISPRS International Journal of Geo-Information 9(6):393, doi:10.3390/ijgi9060393.

Muddarisna, N., Yuniwati, E.D., Masruroh, H. and Rahman, A. 2021. The effectiveness of cover crops on soil loss control in Gede catchment of Malang Regency, Indonesia. Journal of Degraded and Mining Lands Management 8(2):2673-2679, doi:10.15243/jdmlm. 2021.082.2673.

Muntohar, A.S., Mavrouli, O., Jetten, V.G., van Westen, C.J. and Hidayat, R. 2021. Development of landslide early warning system based on the satellite-derived rainfall threshold in Indonesia. In: Casagli, N., Tofani, V., Sassa, K., Bobrowsky, P.T. and Takara, K. (eds.), Understanding and Reducing Landslide Disaster Risk, ICL Contribution to Landslide Disaster Risk Reduction, 227-235. Springer Nature Switzerland, doi:10.1007/978-3-030-60311-3_26.

Nawi, N.M., Atomi, W.H. and Rehman, M.Z. 2013. The effect of data pre-processing on optimized training of artificial neural networks. Procedia Technology 11:32-39, doi:10.1016/j.protcy.2013.12.159.

Nhu, V.-H., Shirzadi, A., Shahabi, H., Singh, S.K., Al-Ansari, N., Clague, J.J., Jaafari, A., Chen, W., Miraki, S., Dou, J., Luu, C., Górski, K., Thai Pham, B., Nguyen, H. D. and Ahmad, B.Bin. 2020. Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, Naïve bayes tree, artificial neural network, and support vector machine algorithms. International Journal of Environmental Research and Public Health 17(8):2749, doi:10.3390/ijerph17082749.

Nisa, A.K., Irawan, M.I. and Pratomo, D.G. 2019. Identification of potential landslide disaster in east java using neural network model (case study: District of Ponogoro). Journal of Physics: Conference Series 1366(1), doi:10.1088/1742-6596/1366/1/012095.

Ogasawara, E., Martinez, L.C., De Oliveira, D., Zimbrão, G., Pappa, G.L. and Mattoso, M. 2010. Adaptive normalization: a novel data normalization approach for non-stationary time series. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), 18-23 July 2010, Barcelona, Spain, doi:10.1109/IJCNN.2010.5596746.

Pijanowski, B.C., Brown, D.G., Shellito, B.A. and Manik, G.A. 2002. Using neural networks and GIS to forecast land use changes: a land transformation model. Computers, Environment and Urban Systems 26(6):553-575, doi:10.1016/S0198-9715(01)00015-1.

Putra, A.N., Nita, I., Jauhary, M.R.Al., Nurhutami, S.R. and Ismail, M.H. 2021. Landslide risk analysis on agriculture area in pacitan regency in east java indonesia using geospatial techniques. Environment and Natural Resources Journal 19(2):141-152, doi:10.32526/ennrj/19/2020167.

Różycka, M., Migoń, P. and Michniewicz, A. 2017. Topographic wetness index and terrain ruggedness index in geomorphic characterisation of landslide terrains, on examples from the Sudetes, SW Poland. Zeitschrift Für Geomorphologie, Supplementary Issues 61(2):61-80, doi:10.1127/zfg_suppl/2016/0328.

RStudio Team. 2020. RStudio: Integrated Development Environment for R (1.3.1093). RStudio, Inc.

UNDRR. 2021. Global Natural Disaster Assessment Report 2020. UN Annual Report, October, 1-80.

Viet, T.T., Lee, G. and Kim, M. 2016. Shallow landslide assessment considering the influence of vegetation Cover. Journal of the Korean Geoenvironmental Society 17(4):17-31, doi:10.14481/jkges.2016.17.4.17.

Wilson, M.F.J., O’Connell, B., Brown, C., Guinan, J.C. and Grehan, A.J. 2007. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Marine Geodesy 30(1-2):3-35, doi:10.1080/01490410701295962.

Zaki, M.K., Noda, K., Ito, K., Komariah, and Ariyanto, D.P. 2021. Long-term trends of diurnal rainfall and hydro-meteorological disaster in the new capital city of Indonesia. IOP Conference Series: Earth and Environmental Science 724(1), doi:10.1088/1755-1315/724/1/012046.

Zamroni, A., Kurniati, A.C. and Prasetya, H.N.E. 2020. The assessment of landslides disaster mitigation in Java Island, Indonesia: a review. Journal of Geoscience, Engineering, Environment, and Technology 5(3):139-144, doi:10.25299/jgeet.2020.5.3.4676.








How to Cite

Masruroh, H., Leksono, A. S., Kurniawan, S., & Soemarno, S. (2023). Developing landslide susceptibility map using Artificial Neural Network (ANN) method for mitigation of land degradation. Journal of Degraded and Mining Lands Management, 10(3), 4479–4494.



Research Article