Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis


  • Yonas Oyda Department of Geology, College of Natural and Computational Sciences, Arba Minch University
  • Muralitharan Jothimani Department of Geology, College of Natural and Computational Sciences, Arba Minch University
  • Hailu Regasa Department of Geology, College of Natural and Computational Sciences, Arba Minch University



Ethiopia, frequency ratio, GIS, Lake Abaya catchment , landslide susceptibility


Ethiopia's varied landscape, significant rainfall, and diverse geological characteristics pose risks of landslides. The specific research area spans 40 km2 within the Lake Abaya catchment area in the Rift Valley of Ethiopia. This investigation aimed to map landslide susceptibility using remote sensing information, GIS technology, and frequency ratio analysis. It evaluated multiple factors influencing landslide susceptibility. The process involved meticulous mapping of thematic layers, utilizing GIS techniques and diverse data sources, including primary data, satellite imagery, and secondary sources. A combination of Google Earth image analysis and field surveys was used to map landslide susceptibility in inaccessible areas. It was determined that 138 landslide sites existed. Of these, 30% (41 points) were assigned to the test of the model and another 30% to the training of the model, for a total of 97 points. The landslide susceptibility was classified into five categories based on frequency ratio analysis of the landslide susceptibility index (LSI): very low, low, moderate, high, and very high. The northeastern sector of the study area demonstrated a comparatively diminished susceptibility to landslides, ranging from low to moderate, whereas the central and southern regions showcased markedly elevated vulnerability. An evaluation of the model's accuracy using the area under the curve (AUC) method based on test inventory landslide data produced encouraging results: 84.8% accuracy on the success rate curve and 78.8% accuracy on the prediction rate curve. Based on the frequency ratio model, a susceptibility map is derived to represent susceptibility levels accurately.


Abedini, M. and Tulabi, S. 2018. Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian Watershed in Lorestan Province, Iran. Environmental Earth Sciences 77(11):1-13.

Anis, Z., Wissem, G., Vali, V., Smida, H. and Essghaier G.M. 2019. GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia. Open Geosciences 11(1):708-726.

Bahrami Y., Hassani H. and Maghsoudi, A. 2020. Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan Province, Iran. GeoJournal 68:1797-1816.

Berhane, G. and Tadesse, K. 2021. Landslide susceptibility zonation mapping using statistical index and landslide susceptibility analysis methods: a case study from Gindeberet District, Oromia Regional State, Central Ethiopia. Journal of African Earth Sciences 180:104240.

Berhane, G., Kebede, M., Alfarah, N., Hagos, E., Grum, B., Giday, A. and 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. Journal of African Earth Sciences 164:103795.

Biswas, B., Rahaman, A. and Barman, J. 2023. Comparative Assessment of FR and AHP models for landslide susceptibility mapping for Sikkim, India and preparation of suitable mitigation techniques. Journal of the Geological Society of India 99:791-801.

Chandra, S. and Indrajit, P. 2019. GIS-based spatial prediction of landslide susceptibility using the frequency ratio model of Lachung River basin, North Sikkim, India. SN Applied Sciences 1(5):1-25.

Chen, W., Pourghasemi, H.R. and Naghibi, S.A. 2018. A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment 77(2):647-664.

Conforti, M., Borrelli, L., Cofone, G. and Gulla, G. 2023. Exploring performance and robustness of shallow landslide susceptibility modeling at a regional scale using different training and testing sets. Environmental Earth Sciences 82(7):1-30.

Devara, M., Tiwari, A. and Dwivedi, R. 2021. Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis. Geomatics, Natural Hazards and Risk 12(1):675-693.

Du, G.L., Zhang, Y.S., Iqbal, J., Yang, Z.H. and Yao, X. 2017Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. Journal of Mountain Science 14(2):249-268.

Fiolleau, S., Uhlemann, S., Falco, N. and Dafflon, B. 2023. Geomorphology Assessing probability of failure of urban landslides through rapid characterization of soil properties and vegetation distribution. Geomorphology 423:108560.

Gazibara, S.B., Krkac, M., and Arbanas, S.M. 2019. Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia). Journal of Maps 15(2):773-779.

Hamid, B., Massinissa, B. and Nabila, G. 2023. Landslide susceptibility mapping using GIS-based statistical and machine learning modeling in the city of Sidi Abdellah, Northern. Modeling Earth Systems and Environment 9(2):2477-2500.

Haque, U., da Silva, P.F., Devoli, G., Pilz, J., Zhao, B., Khaloua, A., Wilopo, W., Andersen, P., Lu, P., Lee, J., Yamamoto, T., Keellings, D., Wu, J.H. and Glass, G.E. 2019. The human cost of global warming: Deadly landslides and their triggers (1995-2014). Science of The Total Environment 682:673-684.

Huang, Z., Peng, L., Li, S., Liu, Y. and Zhou, S. 2023. GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison. Environmental Science and Pollution Research International 30(38):88612-88626.

Kayastha, P., Dhital, M. and Smedt, F. 2013. Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Computers & Geosciences 52:398-408.

Lee, S. and Pradhan, B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33-41.

Manchar, N., Benabbas, C., Hadji, R., Bouaicha, F. and 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. and Mondal, S. 2019. Probabilistic approaches and landslide susceptibility. In Geoinformatics and Modelling of Landslide Susceptibility and Risk. Environmental Science and Engineering. Springer Book Series (ESE), pp. 145-163.

Mersha, T. and Meten, M. 2020. GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern. Geoenvironmental Disasters 7:20.

Meten, M., Bhandary, N.P. and Yatabe, R. 2015. GIS-based frequency ratio and logistic regression modeling for landslide susceptibility mapping of Debre Sina area in central Ethiopia. Journal of Mountain Science 12(6):1355-1372.

Moazzam, M.F.U., Vansarochana, A., Boonyanuphap, J., Choosumrong, S., Rahman, G. and Djueyep, G.P. 2020. Spatio-statistical comparative approaches for landslide susceptibility modeling: case of Mae Phun, Uttaradit Province, Thailand. SN Applied Sciences 2(3):1-15.

Nourani, V., Pradhan, B., Ghaffari, H. and Sharifi, S.S. 2014. Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Natural Hazards 71(1):523-547.

Paul, S. 2022. Change detection and future change prediction in Habra I and II block using remote sensing and GIS - A case study. International Journal of Engineering and Geosciences 7(2):191-207.

Pham, B.T., Jaafari, A., Prakash, I. and Bui DT. 2019. A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment 78:2865-2886.

Pham, B.T., Ramesh, P.S., Sudhir, K. and Quoc T.L. 2015. Landslide Susceptibility Assessment at a Part of Uttarakhand Himalaya, India using GIS-based Statistical Approach of Frequency Ratio Method. International Journal of Engineering Research V4(11): 338-344.

Poudyal, C.P., Chang, C., Oh, H.J. and Lee, S. 2010. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environmental Earth Sciences 61:1059-1064.

Rahman, G. and Collins, A.E. 2017. Geospatial analysis of landslide susceptibility and zonation in Shahpur Valley, Eastern Hindu Kush using frequency ratio model. Proceedings of the Pakistan Academy of Sciences 54(3):149-163.

Rahman, G., Rahman, A. and Ullah, S. 2019. Spatial analysis of landslide susceptibility using a failure rate approach in the Hindu Kush region, Pakistan. Journal of Earth System Science 128(3):1-16.

Rahman, S.A., Islam, M.M., Salman, M.A. and Rafiq, M.R. 2022. Evaluating bank erosion and identifying possible anthropogenic causative factors of Kirtankhola River in Barishal, Bangladesh: an integrated GIS and Remote Sensing approaches. International Journal of Engineering and Geosciences 7(2):179-190.

Reichenbach, P., Rossi, M., Malamud, B.D., Monika, M. and Guzzetti, F. 2018. A review of statistically-based landslide susceptibility models. Earth-Science Reviews 180:60-91.

Saha, A.K., Gupta, R.P. and Arora, M.K. 2002. GIS-based landslide hazard zonation in a part of the Himalayas. International Journal of Remote Sensing 23(2):357-369.

Shabbir, W., Omer, T. and Pilz, J. 2023. The impact of environmental change on landslides, fatal landslides, and their triggers in Pakistan (2003-2019). Environmental Science and Pollution Research 30:33819-33832.

Shrestha, H.L. and Poudel, M. 2018. Landslide susceptibility zonation mapping in post-earthquake scenario in Gorkha District. Forestry: Journal of Institute of Forestry, Nepal 15(15):45-56.

Silalahi, F.E.S, Pamela, Arifianti, Y. and Hidayat, F. 2019. Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geosciences Letters 6:10.

Singh, K. and Kumar, V. 2018. Hazard assessment of landslide disaster using information value method and analytical hierarchy process in the highly tectonic Chamba region in the bosom of Himalaya. Journal of Mountain Science 15(4):808-824.

Song, Y., Niu, R., Xu, S., Ye, R., Peng, L., Guo, T., Li, S. and Chen, T. 2019. Landslide susceptibility mapping based on weighted gradient boosting decision tree in Wanzhou section of the Three Gorges reservoir area (China). ISPRS International Journal of Geo-Information 8(1):4.

Su, C., Wang, B., Lv, Y., Zhang, M., Peng, D. and Bate, B. 2022. Improved landslide susceptibility mapping using unsupervised and supervised collaborative machine learning models. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 387-405.

Vakhshoori, V., Pourghasemi, H.R., Zare, M. and Blaschke, T. 2019. Landslide susceptibility mapping using GIS-based data mining algorithms. Water 11(11):7-13.

Woldearegay, K. 2013. Review of the occurrences and influencing factors of landslides in the highlands of Ethiopia: With implications for infrastructural development. Momona Ethiopian Journal of Science 5(1):3.

Wubalem, A. 2020. Modeling of landslide susceptibility in a part of Abay Basin, northwestern Ethiopia. Open Geosciences 12(1):1440-1467.

Yalcin, A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72.

Yilmaz, O.S. 2023. Production of landslide susceptibility map in geographic information system environment using frequency ratio method: Manisa, Demirci, Tekeler Village example. Geomatics 8(1):42-54.

Youssef, A.M., Mahdi, A.M. and Reza, H. 2022. Landslides and flood multi-hazard assessment using machine learning techniques. Bulletin of Engineering Geology and the Environment 81(9):1-23.

Zhang, Y., Miao, C., Zhu, J., Gao, T., Sun, Y., Zhang, J., Xu, S. and Yang, K. 2022. The impact of landslides on chemical and microbial properties of soil in a temperate secondary forest ecosystem. Journal of Forestry Research 33(6):1913-1923.








How to Cite

Oyda, Y., Jothimani, M., & Regasa, H. (2024). Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis . Journal of Degraded and Mining Lands Management, 11(3), 5885–5895.



Research Article