Mapping landslide susceptibility in Enfraz to Addis Zemen area Northwestern Ethiopia
DOI:
https://doi.org/10.15243/jdmlm.2025.122.7095Keywords:
Ethiopia, frequency ratio , GIS, landslides, susceptibilityAbstract
The study area (Enfraz to Addis Zemen) is located in northwestern Ethiopia, which frequently experiences landslides, causing damage to farmland, engineering structures, infrastructures, and villages, as well as animal and human fatalities. To manage this catastrophic hazard, a comprehensive GIS-based frequency ratio model (FR) was applied to produce a landslide susceptibility map. In this study, 134 landslides were identified from detailed fieldwork and Google Earth imagery analysis, split into 70% to develop the model and 30% for model validation. The relationship between landslide probability with landslide factor classes of lithology, annual mean rainfall, slope, aspect, curvature, elevation, distance to the river, and land use-land cover was analyzed in a GIS environment. FR model assigns weights to each factor class based on observed frequencies. These weighted factors were summed using a raster calculator to produce landslide susceptibility indexes (LSIs), which were classified into very low, low, moderate, high, and very high susceptibility classes using the natural break classification method. The model’s accuracy and performance were validated using the area under the curve of the receiver operating characteristics curve (ROC), which showed an AUC success rate of 92.2% and a predictive rate of 86.05%. These results confirm that the FR model is effective in landslide susceptibility modeling. The generated map can support decision-makers, urban planners, and researchers in land use planning, landslide mitigation strategies, and future research.
References
Bao, S., Liu, J., Wang, L. and Zhao, X. 2022. Application of transformer models to landslide susceptibility mapping. Sensors 22(23). https://doi.org/10.3390/s22239104
Berhane, G., Gebrehiwot, A. and Abay, A. 2023. Landslide susceptibility mapping in the Adwa Volcanic Mountain Plugs, Northern Ethiopia: A comparative analysis of frequency ratio and analytical hierarchy process methods. Geomatics, Natural Hazards and Risk 14(1). https://doi.org/10.1080/19475705.2023.2281244
Fang, Z., Wang, Y., Peng, L. and Hong, H. 2021. A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. International Journal of Geographical Information Science 35(2). https://doi.org/10.1080/13658816.2020.1808897
Gautam, P., Kubota, T. and Aditian, A. 2021. Evaluating underlying causative factors for earthquake-induced landslides and landslide susceptibility mapping in Upper Indrawati Watershed, Nepal. Geoenvironmental Disasters 8(1). https://doi.org/10.1186/s40677-021-00200-3
Getachew, N. and Meten, M. 2021. Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia. Geoenvironmental Disasters 8(1). https://doi.org/10.1186/s40677-021-00177-z
Hang, H.T., Hoa, P.D., Tru, V.N. and Phuong, N.V. 2021. Landslide susceptibility mapping along National Highway-6, Hoa Binh Province, Vietnam using Frequency Ratio Model and GIS. International Journal of GEOMATE 21(85):84-90. https://doi.org/10.21660/2021.85.j2222
Kohno, M. and Higuchi, Y. 2023. Landslide susceptibility assessment in the Japanese Archipelago based on a landslide distribution map. ISPRS International Journal of Geo-Information 12(2). https://doi.org/10.3390/ijgi12020037
Li, B., Liu, K., Wang, M., He, Q., Jiang, Z., Zhu, W. and Qiao, N. 2022. Global dynamic rainfall-induced landslide susceptibility mapping using machine learning. Remote Sensing 14(22). https://doi.org/10.3390/rs14225795
Liu, H., Ding, Q., Yang, X., Liu, Q., Deng, M. and Gui, R. 2024. A knowledge-guided approach for landslide susceptibility mapping using convolutional neural network and graph contrastive learning. Sustainability (Switzerland) 16(11). https://doi.org/10.3390/su16114547
Martinello, C., Cappadonia, C., Conoscenti, C., Agnesi, V. and Rotigliano, E. 2021. Optimal slope units partitioning in landslide susceptibility mapping. Journal of Maps 17(3):152-162. https://doi.org/10.1080/17445647.2020.1805807
Menendez-Duarte, R., Marquinez, J., Vazquez-Tarrio, D. and Fernandez, F.J. 2024. Shallow landslide susceptibility map at a regional scale (Asturias, NW Spain). A heuristic-driven approach. Journal of Maps 20(1). https://doi.org/10.1080/17445647.2024.2375094
Mersha, T. and Meten, M. 2020. GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenvironmental Disasters 7(1). https://doi.org/10.1186/s40677-020-00155-x
Mulugeta, T., Shano, L. and Jothimani, M. 2024. Landslide susceptibility modeling in the Kulfo river catchment, Rift Valley, Ethiopia: An integrated geospatial and statistical analysis. Quaternary Science Advances 14. https://doi.org/10.1016/j.qsa.2024.100191
Nachappa, T., Kienberger, S., Meena, S.R., Holbling, D. and Blaschke, T. 2020. Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk 11(1). https://doi.org/10.1080/19475705.2020.1736190
Putriani, E., Wu, Y.M., Chen, C.W., Ismulhadi, A. and Fadli, D.I. 2023. Development of landslide susceptibility mapping with a multi-variance statistical method approach in Kepahiang, Indonesia. Terrestrial, Atmospheric and Oceanic Sciences 34(1). https://doi.org/10.1007/s44195-023-00050-6
Roccati, A., Paliaga, G., Luino, F., Faccini, F. and Turconi, L. 2021. GIS-based landslide susceptibility mapping for land use planning and risk assessment. Land 10(2):1-28. https://doi.org/10.3390/land10020162
Shano, L., Raghuvanshi, T.K. and Meten, M. 2020. Landslide susceptibility evaluation and hazard zonation techniques - a review. Geoenvironmental Disasters 7(1). https://doi.org/10.1186/s40677-020-00152-0
Thanh, L.N., Fang, Y.M., Chou, T.Y., Hoang, T.V., Nguyen, Q.D., Lee, C.Y., Wang, C.L., Yin, H.Y. and Lin, Y.C. 2022. Using landslide statistical index technique for landslide susceptibility mapping: Case study: Ban Khoang Commune, Lao Cai Province, Vietnam. Water (Switzerland) 14(18). https://doi.org/10.3390/w14182814
Wubalem, A. 2021. Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia. Geoenvironmental Disasters 8(1). https://doi.org/10.1186/s40677-020-00170-y
Wubalem, A. and Meten, M. 2020. Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Applied Sciences 2(5). https://doi.org/10.1007/s42452-020-2563-0
Wubalem, A., Getahun, B., Hailemariam, Y., Mesele, A., Tesfaw, G., Dawit, Z. and Goshe, E. 2022. Landslide susceptibility modeling using the Index of Entropy and Frequency Ratio Method from Nefas-Mewcha to Weldiya Road Corridor, Northwestern Ethiopia. Geotechnical and Geological Engineering 40(10):5249-5278. https://doi.org/10.1007/s10706-022-02214-6
Xiao, T., Yu, L., Tian, W., Zhou, C. and Wang, L. 2021. Reducing local correlations among causal factor classifications as a strategy to improve landslide susceptibility mapping. Frontiers in Earth Science 9. https://doi.org/10.3389/feart.2021.781674
Yu, L., Wang, Y. and Pradhan, B. 2024. Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China. Geoscience Frontiers 15(4):101802. https://doi.org/10.1016/j.gsf.2024.101802
Zhang, J., Gao, B., Huang, H., Chen, L., Li, Y. and Yang, D. 2022. SBAS-InSAR-based landslide susceptibility mapping along the North Lancang River, Tibetan Plateau. Frontiers in Earth Science 10. https://doi.org/10.3389/feart.2022.901889
Zhu, Z., Yuan, X., Gan, S., Zhang, J. and Zhang, X. 2023. A research on a new mapping method for landslide susceptibility based on SBAS-InSAR technology. Egyptian Journal of Remote Sensing and Space Science 26(4):1046-1056. https://doi.org/10.1016/j.ejrs.2023.11.009
Downloads
Submitted
Accepted
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Degraded and Mining Lands Management

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Submission of a manuscript implies: that the work described has not been published before (except in the form of an abstract or as part of a published lecture, or thesis) that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Scientific Journal by Eko Handayanto is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at https://ub.ac.id.
Permissions beyond the scope of this license may be available at https://ircmedmind.ub.ac.id/.