Landslide Susceptibility Mapping Using Weights of Evidence Model on the Slopes of Mount Elgon, Eastern Uganda
Abstract
Generally, landslide susceptibility mapping is an important step in mitigating their impacts. There is, however, particular dearth of information on the application of GIS-based bivariate methods particularly the weights of evidence model in mapping landslide susceptibility on the slopes of Mount Elgon in eastern Uganda. This study, therefore, evaluated the susceptibility of Bukalasi milli-watershed to landslides, as an early warning strategy for the major landslide hotspot in Uganda. A landslide inventory for the study area was prepared, and the weights of influence of selected landslide-conditioning factors were calculated to present their relative importance in landslide susceptibility. Eight conditioning factors were considered in this study namely; land use, lithology, rainfall, elevation, slope aspect, slope angle, plan curvature and profile curvature. Following the results of the Agterberg-Cheng conditional independence test (probability = 62.5%), the hypothesis of conditional independence among these factors was accepted. Validation using the ROC indicated satisfactory performance of the model considering the model prediction rate (Area under the Curve = 0.882) and success rate (Area under the Curve = 0.912). The final landslide susceptibility map highlights high susceptibility in the southern and western parts of the study area. It further shows that whereas Bukibumbi, Bundesi and Suume parishes are the most prone parishes, Shibanga Parish is relatively the least prone to landslides disasters. Thus, such highly susceptible areas should be prioritised during intervention programmes, especially relocation of the residents at risk. Since the absence of forests has been indicated to exacerbate susceptibility to landslides, deforestation should have severe penalties, and extensive tree-planting should instead be encouraged. Other human activities like farming on fragile slopes, which would further destabilise the slopes should particularly be discouraged.
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References
Agterberg, F. P., & Cheng, Q. (2002). Conditional independence test for weights-of-evidence modeling. Natural Resources Research, 11(4), 249-255.
Benchelha, S., Aoudjehane, H. C., Hakdaoui, M., El Hamdouni, R., Mansouri, H., Benchelha, T., & Alaoui, M. (2019). Landslide susceptibility mapping in the Municipality of Oudka, Northern Morocco: a comparison between logistic regression and artificial neural networks models. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 41-49.
Broeckx, J., Maertens, M., Isabirye, M., Namazzi, B., Tamale, J., Jacobs, L., ... & Poesen, J. (2018, April). Landslide susceptibility and rates in the Mount Elgon region, Uganda. In EGU General Assembly Conference Abstracts (p. 16682).
Byou, T. (2021). Evaluation of the landslide susceptibility map obtained by a GIS matrix method: a case of Al Hoceima city (northern Morocco). In SHS Web of Conferences (Vol. 119). EDP Sciences.
Canavesi, V., Segoni, S., Rosi, A., Ting, X., Nery, T., Catani, F., & Casagli, N. (2020). Different approaches to use morphometric attributes in landslide susceptibility mapping based on meso-scale spatial units: A case study in Rio de Janeiro (Brazil). Remote Sensing, 12(11), 1826.
Chen, W., & Li, W. (2014). Application of weights-of-evidence model in landslide susceptibility mapping at Baozhong Region in Baoji, China. EJGE, 19.
Chen, W., Sun, Z., & Han, J. (2019). Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models. Applied sciences, 9(1), 171.
Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., ... & Althuwaynee, O. F. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Natural hazards, 65(1), 135-165.
Elmoulat, M., & Ait Brahim, L. (2018). Landslides susceptibility mapping using GIS and weights of evidence model in Tetouan-Ras-Mazari area (Northern Morocco). Geomatics, Natural Hazards and Risk, 9(1), 1306-1325.
Froude, M. J., & Petley, D. N. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18(8), 2161-2181.
Gadtaula, A., & Dhakal, S. (2019). Landslide susceptibility mapping using weight of evidence method in Haku, Rasuwa district, Nepal. Journal of Nepal Geological Society, 58, 163-171.
Gorokhovich, Y., Doocy, S., Walyawula, F., Muwanga, A., & Nardi, F. (2013). Landslides in Bududa, Eastern Uganda: Preliminary assessment and proposed solutions. In Landslide science and practice (pp. 145-149). Springer, Berlin, Heidelberg.
Gudiyangada Nachappa, T., Tavakkoli Piralilou, S., Ghorbanzadeh, O., Shahabi, H., & Blaschke, T. (2019). Landslide susceptibility mapping for Austria using geons and optimization with the Dempster-Shafer theory. Applied Sciences, 9(24), 5393.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112(1-2), 42-66.
IPCC. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II, and III to the Fifth Assesment Report of the Intergovernemental Panel on Climate Change. Geneva, Switzerland: IPCC. pp.http://www.ipcc.ch/report/ar5/syr//hdl:10013/epic.45156.d001
Kitutu, K. M. G. (2010). Landslide occurrences in the hilly areas of Bududa District in Eastern Uganda and their causes (Doctoral dissertation, Makerere University).
Kitutu, M. G., Muwanga, A., Poesen, J., & Deckers, J. A. (2009). Influence of soil properties on landslide occurrences in Bududa district, Eastern Uganda. African journal of agricultural research, 4(7), 611-620.
Li, B., Wang, N., & Chen, J. (2021). GIS-based landslide susceptibility mapping using information, frequency ratio, and artificial neural network methods in Qinghai Province, Northwestern China. Advances in Civil Engineering, 2021.
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.
Maxwell, A. E., Warner, T. A., & Guillén, L. A. (2021). Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—part 1: Literature review. Remote Sensing, 13(13), 2450.
Mugagga, F., Kakembo, V., & Buyinza, M. (2012). Land use changes on the slopes of Mount Elgon and the implications for the occurrence of landslides. Catena, 90, 39-46.
Mugagga, F., Kakembo, V., & Buyinza, M. (2011). A characterisation of the physical properties of soil and the implications for landslide occurrence on the slopes of Mount Elgon, Eastern Uganda. Natural hazards, 60(3), 1113-1131.
Nakileza, B. R., & Nedala, S. (2020). Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda. Geoenvironmental Disasters, 7(1), 1-13.
National Planning Authority (2020). Third National Development Plan (NDPIII) 2020/21 – 2024/25. National Planning Authority. http://www.npa.go.ug/wp-content/uploads/2020/08/NDPIII-Finale_Compressed.pdf.
Neema, S., Bua, G. M., Tuhebwe, D., Ssentongo, J., Tumuhamye, N., Mayega, R. W., ... & Bazeyo, W. (2018). Community perspective on policy options for resettlement management: A case study of risk reduction in Bududa, eastern Uganda. PLoS currents, 10.
Nohani, E., Moharrami, M., Sharafi, S., Khosravi, K., Pradhan, B., Pham, B. T., ... & M Melesse, A. (2019). Landslide susceptibility mapping using different GIS-based bivariate models. Water, 11(7), 1402.
Pham, B. T., Tien Bui, D., Indra, P., & Dholakia, M. (2015). Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach of frequency ratio method. Int J Eng Res Technol, 4(11), 338-344.
Pradhan, B., Oh, H. J., & Buchroithner, M. (2010). Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomatics, Natural Hazards and Risk, 1(3), 199-223.
Staudt, M., Kuosmanen, E., Babirye, P., & Lugaizi, I. (2014). The Bududa landslide of 1 March 2010. Geological Survey of Finland (Special Paper 56), 373, 384.
Uganda Bureau of Statistics. (2018). Population by Parish Census 2014 Eastern Region, Kampala, Uganda. Uganda Bureau of Statistics.
Vakhshoori, V., & Zare, M. (2018). Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps?. Geomatics, Natural Hazards and Risk, 9(1), 249-266.
Van Den Eeckhaut, M., Hervás, J., Jaedicke, C., Malet, J. P., Montanarella, L., & Nadim, F. (2012). Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides, 9(3), 357-369.
Xing, Y., Yue, J., Guo, Z., Chen, Y., Hu, J., & Travé, A. (2021). Large-scale landslide susceptibility mapping using an integrated machine learning model: A case study in the Lvliang mountains of China. Frontiers in Earth Science, 622.
Yalcin, A., Reis, S., Aydinoglu, A. C., & Yomralioglu, T. (2011). A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena, 85(3), 274-287.
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