Landslide Susceptibility Assessment Using Frequency Ratio: A Case Study of Kiliba (Sud-Kivu/DR Congo)
Abstract
The conversion of natural ecosystems into agricultural or urban areas can alter geomorphological processes, particularly in landslide-prone regions. Landslides in such areas can be triggered by natural events like heavy rainfall or earthquakes, as well as human activities such as deforestation and unplanned urbanization. Their impacts can be severe, resulting in significant socio-economic damage. Uvira Territory, in the western part of the East African Rift Valley, frequently experiences these events. It is located between the Ruzizi Plain to the east and the Mitumba Mountains to the west, with diverse geology comprising precambrian formations and quaternary sediments. The topography has a stepped relief with altitudes ranging from 770 to 3250 meters. The climate is tropical and humid, with a rainy season from September to May and a dry season from June to August. The area features coastal plains and mountain slopes, with many waterways flowing into Lake Tanganyika or the Ruzizi River. Detailed studies on landslide susceptibility mapping in this area are limited. This study aimed to map landslide susceptibility in the Kiliba River catchment to assist policymakers in land management. It used Google Earth images, GPS surveys, and field observations, applying a Frequency Ratio (FR) model that considered seven geo-environmental factors: slope, aspect, elevation, distance to watercourses, topographic wetness index, vegetation cover, and land use/landcover. The inventory identified 106 landslides in the study area, with densities of up to 11.25 landslides per km². Key factors in predicting landslide susceptibility were slope, elevation, and vegetation cover. The prediction model had an accuracy rate of 72.2%. The study shows that regions at medium elevation with steep slopes and low vegetation cover are mostly at risk for landslides. These findings are key for land management and disaster prevention. Future studies should consider more factors and a broader geographic range to enhance risk management
Downloads
References
Akter, T., Quevauviller, P., Eisenreich, S. J., & Vaes, G. (2018). Impacts of climate and land use changes on flood risk management for the Schijn River, Belgium. Environmental Science and Policy, 89(July), 163–175. https://doi.org/10.1016/j.envsci.2018.07.002
Alin, S. R., O’Reilly, C. M., Cohen, A. S., Dettman, D. L., Palacios-Fest, M. R., & McKee, B. A. (2002). Effects of land-use change on aquatic biodiversity: A view from the paleorecord at Lake Tanganyika, East Africa. Geology, 30(12), 1143–1146. https://doi.org/10.1130/0091-7613(2002)030<1143:EOLUCO>2.0.CO;2
Azanga, E., Majaliwa, M., Kansiime, F., Mushagalusa, N., Karume, K., & Tenywa, M. M. (2016). Land-use and land cover, sediment and nutrient hotspot areas changes in Lake Tanganyika Basin. African Journal of Rural Development, 1(1), 75–90.
Broothaerts, N., Kissi, E., Poesen, J., Van Rompaey, A., Getahun, K., Van Ranst, E., & Diels, J. (2012). Spatial patterns, causes and consequences of landslides in the Gilgel Gibe catchment, SW Ethiopia. Catena, 97, 127–136. https://doi.org/10.1016/j.catena.2012.05.011
Burrough, P. A., & McDonell. (1986). Principles of geographical. In Information systems for land resource assessment …. https://www.academia.edu/download/2438559/9fjg8q78n4wux4l.pdf
Dai, F. C., & Lee, C. F. (2003). A spatiotemporal probabilistic modelling of storm-induced shallow landsliding using aerial photographs and logistic regression. Earth Surface Processes and Landforms, 28(5), 527–545. https://doi.org/10.1002/esp.456
Defries, R. S., Rudel, T., Uriarte, M., & Hansen, M. (2010). Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nature Geoscience, 3(3), 178–181. https://doi.org/10.1038/ngeo756
Depicker, A., Jacobs, L., Delvaux, D., Havenith, H. B., Maki Mateso, J. C., Govers, G., & Dewitte, O. (2020). The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift. Geomorphology, 353, 106886. https://doi.org/10.1016/j.geomorph.2019.106886
Dewitte, O., Dille, A., Depicker, A., Kubwimana, D., Maki Mateso, J. C., Mugaruka Bibentyo, T., Uwihirwe, J., & Monsieurs, E. (2021). Constraining landslide timing in a data-scarce context: from recent to very old processes in the tropical environment of the North Tanganyika-Kivu Rift region. Landslides, 18(1), 161–177. https://doi.org/10.1007/s10346-020-01452-0
Du, G. liang, Zhang, Y. shuang, Iqbal, J., Yang, Z. hua, & Yao, X. (2017). Landslide 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. https://doi.org/10.1007/s11629-016-4126-9
Gill, J. C., & Malamud, B. D. (2014). Reviewing and visualizing the interactions of natural hazards. Reviews of Geophysics, 52(4), 680–722. https://doi.org/10.1002/2013RG000445
Guo, C., Montgomery, D. R., Zhang, Y., Wang, K., & Yang, Z. (2015). Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology, 248, 93–110. https://doi.org/10.1016/j.geomorph.2015.07.012
Hidayat, S., Pachri, H., & Alimuddin, I. (2019). Analysis of Landslide Susceptibility Zone using Frequency Ratio and Logistic Regression Method in Hambalang, Citeureup District, Bogor Regency, West Java Province. IOP Conference Series: Earth and Environmental Science, 280(1), 0–11. https://doi.org/10.1088/1755-1315/280/1/012005
Hong, H., Xu, C., & Bui, D. T. (2015). Landslide Susceptibility Assessment at the Xiushui Area (China) Using Frequency Ratio Model. Procedia Earth and Planetary Science, 15, 513–517. https://doi.org/10.1016/j.proeps.2015.08.065
Ilunga, L. (1991). Morphologie, volcanisme et sedimentation dans le rift du Sud-Kivu. Bulletin - Societe Geographique de Liege, 27, 209–228.
Ilunga, L. (2006). Study the main sites of erosion in Uvira (D.R. Congo). Geo-Eco-Trop, 30(2), 1–12.
Jacobs, L., Dewitte, O., Poesen, J., Maes, J., Mertens, K., Sekajugo, J., & Kervyn, M. (2017). Landslide characteristics and spatial distribution in the Rwenzori Mountains, Uganda. Journal of African Earth Sciences, 134, 917–930. https://doi.org/10.1016/j.jafrearsci.2016.05.013
Kannan, M., Saranathan, E., & Anabalagan, R. (2013). Landslide vulnerability mapping using frequency ratio model: A geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India. Arabian Journal of Geosciences, 6(8), 2901–2913. https://doi.org/10.1007/s12517-012-0587-5
Khan, H., Shafique, M., Khan, M. A., Bacha, M. A., Shah, S. U., & Calligaris, C. (2019). Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan. Egyptian Journal of Remote Sensing and Space Science, 22(1), 11–24. https://doi.org/10.1016/j.ejrs.2018.03.004
Kirschbaum, D. B., Adler, R., Hong, Y., Kumar, S., Peters-Lidard, C., & Lerner-Lam, A. (2012). Advances in landslide nowcasting: Evaluation of a global and regional modeling approach. Environmental Earth Sciences, 66(6), 1683–1696. https://doi.org/10.1007/s12665-011-0990-3
Knapen, A., Kitutu, M. G., Poesen, J., Breugelmans, W., Deckers, J., & Muwanga, A. (2006). Landslides in a densely populated county at the footslopes of Mount Elgon (Uganda): Characteristics and causal factors. Geomorphology, 73(1–2), 149–165. https://doi.org/10.1016/j.geomorph.2005.07.004
Larsen, I. J., & Montgomery, D. R. (2012). Landslide erosion coupled to tectonics and river incision. Nature Geoscience, 5(7), 468–473. https://doi.org/10.1038/ngeo1479
Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33–41. https://doi.org/10.1007/s10346-006-0047-y
Lepore, C., Kamal, S. A., Shanahan, P., & Bras, R. L. (2012). Rainfall-induced landslide susceptibility zonation of Puerto Rico. Environmental Earth Sciences, 66(6), 1667–1681. https://doi.org/10.1007/s12665-011-0976-1
Mandal, B., & Mandal, S. (2018). Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya, India. Advances in Space Research, 62(11), 3114–3132. https://doi.org/10.1016/j.asr.2018.08.008
Maki, J. M., Monsieurs, E., Jacobs, L., & Mateso, L. B. (2016). A regional inventory of the landslide processes and the elements at risk on the Rift flanks west of Lake Kivu (DRC). Geophysical Research Abstracts, 18(April), 6345.
Maki, J. M., & Olivier, D. (2014). Towards an inventory of landslide processes and the elements at risk on the Rift flanks West of Lake Kivu (DRC). Geo-Eco-Trop, 18(January), 137–154.
Migombano Useni, P. (2011). Évaluation et cartographie par SIG du risque lié aux glissements de terrain à Bukavu (Sud Kivu, RD CONGO). 61.
Mohammady, M., Pourghasemi, H. R., & Pradhan, B. (2012). Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences, 61, 221–236. https://doi.org/10.1016/j.jseaes.2012.10.005
Mölsä, H., Reynolds, J. E., Coenen, E. J., & Lindqvist, O. V. (1999). Fisheries research towards resource management on Lake Tanganyika. Hydrobiologia, 407, 1–24. https://doi.org/10.1023/A:1003712708969
Moore, R. R. (1978). Rainfall Erosivity in East Africa: Kenya, Tanzania and Uganda. Geografika Annaler. Series A. Physical Geography. 1979. 61; 147-156. Geografika Annaler. Series A. Physical Geography, 61, 147–156.
Mugaruka Bibentyo, T. (2018). Distribution des glissements de terrain dans un environnement en mutation: Focus sur la gorge de la Ruzizi située entre la RD Congo et le Rwanda. 66.
Nacishali Nteranya, J. (2021a). Cartographie de l’érosion hydrique des sols et priorisation des mesures de conservation dans le territoire d’Uvira (République démocratique du Congo). VertigO, Volume 20 Numéro 3. https://doi.org/10.4000/vertigo.28888
Nacishali Nteranya, J. (2021b). Cartographie de l’érosion hydrique des sols et priorisation des mesures de conservation dans le territoire d’Uvira (République démocratique du Congo). VertigO, Volume 20 numéro 3, 1–33. https://doi.org/10.4000/vertigo.28888
Ozer, P. (2009). Introduction aux risques naturels. http://orbi.ulg.be/handle/2268/22442
Park, N. W. (2011). Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environmental Earth Sciences, 62(2), 367–376. https://doi.org/10.1007/s12665-010-0531-5
Park, S., Choi, C., Kim, B., & Kim, J. (2013). Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental Earth Sciences, 68(5), 1443–1464. https://doi.org/10.1007/s12665-012-1842-5
Persichillo, M. G., Bordoni, M., & Meisina, C. (2017). The role of land use changes in the distribution of shallow landslides. Science of the Total Environment, 574, 924–937. https://doi.org/10.1016/j.scitotenv.2016.09.125
Pimiento, E. (2010). Shallow Landslide Susceptibility Modelling and Validation. 6, 119.
Rabby, Y. W., & Li, Y. (2020). Landslide susceptibility mapping using integrated methods: A case study in the chittagong hilly areas, bangladesh. Geosciences (Switzerland), 10(12), 1–26. https://doi.org/10.3390/geosciences10120483
Rossi, M., & Reichenbach, P. (2016). LAND-SE: A software for statistically based landslide susceptibility zonation, version 1.0. Geoscientific Model Development, 9(10), 3533–3543. https://doi.org/10.5194/gmd-9-3533-2016
Roy, J., & Saha, S. (2019). Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India. Geoenvironmental Disasters, 6(1). https://doi.org/10.1186/s40677-019-0126-8
Saley, M. B., Kouamé, F. K., Penven, M. J., Biémi, J., & Boyossoro Kouadio, H. (2005). Cartographie Des Zones À Risque D ’ Inondation Dans La Région Semi-Montagneuse À Louest De La Côte D ’ Ivoire : Apports Des Mna Et De L ’ Imagerie Satellitaire. Télédétection, 5(1-2–3), 53–67.
Shafique, M., van der Meijde, M., & Khan, M. A. (2016). A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing prospective. Journal of Asian Earth Sciences, 118, 68–80. https://doi.org/10.1016/j.jseaes.2016.01.002
Silalahi, F. E. S., Pamela, Arifianti, Y., & Hidayat, F. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters, 6(1). https://doi.org/10.1186/s40562-019-0140-4
Solaimani, K., Mousavi, S. Z., & Kavian, A. (2013). Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arabian Journal of Geosciences, 6(7), 2557–2569. https://doi.org/10.1007/s12517-012-0526-5
Xiao, J., & Moody, A. (2005). A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98(2–3), 237–250. https://doi.org/10.1016/j.rse.2005.07.011
Xu, C., Xu, X., Dai, F., & Saraf, A. K. (2012). Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers and Geosciences, 46, 317–329. https://doi.org/10.1016/j.cageo.2012.01.002
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. https://doi.org/10.1016/j.catena.2011.01.014
Yilmaz, I. (2010). Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: Conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61(4), 821–836. https://doi.org/10.1007/s12665-009-0394-9
Zamukulu, P. (2020). Susceptibilité des sols d’Idjwi aux risques d’érosion hydrique et glissement de terrain, R.D. Congo. In Mémoire Master, inédit, Faculté des Sciences Agronomiques et de l’Environnement, UEA Dans (Issue 1).
Copyright (c) 2024 Isaac Chunga Chako, Toussaint Mugaruka Bibentyo, Guy Ilombe Mawe, Charles Nzolang, PhD, Majaliwa Mwanjalolo, PhD, Fils Makanzu Imwangana, PhD
This work is licensed under a Creative Commons Attribution 4.0 International License.
