Landslides Hazard Mapping in Rwanda Using Bivariate Statistical Index Method

Nahayo, L. and Mupenzi, C. and Habiyaremye, G. and Kalisa, E. and Udahogora, M. and Nzabarinda, V. and Li, L. (2019) Landslides Hazard Mapping in Rwanda Using Bivariate Statistical Index Method. Environmental Engineering Science, 36 (8). pp. 892-902.

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Landslides hazard mapping (LHM) is essential in delineating hazard prone areas and optimizing low cost mitigation measures. This study applied the Geographic Information System and statistical index method in LHM in Rwanda. Field surveys identified 336 points that were employed to construct a landslides inventory map. Ten landslides predicting factors were analyzed: normalized difference vegetation index, elevation, slope, aspects, lithology, soil texture, distance to rivers, distance to roads, rainfall, and land use. The factor variables were converted into categorized variables according to the percentile divisions of seed cells. Then, values of each factor’s class weight were calculated and summed to create landslides hazard map. The estimated hazard map was split into five hazard classes (very low, low, moderate, high, and very high). The results indicated that the northern, western, and southern provinces are largely exposed to landslides hazard. The major landslides hazard influencing factors are elevation, slope, rainfall, and poor land management. Overall, this LHM would help policy makers to recognize each area’s hazard extent, key triggering factors, and the required hazard mitigation measures. These measures include planting trees to enhance vegetation cover and reduce the runoff, and construction of buildings on low steep slope areas to reduce people’s hazard exposure; while agroforestry and bench terraces would reduce sediments that take out the exposed soil (erosion) and pollute water quality.

Item Type:
Journal Article
Journal or Publication Title:
Environmental Engineering Science
Additional Information:
Final publication is available from Mary Ann Liebert, Inc., publishers
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Deposited On:
01 Oct 2019 09:20
Last Modified:
21 Sep 2023 02:42