Flood Risk Management in the Tshangu District: Case Study of the Communes of Kimbanseke, Ndjili, Kinseso, Matete and Masina, in the City-province of Kinshasa
Aug 2026· International Journal For Multidisciplinary Research· Vol 8· 0 citations
Abstract
This research focuses on analyzing flood susceptibility in the Democratic Republic of Congo, specifically in the Tshangu River basin in the eastern part of Kinshasa. Floods represent a major problem in this region, with significant human, material, and socio-economic consequences. The main objective of this research is to conduct a quantified and geospatial assessment of flood susceptibility in the Tshangu River basin. The approach employed involves the use of remote sensing, geographic information systems (GIS), and the ratio frequency (RF) model. This probabilistic method allows us to create maps locating areas likely to be affected by flooding. This methodological choice stems from the limited data available and the challenges of accessing the area in our study zone. The RF model is used to quantify the probability of flooding in identified at-risk areas based on the physical characteristics of the watershed and historical flood data. The results of the analysis using the FR model identified three distinct flood-prone areas. The main factors influencing the classification of flood zones are proximity to watercourses, river shape and density, the Stream Power Index (SPI), topography, and rainfall. Factors influencing flooding in the Tshangu basin are numerous, including heavy rainfall, the region's geomorphology, impermeable soils, and unplanned urbanization. These factors contribute to soil saturation, water accumulation in low-lying areas, and drainage problems. In conclusion, recommendations are proposed to mitigate flood risks in the Tshangu basin. These include improving drainage infrastructure, watershed management, urban planning, raising awareness and educating residents, and coordinating interventions among local authorities, government agencies, NGOs, and local communities.
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