This study aims to assess local perception and adaptation strategies to landslide occurrences in the Kivu catchment of Rwanda. Qualitative, quantitative, and combined methods were applied for data collection to investigate how the locals perceived the types of landslides, origins, impacts, contributing variables to landslides, and adaptation strategies to landslides. The investigation conducted interviews with 384 residents from the six districts of the study area, key informant interviews and field observations. The findings showed that falls, flows, slides and spreads are the types of landslides frequently occurring in the study area. Heavy rain, steep slope, road construction, inappropriate agriculture practices, deforestation, earthquake, and mining were found to cause landslides with effects such as human fatalities, infrastructure damage, injuries, and property losses. Different measures are adopted for landslide risk reduction, including agroforestry, terracing, stormwater drainage systems, and relocating people from high-risk areas. Residents have a positive opinion of their community’s approach to managing landslides effectively. However, the findings revealed gaps in cooperation between the parties where Non-Governmental Organizations do not appear to be active participants during intervention activities for landslide management in the study area. Regression analysis has shown that deforestation, inappropriate agricultural practices, roads construction, earthquakes, and climate change are the key factors significantly contributing to landslide occurrences in the study area. Further research must be conducted on the subject using a variety of methodologies, notably those related to applied artificial intelligence in order to enrich the literature presently available on landslides in the Kivu catchment of Rwanda.

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment. This study adopted a GIS-based Analytical Hierarchy Process (GIS-AHP) integrated with local community knowledge to evaluate flood susceptibility using 10 conditioning factors: Topographic Wetness Index (TWI), Elevation, Rainfall, Slope, Land use/Land cover (LULC), Soil types, Normalized Difference Vegetative Index (NDVI), Distance to roads, Distance to rivers, and drainage density. These factors were selected based on their established influence on flood susceptibility as identified through literature review, expert consultation, and local community experience in the flood-affected zones. Spatial datasets were gathered from remote sensing platforms, Digital Elevation Models, Meteorological records, and existing geospatial databases, and were processed within a GIS environment. The pairwise comparison matrix of the AHP was used to derive weighting coefficients representing the relative contribution of each factor in inducing flood, with Rainfall (0.23), Slope (0.15), Distance to river (0.12), drainage density (0.12), and Elevation (0.11) as the most influential criteria. The findings revealed that 88.4% of the study area falls within a moderate flood-susceptible zone, whereas 6.4% and 5.2% fall within high and low susceptible zones, respectively. The current study indicates that damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate 0.97 as an overall accuracy, confirming strong model performance and reliability. The proposed adaptive strategies for enhancing flood resilience include improvement in land use planning, use of early warning systems, and sustainable catchment management.