Browsing by Author "Esagu, John Calvin"

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Assessment of landslide susceptibility and settlement exposure via Geospatial techniques in Bulambuli distrcit, eastern Uganda
(Applied Environmental Research, 2025-10-15) Mulabbi, Andrew; Esagu, John Calvin; Akello, Gertrude; Turyahabwe, Remigio
Landslide susceptibility is a significant concern in Elgon County, Uganda, particularly during the rainy season. This vulnerability is attributable to several factors, including steep slopes, fertile soils, and dense settlements on volcanic ridges. Landslide susceptibility maps are important in mitigating the risk particularly at the local level. The objectives of this study were 1) to model landslide susceptibility via an interpretable machine-learning model, 2) to identify the most influential factors for landslide susceptibility in the study area, and 3) to assess the exposure of settlements to landslide risk. This study employed the XGBoost model trained on nine conditioning factors via GIS data. Exposure analysis was performed through the zonal statistics and spatial overlay of the landslide susceptibility map with the settlement footprint data and classified into four risk exposure classes. The results show that the XGBoost model attained an AUC of 95.2%, indicating its precision. The results further revealed that approximately 50% of the slopes are susceptible to landslides and that 76% of the settlements in the study area are highly exposed to landslide risk. Bulugunya, Sisiyi, Lusha, and Buginyanya subcounties located on the middle slopes are the most susceptible areas in Elgon County and have relatively high settlement exposure because of the overlap of dense settlements with unstable terrain. The SHAP analysis identified slope, elevation, and the NDVI as the key influencing factors of susceptibility. This study highlights the importance of conducting detailed, local-scale landslide susceptibility and risk exposure mapping as necessary for risk and vulnerability assessment. The generation of such maps has the potential to inform land-use planning and risk-reduction strategies, thus offering significant advantages over regional models. Furthermore, by interpreting the XGBoost model, this study provides valuable insights into the decision-making processes of machine learning models, promoting their practical application in designing appropriate disaster mitigation plans.
Flood inundation and damage assessment of the degraded Semliki River plains using SAR data, Google Earth Engine, and GIS techniques
(Journal of Degraded and Mining Lands Management, 2025-06-21) Mulabbi, Andrew; Esagu, John Calvin; Akello, Gertrude; Turyahabwe, Remigio
The Semliki River valley in Ntoroko district has experienced devastating annual floods since 2019. Recurrent floods in Ntoroko District have displaced thousands and devastated pasturelands, disrupting livelihoods. Therefore, rapid assessment of flooded areas is crucial for developing effective mitigation strategies, disaster preparedness plans, and proactive policies to enhance resilience and mitigate the impact of future flood events. This study introduced a combined approach using Synthetic Aperture Radar (SAR) imagery and a digital elevation model (DEM) to map flood extent, depth, and building exposure in the Semliki Valley. Using Sentinel-1 SAR images taken both before and during the flood, combined with the ALOS PALSAR DEM, inundated areas and flood depths were determined, based on thresholding the SAR backscatter of the VH polarisation images. The flood extent maps were generated using Google Earth Engine and GIS techniques to create depth maps by subtracting the surface elevation from the height/surface of the flood waters. Building exposure and impact analysis for two flood events was ascertained through spatial join and overlay. The results showed that the 2023 flood event inundated approximately 1,968 hectares, including 1,553 hectares of pastureland and 74 buildings, while the 2024 event covered 1,139 hectares, equally inundating 1,050 hectares of pastureland and 54 buildings. Further analysis revealed that despite the smaller extent, the 2024 flood event caused a severe impact on the buildings compared to the 2023 flood disaster.
Prediction of inundation due to Kabuyanda dam failure and its impact on the communities of Isingiro district, western Uganda
(Kyambogo University[unpublished work], 2022-09-01) Esagu, John Calvin
Globally, dams are indispensable in overcoming hindrances posed by climate change through ensuring sustainable water supply for irrigation. However, in case of failure, Dam floods cause devastating effects in fatalities and financial losses. The study focused on predicting the flood extent in case of Kabuyanda dam failure, determining the exposure of land use types, estimate the damages/losses resulting from the inundation in the eventuality of the dam failure and establish possible flood mitigation measures in Isingiro district. A cross-sectional survey design was adopted following both qualitative and quantitative approaches. Hydrologic Engineering Center River Analysis System model was used to predict flow simulation while depth-damage stage, and replacement values were considered for risk analysis. The data used was acquired from Uganda Beareau of statistics, Ministry of education and sports, Ministry of health, Ministry of water and environment, National risk and vulnerability atlas for Uganda, key informant interviews, and google earth. Geo-spatial analysis, descriptive statistics, and Nvivo software were used to analyze the data. The study revealed that in the eventuality of a dam failure, the spatial extent of floodwater would inundate approximately 1,745.65 hectares of land totaling 43.20% of the Kabuyanda flood plain (4040.60 hectares) with flood velocity and depth ranging between 11.99 m/s to 0 m/s and 0-8.4 m respectively. About 5, 756 people, 319.15 hectares of croplands, 178 roads, 8 schools, police post, and a medical center are exposed to potential dam-break inundation and damage with loss estimate totaling approximately 4,158,130,546 UGS. Flood preparedness will be more vital than response and recovery. Low flood zone and uphill regions are suggested as evacuation centers; river banks for forestry and flood fringe for crop cultivation. Conclusively, elevation within the flood plain determines water surface movement, damageability while losses depend on flood velocity and depth. Therefore, flood emergency preparedness strategies are a prerequisite in protecting the downstream population, reducing the damages and losses that could to result from potential dam failure. The estimated cost is 1,670,738 USD (5,912.992,392 UGX) towards meeting the activities to mitigate an inundation disaster in Kabuyanda irrigation scheme in the Isingiro notably evacuation and resettlement from the flood danger spots.