School of Engineering (SEng.) Collections
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ItemDesign and assessment of earth-air heat exchanger as an intergrated natural cooling system for net zero tropicl buildings.(Makerere University, 2026-09-11)Earth Air Heat Exchangers (EAHE), represent a promising passive technology for addressing cooling and heating needs in buildings by utilizing the earth's stable subsurface temperature. EAHEs have been extensively used for both space heating and cooling over the course of many years, and this topic remains attractive to researchers. Intensive studies have been carried out on this topic concerning the heat and mass transfer characteristics of the EAHE, design and operational parameters, energy saving potential, feasibility study in different climates and buildings and thermal performance of hybrid EAHE systems (Zhao et al., 2024). At depths between 1 to 4 meters below the ground surface, soil temperatures remain relatively constant throughout the year, typically ranging between 10-25°C depending on geographic location and climate zone (El Khachine et al., 2024). This thermal stability provides a natural heat sink for cooling in dry/hot season and a heat source for warming in cold (Peñaloza Peña et al., 2021). The use of EAHE systems for air conditioning in commercial and industrial settings offers several environmental benefits and is capable of operating in both standalone and hybrid modes (Lattieff et al., 2022). The advantages of EAHE systems are particularly relevant for low-income tropical contexts. These systems require no compressors, refrigerants, or fossil fuel combustion, with only low-power fans or blowers needed to circulate air. This translates to minimal operational costs and maintenance requirements compared to conventional air conditioning. EAHE systems can reduce cooling energy demand by 20-30% while simultaneously improving indoor air quality through continuous fresh air ventilation (Li et al., 2023). The technology is compatible with locally available materials and low-skill construction techniques, making it accessible for implementation in resource-constrained settings. Research across various climate zones has demonstrated the effectiveness of EAHE systems. Studies in tropical and hot-arid climates have shown temperature reductions of 10-16°C during peak summer conditions. The design of net zero buildings incorporating natural temperature harnessing technologies like EAHE systems represents a critical intervention point for low-income tropical regions. By dramatically reducing cooling energy demand through passive strategies while providing affordable, renewable-powered thermal regulation, these buildings can break the cycle of energy poverty, improve health and well-being, and contribute to global climate mitigation efforts. This project explores the technical, economic, and social dimensions of integrating EAHE technology within comprehensive net zero building designs tailored to the specific needs and constraints of low-income tropical communities, contributing to the broader global imperative of achieving universal access to sustainable, healthy, and climate-resilient buildings.
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ItemAssessment of the influence of road geometric design parameters on crash frequency and severity at Bwaise interchange(Makerere University, 2026)Road geometric design parameters are fundamental determinants of road safety, governing vehicle operating speeds, driver workload, braking performance, and the margin available for collision avoidance. Deficiencies in horizontal curvature, longitudinal gradient, lane width, and intersection configuration systematically elevate both crash frequency and severity, yet localized quantitative analyses linking such deficiencies to crash occurrence at urban hotspots in Uganda remain scarce. This study assessed the influence of road geometric design parameters on crash frequency and severity at Bwaise interchange, Kawempe Division, Kampala, over the period 2020-2024. Twenty segments, ten circulatory arcs, and ten approach/exit arm segments produced a panel of 100 segment-year observations from 74 spatially confirmed crash events sourced from Kawempe Police Station and the KCCA Bloomberg Road Safety Database. Geometric parameters were extracted from Ministry of Works and Transport as-built drawings and Google Earth Pro profiles, with spatial allocation in ArcGIS Pro 3.4.2. Poisson, Negative Binomial, and Zero-Inflated Negative Binomial regression were employed for crash frequency, ordered logistic regression for severity, and Crash Modification Factors derived from model Incidence Rate Ratios using the AASHTO Highway Safety Manual framework. The study recorded a mean crash rate of 21.01 crashes per kilometer per year across 20 segments, with four primary hotspots identified. Damage-only crashes dominated at 65.92%, while 18.1% involved injury or fatality; all recorded fatalities occurred on approach arm segments with steep gradients, three of which exceeded the recommended maximum of 4%, the worst recording 7.01%. The Negative Binomial model was confirmed as the most appropriate crash frequency estimator. Each one percentage point increase in longitudinal gradient was associated with a 17.8 -19.5% increase in expected annual crash count. Horizontal curve radius was the strongest geometric predictor, with each one-meter increase reducing expected crashes by 2.9-5.0% (r = -0.675). Crash Modification Factor analysis indicated that gradient reduction from 7.01% to the recommended 4.0% was associated with a 39-41% crash reduction, the single highest-impact intervention at the junction. Ordered logistic regression confirmed gradient and curve radius as significant predictors of crash severity. These findings confirm that geometric design non-compliance is systematically associated with elevated crash frequency and severity at Bwaise interchange.
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ItemFlood forecasting and early warning In Nyamwamba river catchment using machine learning(Makerere University, 2026-06)The Nyamwamba River Catchment in Kasese District experiences frequent, destructive flash floods driven by steep topography, heavy rainfall, and climate variability, exposing critical vulnerabilities in regional disaster management due to sparse gauging infrastructure and reactive response mechanisms. This study develops an integrated machine learning-based flood forecasting and early warning framework to quantify flood risk in the Nyamwamba catchment and to provide timely, location-specific warnings for vulnerable downstream communities. Predictive modeling using Extreme Gradient Boosting (XGBoost)—incorporating binary classification for flood occurrence and regression for river discharge prediction—was forced with multi-source meteorological and remote sensing data (ERA5-Land baseline 2000–2025 and Open-Meteo APIs), utilizing engineered hydrological features such as antecedent precipitation indices, rolling statistics, and flash-flood threats. Spatial flood routing and overland flow simulations were further conducted within an ArcGIS environment to evaluate catchment response times and critical lead intervals under high-intensity precipitation scenarios. Outputs included optimized XGBoost classification and regression models, a real-time operational forecasting pipeline, GIS-based spatial flood simulations demonstrating sub-hourly catchment response times, and an automated multi-channel early warning platform capable of dispatching instant SMS and email alerts to at-risk stakeholders.
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ItemAssessment of flow variability and operation efficiency for the Nyagak run of rivers hydropower plant in Zombo district.(Makerere University, 2026-06)The Nyagak Run-of-River Hydropower Plant (6.6 MW) in Zombo District depends entirely on natural river inflows, making its generation performance highly sensitive to seasonal variability and climate change. This study develops an integrated continuous hydrological–operational modelling framework to quantify flow variability in the Nyagak catchment (≈590km²) and to assess its influence on daily power production. Continuous simulation using HEC-HMS (Deficit and constant, baseflow recession and Clark Unit Hydrograph as the transform method) was forced with observed station and gridded rainfall/temperature data (baseline 2000–2025), calibrated (2000–2019) and validated (2020–2025). Climate-adjusted inflows were generated using bias-corrected CORDEX-Africa projections under RCP4.5 and RCP8.5. Outputs included a calibrated continuous hydrological model for the Nyagak catchment, daily inflow time-series for baseline and future scenarios, daily power production estimates computed from simulated flows, and scenario comparisons of generation reliability under low- and high-flow conditions.
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ItemInfluence of infiltration and soil moisture models on urban hydrological model reliability(Makerere University, 2026-06-26)This study investigates the influence of infiltration and soil moisture models on the reliability of urban hydrological models, focusing on guiding the selection of Blue-Green Infrastructure (BGI) for stormwater management. Infiltration models such as Horton and Green-Ampt, combined with dynamic representation of soil moisture, fundamentally affect predictions of runoff generation and flood risks in urban catchments. Given that the Kinawataka catchment contains compacted urban soils, heterogeneous pervious–impervious patterns, and frequently saturated low-lying areas, accurately modelling infiltration and soil moisture becomes essential for improving flood prediction reliability. The results from these hydrological models provide critical insights that help in choosing appropriate BGI solutions by indicating how infiltration and soil moisture conditions impact urban runoff and flood mitigation potential. This approach supports optimized BGI selection to enhance urban flood resilience and sustainable water management strategies. The study aims to enhance the understanding of runoff and infiltration processes within the Kinawataka catchment by conducting rainfall simulations and applying hydrological modelling techniques. This research provides a novel contribution by jointly evaluating infiltration models and soil moisture dynamics specifically for an urban Ugandan catchment, an aspect that has received limited attention in previous studies. The research will generate reliable data to support the strategic selection and design of Blue-Green Infrastructure (BGI) interventions that are appropriate for the local urban context. Ultimately, by clarifying how infiltration and soil moisture representations influence hydrological model reliability, the study contributes to improving flood forecasting, model calibration, and urban flood resilience in data-scarce and rapidly urbanizing cities such as Kampala.