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Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia
Thesis (PhD)--Stellenbosch University, 2026.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2026
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| _version_ | 1867614014517805056 |
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| access_status_str | Open Access |
| author | Hirko, Deme Betele |
| author2 | Du Plessis, Jakobus Andries |
| author_browse | Du Plessis, Jakobus Andries Hirko, Deme Betele |
| author_facet | Du Plessis, Jakobus Andries Hirko, Deme Betele |
| author_sort | Hirko, Deme Betele |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhD)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/136061 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:45:17.761Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/136061 Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia Hirko, Deme Betele Du Plessis, Jakobus Andries Bosman, Adele Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering. Thesis (PhD)--Stellenbosch University, 2026. Hirko, D. B. 2026. Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/b2ce514f-ac8c-422b-802c-cb982c4bcc96 Climate change, rapid urbanisation, and population growth are placing escalating pressure on surface water resources in Ethiopia’s Upper Awash Sub-Basin, a region already grappling with rising agricultural demand and limited water availability. This study presents an integrated modelling framework that combines Machine Learning (ML)techniques with the Water Evaluation and Planning (WEAP) system to evaluate andoptimise surface water allocation under future climate and demographic scenarios. Historical climate patterns from 1948 to 2010 were examined using both observed and satellite-based datasets (Princeton datasets). A Random Forest (RF) algorithm trained on these data achieved high predictive accuracy (R² = 0.97 for training; 0.96 for testing; RMSE = 0.12°C). The analysis revealed a historical warming trend of 0.04 to 0.05°C per year and a gradual decline in precipitation, ranging from 0.22 to 0.40 mm per year. Future climate projections based on CMIP6 under the Shared Socioeconomic Pathways (SSP4.5 and SSP8.5) for the period 2025 to 2075 estimate temperature increases between 0.9°C and 1.6°C per year. Projected precipitation changes range from a 15.5% increase (SSP4.5) between 2025 and 2075 to a 6.3% decline (SSP8.5), indicating heightened uncertainty and variability in regional water availability. These climatic changes are expected to intensify hydrological stress across the basin, reducing baseflow by approximately 46.9 mm per year, increasing evapotranspiration to between 890 mm and 1010 mm annually, and causing soil moisture fluctuations from +16.4 mm to −124.9 mm per year. Simultaneously, the population, projected using linearregression and exponential growth models, is expected to grow from 6.3 million in 2025 to nearly 39 million by 2075, substantially increasing water demand across all sectors. The ML-enhanced WEAP model demonstrated improved forecasting capabilities, with the RF model outperforming the Long Short-Term Memory (LSTM) network (MAE: 0.41 vs. 0.46). SHapley Additive exPlanations (SHAP) analysis identified lagged population growth and unmet demand as the most influential predictors, alongside temperature and drought-related variables. To support adaptive water governance, a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to generate Pareto-optimal water allocation strategies. Two trade-off solutions were identified: (1) an equity-oriented strategy, allocating 38% of water to urban use, 37% to agriculture, and 25% to industry (Gini index = 0.22; demand penalty = 0.10). Here, the Gini index, a measure of distributional fairness across sectors, indicates relatively high equity, while the demand penalty, a measure of unmet demand relative to total demand, remains low. (2) An efficiency-focused strategy, favouring urban (43%) and industrial (25%) sectors over agriculture (32%), offers a 9% economic gain at the cost of a slightly higher demand penalty (0.12), reflecting greater overall efficiency but less balanced distribution. Overall, this research demonstrates the potential of a hybrid ML–WEAP approach to improve long-term water allocation planning in data-scarce, climate-sensitive regions. The framework offers a replicable, evidence-based tool for enhancing climate resilience and promoting equitable and efficient water resource management in the Upper Awash Sub-Basin and beyond. Doctoral 2026-04-21T12:06:49Z 2026-04-21T12:06:49Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/136061 en Stellenbosch University 247 pages : ill. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Hirko, Deme Betele Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia |
| title | Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia |
| title_full | Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia |
| title_fullStr | Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia |
| title_full_unstemmed | Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia |
| title_short | Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia |
| title_sort | using machine learning and the water evaluation and planning model to evaluate climate change impacts on surface water allocation in the upper awash sub basin ethiopia |
| url | https://scholar.sun.ac.za/handle/10019.1/136061 |
| work_keys_str_mv | AT hirkodemebetele usingmachinelearningandthewaterevaluationandplanningmodeltoevaluateclimatechangeimpactsonsurfacewaterallocationintheupperawashsubbasinethiopia |