Water Demand –Supply Gap Prediction and Driving Factors Analysis

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dc.contributor.advisorAgizew Nigussie
dc.contributor.authorGenet Hailemicheal
dc.date.accessioned2025-05-14T09:31:37Z
dc.date.available2025-05-14T09:31:37Z
dc.date.issued2024
dc.description.abstractThis study uses FFANNs to develop a predictive model for urban water demand supply gap forecasting. The model considers nine independent variables and uses gird search hypermeter tuning and found that Bayesian regularization back propagation training algorithm and hyperbolic tangent sigmoid transfer function (tansig) with 40 neurons in the hidden layer with seven input variables best performing model and concluded that MLP NN is an effective tool for understanding and simulating non linear water demand supply gap behavior, benefiting water providers and decision makers.In addition, this study decomposed water demand factors in Addis Ababa city using LDMI decomposition analysis methods. Ten influential factors were identified, including rainfall, temperature, sunshine hours, relative humidity, population, industrial growth rate, economic growth, tourist number, livestock number, and water tariff. The study found that except population and livestock factors contributing to total water demand change varied at different years, with industrial growth, rainfall, humidity, tourist number, and sunshine hours inhibiting change during 2021 2022. Generally, for driving factor analysis LMDI method could be a good factor decomposer provided that data interaction and independence is absent.
dc.identifier.urihttps://etd.aau.edu.et/handle/123456789/5455
dc.language.isoen_US
dc.publisherAddis Ababa University
dc.subjectwater demand
dc.subjectwater supply
dc.subjectgap
dc.subjectprediction ANN
dc.subjectdriving factors
dc.subjectdecomposition
dc.subjectLMDI
dc.titleWater Demand –Supply Gap Prediction and Driving Factors Analysis
dc.typeThesis

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Water Supply and Environmental Engineering