Shilehwa, CelsusMakhanu, Sibilike KKhaemba, Alex2024-01-312024-01-312019-09-25https://www.researchgate.net/publication/338172718_Application_of_Artificial_Neural_Network_Model_In_Forecasting_Water_Demand_Case_of_Kimilili_Water_Supply_Scheme_Kenyahttps://repository.nrf.go.ke/handle/123456789/486otable water treatment and supply systems are designed and constructed to deliver adequate amounts of water to meet consumer demand requirements, consequently water demand forecasting is essential for design and operations management of treated water supply systems. Correct prediction of time varying water demand trends and the critical water demand values determines the extent to which a network can satisfy critical demand and maintain economic efficiency. The aim of this study was to forecast Kimilili water supply scheme water demand up to 2030. Kimilili water supply scheme being operated by Nzoia Water Services Company Limited is characterized of rapidly increasing water demand leading to persistent water supply shortages hence unplanned fluctuations in the system water production hours. Artificial Neural Network (ANN) model was utilized to forecast Kimilili water supply scheme water demand. The trained model had good performance with a coefficient of determination (R2) of 0.999972988. The results indicated that Water demand for Kimilili water supply was increasing with time and the general relationship between time and water demand was defined by a sixth order polynomial function given by y = 9e-0x6-1e-05x5+0.0005x4-0.0115x3+0.1178x2+0.1384x+100.48. The study confirmed that ANN can simulate the water demand characteristics of the water supply very wellenMasinde Muliro University of Science and TechnologyArticle