Title: Modelling of runoff rates for a suburban area in Ibadan, Nigeria

Authors: Blessing F. Sasanya; Sunday O. Adesogan

Addresses: Department of Crop and Soil Science, University of Port Harcourt, P.M.B. 5323, Choba East-West Road, Rivers State, Nigeria ' Department of Civil Engineering, University of Ibadan, Ibadan, Nigeria

Abstract: Accurate estimation and prediction of runoff rates is germane to the performance of water resources development. The importance of simplified and location-based runoff models cannot be overemphasised in water resources planning. Several existing runoff models are generic and not location based. This study therefore aims to establish high performing models for runoff estimation in a suburban area of Ibadan. Three runoff estimation methods [Kothyari, rational and Natural Resources Conservation Service (NRCS)] were compared. Monthly runoff rates were also simulated from monthly rainfall, maximum and minimum temperature using artificial neural network (ANN). The results showed NRCS model was best for runoff estimation. Linear and logarithmic relationships were established between rainfall and runoff, but the relationship was less accurate on a semi logarithmic scale with r = 0.83. Two hidden layer/eight hidden nodes ANN model performed best (RMSE 9.23 mm/month). This study is applicable to design of hydraulic structures and water supply development.

Keywords: runoff estimation; artificial neural network; ANN; water resources; runoff simulation; Nigeria.

DOI: 10.1504/IJEE.2022.128034

International Journal of Environmental Engineering, 2022 Vol.11 No.4, pp.305 - 319

Received: 30 Jan 2021
Accepted: 19 Jan 2022

Published online: 04 Jan 2023 *

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