Flood Prediction using Rainfall – Runoff Spatial Variation: An Overview of Flood Prediction Models

Abstract

High intensity rainfall and associated floods have become frequent in most cities and urban areas in recent years, within the lower reaches of the Niger Delta. The magnitude and time variation of rainfall and associated runoff has proved more difficult to predict. This is mainly as a result of the inherent stochastic nature of such events. The need for a systematic approach to flood forecasting based on rainfall is of the essence. This work presents rainfall data obtained for an urban city located within the Niger Delta proximal location to Port Harcourt, Rivers State, with a corresponding time series analysis of the data as the basic input information. The associated runoff data will be simulated to investigate runoff related events and the spatial variation of flow directions within the catchment under study. The review of selected appropriate mathematical models and graphical comparisons between quantities of rainfalls observed form the theoretical frame for an effective flood prediction procedure. The considered time-series analysis techniques, and especially those based on the use of Artificial Neural Network (ANN), provide a significant improvement in the flood forecasting accuracy in comparison to the use of simple prediction approaches, which are often applied in hydrological practice.