Track: Modeling and Simulation

Abstract

Flooding disaster happens almost annually in Pakistan. With no real solution to this incident, loss of human lives and wealth are inevitable. Heavy rainfall is an important aspect which contributes to flooding. Monitoring rainfall remains an integral part of flood defense system. One of the most leading method to predict flood is by developing a forecast model of rainfall-runoff. Rainfall and river flow relation are very much subjective with various affecting factors.  Artificial Neural Network (ANN) is preferred to model hydro system because of its accountability of nonlinear dynamics of water flow.  A case study is done on a flood-prone river basin in Jhelum river, in Kashmir. Rainfall data of 5 hydrologic stations and river level are used as the input. The performance of the learning algorithms involved were evaluated with a coefficient of determination (R) and Mean Square Error (MSE). The network configuration of the ANN which best fit each algorithm is determined.   The results achieved showed the promising advantage of LM as compared to BR and PSO.