7 hours flood prediction modelling using NNARX structure: Case study Terengganu

Abstract

Most of the countries have paid great attention to flood water level prediction since flood may damages people's life and property. Currently, hydrological models were used to get the prediction of flood water levels. However, this involved with various parameters such as hydrometric measurements, weather forecasts and hydrogeological maps, in addition to water level, temperature and flow observations. Therefore, such models are usually difficult to develop especially when describing large and complex system such as the dynamic of flood water level. Since flood water level fluctuate highly nonlinear, it is very difficult to predict the flood water level. Since Artificial Neural Network was proven to be best model to handle nonlinear cases, this paper proposed flood prediction modelling using Artificial Neural Network (ANN) technique with 7 hours prediction time. The area of study was Terengganu where the input parameters used in this modelling were river water level at upstream stations whereas output parameter was river water level at downstream station or so called flood location. 542 samples data collected from 15/12/2011 till 19/12/2011 were used for modelling, 542 samples data collected from 26/2/2012 till 1/3/2011 were used for model validation and 428 samples data collected data from 4/6/2013 till 7/6/2013 were used for model testing. Results showed that NNARX model successfully predicted flood water level 7 hours ahead of time.