Modular neural network approach for short term flood forecasting a comparative study

Rahul P,A A

doi:10.14569/ijacsa.2010.010514

Abstract

The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Modular feedforward neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modular neural network modeling. Methodologies and techniques for four models are presented in this paper and a comparison of the short term runoff prediction results between them is also conducted. The prediction results of the Modular feedforward neural network with model two indicate a satisfactory performance in the three hours ahead of time prediction. The conclusions also indicate that Modular feedforward neural network with model two is more versatile than other and can be considered as an alternate and practical tool for predicting short term flood flow.

Highlights

The main focus of this research is development of Artificial Neural Network (ANN) models for short term flood forecasting, determining the characteristics of different neural network models
We demonstrate four different models of Modular feedforward neural network (M FF) models for real time prediction of runoff at the dam and compare the effectiveness of these methods
An ANN-based short-term runoff forecasting system is developed in this work

Summary

INTRODUCTION

The main focus of this research is development of Artificial Neural Network (ANN) models for short term flood forecasting, determining the characteristics of different neural network models. Neural networks are well suited to modeling systems on a real-time basis, and this could greatly benefit operational flood forecasting systems which aim to predict the flood hydrograph for purposes of flood warning and control[16]. Multilayer perceptrons (MLPs) are feedforward neural networks trained with the standard backpropagation algorithm. They are supervised networks so they require a desired response to be trained. A subset of historical rainfall data from the Wardha River catchment in India was used to build neural network models for real time prediction. We demonstrate four different models of Modular feedforward neural network (M FF) models for real time prediction of runoff at the dam and compare the effectiveness of these methods. We use two hidden layers, tanh activation function with 0.7 momentum and mean squared error of the cross validation set as stopping criteria which give the optimal results

METHODOLOGY

RESULT

Findings

CONCLUSION