A comparative analysis of training methods for artificial neural network rainfall-runoff models

Authors:
Sanaga Srinivasulu;Ashu Jain
Affiliations:
Center for Spatial Information Technology, Institute of Post Graduate Studies and Research, Jawaharlal Nehru Technological University, Hyderabad 500 028, India;Department of Civil Engineering, Indian Institute of Technology, Kanpur 208016, India
Venue:
Applied Soft Computing
Year:
2006

Citing 3
Cited 8

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
An evaluation of artificial neural network technique for the determination of infiltration model parameters

Applied Soft Computing

Hybrid neural network models for hydrologic time series forecasting

Applied Soft Computing
Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

Applied Soft Computing
Suitability of different neural networks in daily flow forecasting

Applied Soft Computing
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

Mathematics and Computers in Simulation
HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR FORECASTING TIME-SERIES DATA

Applied Artificial Intelligence
Feature extraction for time-series data: An artificial neural network evolutionary training model for the management of mountainous watersheds

Neurocomputing
Information recovery from measured data by linear artificial neural networks-An example from rainfall-runoff modeling

Applied Soft Computing
Application of visual clustering properties of self organizing map in machine-part cell formation

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper compares various training methods available for training multi-layer perceptron (MLP) type of artificial neural networks (ANNs) for modelling the rainfall-runoff process. The training methods investigated include the popular back-propagation algorithm (BPA), real-coded genetic algorithm (RGA), and a self-organizing map (SOM). A SOM was used to first classify the input-output space into different categories and then develop feed-forward MLP models for each category using BPA. The daily average rainfall and streamflow data derived from an existing catchment were employed to develop all ANN models investigated in this study. A wide variety of standard statistical performance evaluation measures were employed to evaluate the performances of various ANN models developed. The results obtained in this study indicate that the approach of first classifying the input-output space into different categories using SOM and then developing separate ANN models for different classes trained using BPA performs better than the approach of developing a single ANN rainfall-runoff model trained using BPA. The ANN rainfall-runoff model trained using RGA was able to provide a better generalization of the complex, dynamic, non-linear, and fragmented rainfall-runoff process in comparison with the other approaches investigated in this study. It has been found that the RGA trained ANN model significantly outperformed the ANN model trained using BPA, and was also able to overcome certain limitations of the ANN rainfall-runoff model trained using BPA reported by many researchers in the past. It is noted that the performances of various ANN models should to be evaluated using a wide variety of statistical performance indices rather than relying on a few global error statistics normally employed that are similar in nature to the global error minimized at the output layer of an ANN.