Development of Nonlinear Model Based on WaveletAN

Authors: Seyed Ahmad Akrami Vahid Nourani S J S Hakim

Publish Date: 2014/05/23

Volume: 28, Issue: 10, Pages: 2999-3018

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Abstract

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management Artificial neural networks ANN have been found efficient particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models However time series prediction based on ANN algorithms is fundamentally difficult and faces some other problems For this purpose one method that has been identified as a possible alternative for ANN in hydrology and water resources problems is the adaptive neurofuzzy inference system ANFIS Nevertheless the data arising from the monitoring stations and experiment might be corrupted by noise signals owing to systematic and nonsystematic errors This noisy data often made the prediction task relatively difficult Thus in order to compensate for this augmented noise the primary objective of this paper is to develop a technique that could enhance the accuracy of rainfall prediction Therefore the wavelet decomposition method is proposed to link to ANFIS and ANN models In this paper two scenarios are employed in the first scenario monthly rainfall value is imposed solely as an input in different time delays from the time t to the time t4 into ANN and ANFIS second scenario uses the wavelet transform to eliminate the error and prepares subseries as inputs in different time delays to the ANN and ANFIS The four criteria as Root Mean Square Error RMSE Correlation Coefficient R 2 Gamma coefficient G and Spearman Correlation Coefficient ρ are used to evaluate the proposed models The results showed that the model based on wavelet decomposition conjoined with ANFIS could perform better than the ANN and ANFIS models individually

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