Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam

• Published in: Water resources management 2014-08, Vol.28 (10), p.2999-3018
• Main Authors: Akrami, Seyed Ahmad, Nourani, Vahid, Hakim, S. J. S
• Format: Article
• Language: English
• Subjects: Adaptive systems; algorithms; Artificial neural networks; Atmospheric Sciences; Civil Engineering; correlation; Correlation coefficient; Decomposition; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Environment; Errors; Exact sciences and technology; Forecasting; Fourier transforms; fuzzy logic; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology; Hydrology. Hydrogeology; Hydrology/Water Resources; Learning theory; Mathematical models; Methods; monitoring; Neural networks; Precipitation; prediction; Propagation; Rain; Rainfall; Rivers; runoff; stochastic processes; Stream flow; Structural engineering; Studies; Time series; time series analysis; Water management; Water resources; Water resources management; wavelet; Wavelet transforms; Weather forecasting
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-014-0651-x

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 neuro-fuzzy inference system (ANFIS). Nevertheless, the data arising from the monitoring stations and experiment might be corrupted by noise signals owing to systematic and non-systematic 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 (t-4) into ANN and ANFIS, second scenario uses the wavelet transform to eliminate the error and prepares sub-series 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.