Development of a coupled wavelet transform and evolutionary Levenberg‐Marquardt neural networks for hydrological process modeling. (28th June 2017)

• Record Type: Journal Article
• Authors: Abbaszadeh, Peyman; Alipour, Atieh; Asadi, Shahrokh
• Is Part Of: Computational intelligence. Volume 34:Number 1(2018)
• Page Start: 175
• Page End: 199
• Publication Date: 2017-06-28
• Subjects: genetic algorithm -- Levenberg‐Marquardt -- neural network -- wavelet transform; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12124
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: This research aims to present a general framework by which the most appropriate wavelet parameters including mother wavelet, vanishing moment, and decomposition level can be chosen for a joint wavelet transform and machine learning model. This study is organized in 2 parts: the first part presents an evolutionary Levenberg‐Marquardt neural network (ELMNN) model as the most effective machine learning configuration, and the second part describes how the wavelet transform can be effectively embedded with the developed ELMNN model. In this research, the rainfall and runoff time series data of 2 distinct watersheds at 2 different time scales (daily and monthly) were used to build the proposed hybrid wavelet transform and ELMNN model. The conclusions of this study showed that the Daubechies wavelet more than other wavelet families is capable to extract the informative features of hydrologic series. The vanishing moment and decomposition level of this mother wavelet should be selected based on the watershed behavior and the time resolution of rainfall and runoff time series, respectively. The verification results for both watersheds at daily and monthly time scales indicated root mean square error, peak value criterion, low value criterion, and Kling‐Gupta efficiency as about 0.017, 0.021, 0.023, and 0.91, respectively.