Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

نویسندگان

چکیده

The aim of this study is to evaluate the ability soft computing models including multilayer perceptron- (MLP-) water wave optimization (MLP-WWO), MLP-particle swarm (MLP-PSO), and MLP-genetic algorithm (MLP-GA), simulate daily monthly reference evapotranspiration (ET) at Aidoghmoush basin (Iran). Principal component analysis (PCA) was used find best input combination lagged ETs. According results, ET values with 1, 2, 3 (days) lags as well those (months) were most effective variables in formation PCs. total variance proportion inputs eigenvalues identify important variables. accuracy assessed based on multiple statistical indices such mean absolute error (MAE), Nash–Sutcliff efficiency (NSE), percent bias (PBIAS). results showed that performance hybrid MLP better than standalone MLP. findings confirmed MLP-WWO could precisely predict ET.

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ژورنال

عنوان ژورنال: Complexity

سال: 2021

ISSN: ['1099-0526', '1076-2787']

DOI: https://doi.org/10.1155/2021/6683759