Inclusive multiple model using hybrid artificial neural networks for predicting evaporation

Ehteram, Mohammad and Panahi, Fatemeh and Ahmed, Ali Najah and Mosavi, Amir H. and El-Shafie, Ahmed (2022) Inclusive multiple model using hybrid artificial neural networks for predicting evaporation. Frontiers In Environmental Science, 9. ISSN 2296-665X, DOI

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Predicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algorithm (SCA), and genetic algorithm (GA) for robust training to predict daily evaporation of seven synoptic stations with different climates. The inclusive multiple model (IMM) is then used to predict evaporation based on established hybrid ANN models. The adjusting model parameters of the current study is a major challenge. Also, another challenge is the selection of the best inputs to the models. The IMM model had significantly improved the root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) values of all the proposed models. The results for all stations indicated that the IMM model and ANN-CSA could outperform other models. The RMSE of the IMM was 18, 21, 22, 30, and 43% lower than those of the ANN-CSA, ANN-SCA, ANN-FFA, ANN-GA, and ANN models in the Sharekord station. The MAE of the IMM was 0.112 mm/day, while it was 0.189 mm/day, 0.267 mm/day, 0.267 mm/day, 0.389 mm/day, 0.456 mm/day, and 0.512 mm/day for the ANN-CSA, ANN-SCA, and ANN-FFA, ANN-GA, and ANN models, respectively, in the Tehran station. The current study proved that the inclusive multiple models based on improved ANN models considering the fuzzy reasoning had the high ability to predict evaporation.

Item Type: Article
Funders: None
Uncontrolled Keywords: Artificial neural network; Machine learning; Evaporation; Capuchin search algorithm; Inclusive multiple models; Artificial intelligence
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 02 Aug 2022 04:27
Last Modified: 02 Aug 2022 04:27

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