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Engineering with Computers
Erschienen in: Engineering with Computers 2/2023

06.11.2021 | Original Article

Developing the artificial neural network–evolutionary algorithms hybrid models (ANN–EA) to predict the daily evaporation from dam reservoirs

verfasst von: Naser Arya Azar, Nazila Kardan, Sami Ghordoyee Milan

Erschienen in: Engineering with Computers | Ausgabe 2/2023

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Abstract

Evaporation is one of the key factors in the hydrological cycle, and it is one of the most critical parameters in hydrological, agricultural, and meteorological studies, especially in arid and semi-arid regions. By estimating evaporation, it is possible to make a significant contribution to studies related to water balance, management and design of irrigation systems, estimation of crop amount, and management of water resources. In this regard, in the present study, using artificial neural network (ANN) and its hybrid algorithms including Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO), the daily evaporation value from the reservoir of Qaleh Chay Ajab Shir dam was estimated. For this purpose, the effective parameters in the evaporation process were introduced to each of the models in the form of different input patterns, and the evaporation value from the reservoir was also considered as output parameter. The results showed that the best selected model for ANN is the P3 model including three parameters of minimum air temperature, and daily evaporation data with NASH of 0.89, RMSE of 1.5 mm/day, and MAE of 1.1 mm/day, which was optimized by applying hybrid algorithms to train the neural network. The results disclosed that all three models had a good performance in estimating the daily evaporation value, so that the value of the correlation coefficient for all three models is in the range of 0.95–0.99. Based on evaluation criteria, ANN–HHO has better performance than the two other algorithms in estimating daily evaporation value. The values of NASH, RMSE and MAE for the selected pattern of the test data are 0.943, 0.908 and 0.736 mm/day, respectively. For better analysis, Taylor diagram is used (RMSD = 0.98, CC = 0.97, STD = 4 for ANN–HHO). The results of this diagram also showed that the ANN–HHO model provides acceptable performance when compared with other models. Considering the promising results of the models in predicting the daily evaporation from dam, it is suggested to use the existing approach for landscaping the groundwater balance and design of irrigation systems.
Vorheriger Artikel Generalized Gegenbauer–Humbert wavelets for solving fractional partial differential equations
Nächster Artikel Drucker-Prager plasticity model in the framework of OSB-PD theory with shear deformation

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Metadaten
Titel
Developing the artificial neural network–evolutionary algorithms hybrid models (ANN–EA) to predict the daily evaporation from dam reservoirs
verfasst von
Naser Arya Azar
Nazila Kardan
Sami Ghordoyee Milan
Publikationsdatum
06.11.2021
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 2/2023
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-021-01523-3

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