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Babaali, Hamidreza, Dehghani, Reza, Nohani, Ebrahim. (1404). Application of meta-heuristic algorithms to estimate daily evaporation rate. , 13(1), 83-92. doi: 10.22069/ijerr.2025.22953.1456
Hamidreza Babaali; Reza Dehghani; Ebrahim Nohani. "Application of meta-heuristic algorithms to estimate daily evaporation rate". , 13, 1, 1404, 83-92. doi: 10.22069/ijerr.2025.22953.1456
Babaali, Hamidreza, Dehghani, Reza, Nohani, Ebrahim. (1404). 'Application of meta-heuristic algorithms to estimate daily evaporation rate', , 13(1), pp. 83-92. doi: 10.22069/ijerr.2025.22953.1456
Babaali, Hamidreza, Dehghani, Reza, Nohani, Ebrahim. Application of meta-heuristic algorithms to estimate daily evaporation rate. , 1404; 13(1): 83-92. doi: 10.22069/ijerr.2025.22953.1456

Application of meta-heuristic algorithms to estimate daily evaporation rate

Environmental Resources Research
مقاله 6، دوره 13، شماره 1، مهر 2025، صفحه 83-92    اصل مقاله (1.49 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22069/ijerr.2025.22953.1456
نویسندگان
Hamidreza Babaali1؛ Reza Dehghani* 2؛ Ebrahim Nohani3
1Associate Professor of Civil Engineering
2PhD in water science and engineering
3Assistant Professor, Department of Civil Engineering, Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran.
چکیده
Accurate estimation of daily evaporation is very important in the sustainable management of water resources. Therefore, the purpose of this study is to investigate the application of the artificial neural network model with the meta-heuristic algorithm of wavelet and firefly to estimate daily ET0. To achieve this goal, two combined W-ANN and FA-ANN models were investigated for daily estimation of ET0 in two Mediterranean climates in the west of Iran as a case study. Daily climatic parameters including maximum and minimum temperature (T max and T min), sunshine duration (n), relative humidity (RH), wind speed (U), and evaporation ET0 were collected from two weather stations from 2012-2022 and during Four combined scenarios were investigated. which were used from 2012 to 2019 for model training and from 2022 to 2019 for model testing. To compare and evaluate the models, statistical indicators of the correlation coefficient, root mean square error, mean absolute value of error, normalized root mean square error, and Nash Sutcliffe coefficient were used. The results showed that all the investigated models have better performance in combined input scenarios. The results of the evaluation criteria showed that the W-ANN hybrid model has the highest daily estimation accuracy.
کلیدواژه‌ها
Evaporation؛ Artificial Neural Network؛ Firefly؛ Wavelet
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