Application of meta-heuristic algorithms to estimate daily evaporation rate

Document Type : Research Paper

Authors

1 Associate Professor, Department of Civil Engineering, Faculty of Engineering, Islamic Azad University of Khorramabad, Iran

2 PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, Iran

3 Assistant Professor, Department of Civil Engineering, Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran

Abstract

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.

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References

Alempour Rajabi, F., Ahmadi, E., Ragab Ibrahim, O., Al Maghawry, S.M., and  Aghamajidi, R. 2024.  Modeling Monthly Evaporation in Different Climates Using ANN–COOT Hybrid Algorithm. Iranian Journal of Science and Technology.48, 3495–3510.
Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. 1998. Crop evapotranspiration (Guidelines for computing crop water requirements). FAO Irrigation and drainage paper. Food and Agricultural Organization of theUnited Nations. 4(2), 300-355.
Bidabadi, M., Babazadeh, H., Shiri, J., and Saremi, A. 2024. Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data. Applied Water Science. 14(3), 655-672.
Dehghani, R. Torabi Poudeh, H., Younesi, H., and  Shahinejad, B. 2020. Daily Streamflow Prediction Using Support Vector Machine-Artificial Flora (SVM-AF) Hybrid Model. Acta Geophysica. 68(6):51-66.
Dehghani, R., and  Dehghani, F. 2022. Application of Wavelet Neural Network in Estimation of Average Air-temperature. Environmental Resources Research.10(2), 291-300
Dehghani, R., and  Torabi, H. 2021. Dissolved oxygen concentration predictions for running waters with using hybrid machine learning techniques. Modeling Earth Systems and Environment.8(2),455-465.
Dewes, C.F., Rangwala, I., Barsugli, J.J., Hobbins, M.T., and  Kumar, S. 2017. Drought risk assessment under climate change is sensitive to methodological choices for the estimation of evaporative demand. PloS One.12 (3), 844-861.
Feng, Y., Jia, Y., Cui, N., Zhao, L., Li, C., and Gong, D. 2017b. Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China. Agricultural Water Management. 181, 1-9
Hargreaves, G.H., and Samani, Z.A. 1985. Reference crop evapotranspiration from temperature. Applied engineering in agriculture. 1(2), 96-99
Karthika, B.S., and Deka, P.C. 2016.  Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters. International Journal of Intelligent Systems and Applications. 8(1), 25-34.
Kisi, O.,  Karahan, M., and  Sen, Z. 2006. River suspended sediment modeling using fuzzy logic approach. Hydrological Process. 20(2), 4351-4362.
Landeras, G., Ortiz-Barredo, A., and López, J.J. 2008. Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management. 95(5), 553-565
Liu, W., and Sun, F. 2016. Assessing estimates of evaporative demand in climate models using observed pan evaporation over China. Journal of Geophysics. 121 (14), 8329-8349
Nagy, H., Watanabe, K., and Hirano, M. 2002. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulics Engineering. 128(3), 558-559.
Nourani, V., Kisi,  Ö.,  and  Komasi, M.  2011. Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology. 402(2), 41–59.
Nourani, V., Alami,  M. T.,   and Aminfar, M.H. 2009. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence. 22(2) , 466–472.
Sahi, S., Mousavi, S.F., and Hosseini, K.H. 2021. Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region. Journal of Soft Computing in Civil Engineering. 5(3), 75-87
Seifi, A., and Soroush, F. 2020.  Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran. Computers and Electronics in Agriculture. 173(4), 526-542
Wang, D., Safavi, A.A., and Romagnoli, J.A. 2000.  Wavelet-based adaptive robust M-estimator for non-linear system identification. AIChE Journal.46(4), 1607-1615.
Wang, S., Fu, Z.Y., Chen, H.S., Nie, Y.P., and Wang, K.L. 2016. Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN). Theoretical and Applied Climatology. 126(3-4), 493-504.
Yan, X., Zhu, Y., Wu, J., and Chen, H. 2012. An improved firefly algorithm with adaptive strategies. Advanced Science Letters. 16(1), 249–254.
Yang, X.S. 2008. Firefly algorithm, nature-inspired meta-heuristic algorithms. Wiley Online Libr. 20, 79–90.
Zeidalinejad, N., and Dehghani, R. 2023. Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways. Groundwater for Sustainable Development. 20(4), 560-575.
Environmental Resources Research
Volume 13, Issue 1
July 2025
Pages 83-92

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