A comparative study using a data-driven method versus a hybrid approach to estimate daily reference evapotranspiration in Ahvaz

Document Type : Research Paper

Authors

1 PhD student, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran.

2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran.

Abstract

Daily reference evapotranspiration prediction is a decisive and useful tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. The use of hybrid models with the help of climatic factors is an effective method in the daily reference evapotranspiration forecasting process. Therefore, in this study, the ability of the support vector regression model (SVR) and the combined model of support vector regression with the fruit fly algorithm (SVR-FOA) in estimating daily reference evapotranspiration in Ahvaz station during the period of 2000-2022 using four statistical criteria was evaluated. The inputs used included parameters of average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunshine hours. The sensitivity analysis of the input parameters using Pearson's correlation coefficient also showed that among the input parameters, the parameters of sunshine hours and relative humidity were effective components in the prediction of evapotranspiration, thus reducing the error in all models. The obtained results showed that the sixth scenario of the SVR-FOA model provided the best performance with the lowest error (1.24 mm/day) compared to all models. Among the scenarios of the SVR model, the third scenario of the SVR model showed the lowest error (1.45 mm/day) compared to other SVR combinations. The results of this research showed that the sixth scenario of the SVR-FOA model had the best performance, and the fruit fly hybrid algorithm improved the performance of the support vector regression in estimating daily reference evapotranspiration.

Keywords

Main Subjects

References

  • Ahmadi, M.,Sharifi, A.,Dorosti, S.,Ghoushchi, S.J., Ghanbari, N., 2020. Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Science of the total environment, 729, pp. 138705. DOI: 10.1016/j.scitotenv.2020.138705.

 

  • Aljanabi, Q.,Chik, Z.,Allawi, M.F.,El-Shafie, A.H.,Ahmed, A.N., El-Shafie, A., 2018. Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment. Neural Computing and Applications, 30, pp. 2459-2469.

 

  • Allawi, M.F.,Binti Othman, F.,Afan, H.A.,Ahmed, A.N.,Hossain, M.S.,Fai, C.M., El-Shafie, A., 2019. Reservoir evaporation prediction modeling based on artificial intelligence methods. Water, 11(6), pp. 1226. DOI: 10.3390/w11061226.

 

  • Allen, R.G.,Pereira, L.S.,Howell, T.A., Jensen, M.E., 2011. Evapotranspiration information reporting: II. Recommended documentation. Agricultural Water Management, 98(6), pp. 921-929. DOI: 10.1016/j.agwat.2010.12.016.

 

  • Arya Azar, N.,Ghordoyee Milan, S., Kayhomayoon, Z., 2021. Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization algorithm. Environmental Monitoring and Assessment, 193, pp. 1-14.

 

  • Asadifard, E. and Masoudi, M., 2018. Status and prediction of carbon monoxide as an air pollutant in Ahvaz City, Iran. Caspian Journal of Environmental Sciences, 16(3), pp. 203-23. DOI: 10.22124/cjes.2018.3061.

 

  • Baydaroğlu, Ö. and Koçak, K., 2014. SVR-based prediction of evaporation combined with chaotic approach. Journal of Hydrology, 508, pp. 356-363. DOI: 10.1016/j.jhydrol.2013.11.008.

 

  • Chen, J.-L.,Yang, H.,Lv, M.-Q.,Xiao, Z.-L., Wu, S.J., 2019. Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China. Theoretical and Applied Climatology, 138(1), pp. 1095-1107.

 

  • El Bilali, A.,Abdeslam, T.,Ayoub, N.,Lamane, H.,Ezzaouini, M.A., Elbeltagi, A., 2023. An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation. Journal of Environmental Management, 327, pp. 116890. DOI: 10.1016/j.jenvman.2022.116890.

 

  • Eslamian, S. and Eslamian, F., 2022. Handbook of HydroInformatics: Volume I: Classic Soft-Computing Techniques. Elsevier.

 

  • Ghumman, A.R.,Jamaan, M.,Ahmad, A.,Shafiquzzaman, M.,Haider, H.,Al Salamah, I.S., Ghazaw, Y.M., 2021. Simulation of pan-evaporation using penman and hamon equations and artificial intelligence techniques. Water, 13(6), 793. DOI: 10.3390/w13060793.

 

  • Guan, Y.,Mohammadi, B.,Pham, Q.,Adarsh, S.,Balkhair, K.S.,Rahman, K.U.,Linh, N.T.T., Tri, D.Q., 2020. A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model. Theoretical and Applied Climatology, 142, pp. 349-367.

 

  • Huang, G.,Wu, L.,Ma, X.,Zhang, W.,Fan, J.,Yu, X.,Zeng, W., Zhou, H., 2019. Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. Journal of Hydrology, 574, pp. 1029-1041. DOI: 10.1016/j.jhydrol.2019.04.085.

 

  • Jha, S.K. and Hayashi, K., 2014. A novel odor filtering and sensing system combined with regression analysis for chemical vapor quantification. Sensors and Actuators B: Chemical, 200, pp. 269-287. DOI: 10.1016/j.snb.2014.04.022.

 

  • Kisi, O.,Genc, O.,Dinc, S., Zounemat-Kermani, M., 2016. Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree. Computers and Electronics in Agriculture, 122, pp. 112-117. DOI: 10.1016/j.compag.2016.01.026.

 

  • Kumar, P. and Singh, A.K., 2022. A comparison between MLR, MARS, SVR and RF techniques: hydrological time-series modeling. Journal of Human, Earth, and Future, 3(1), pp. 90-98. DOI: 10.28991/HEF-2022-03-01-07.

 

  • Malik, A.,Tikhamarine, Y.,Al-Ansari, N.,Shahid, S.,Sekhon, H.S.,Pal, R.K.,Rai, P.,Pandey, K.,Singh, P., Elbeltagi, A., 2021. Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Engineering Applications of Computational Fluid Mechanics, 15(1), pp. 1075-1094. DOI: 10.1080/19942060.2021.1942990.

 

  • Mashaly, A.F. and Fernald, A.G., 2020. Identifying capabilities and potentials of system dynamics in hydrology and water resources as a promising modeling approach for water management. Water, 12(5), pp. 1432. DOI: 10.3390/w12051432.

 

  • Mirzania, E.,Vishwakarma, D.K.,Bui, Q.-A.T.,Band, S.S., Dehghani, R., 2023. A novel hybrid AIG-SVR model for estimating daily reference evapotranspiration. Arabian Journal of Geosciences, 16(5), pp. 1-14.

 

  • Pan, W.-T., 2012. A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, pp. 69-74. DOI: 10.1016/j.knosys.2011.07.001.

 

  • Poluru, R.K. and Kumar R, L., 2021. An Improved Fruit Fly Optimization (IFFOA) based Cluster Head Selection Algorithm for Internet of Things. International Journal of Computers and Applications, 43(7), pp. 623-631. DOI: 10.1080/1206212X.2019.1600831.

 

  • Ruiming, F. and Shijie, S., 2020. Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network. Agricultural Water Management, 236, pp. 106177. DOI: 10.1016/j.agwat.2020.106177.

 

  • Saltelli, A.,Aleksankina, K.,Becker, W.,Fennell, P.,Ferretti, F.,Holst, N.,Li, S., Wu, Q., 2019. Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental modelling & software, 114, pp. 29-39. DOI: 1016/j.envsoft.2019.01.012.

 

  • Samadianfard, S.,Jarhan, S.,Salwana, E.,Mosavi, A.,Shamshirband, S., Akib, S., 2019. Support vector regression integrated with fruit fly optimization algorithm for river flow forecasting in Lake Urmia Basin. Water, 11(9), pp. 1934. DOI: 3390/w11091934.

 

  • Sattari, M.T.,Feizi, H.,Samadianfard, S.,Falsafian, , Salwana, E., 2021. Estimation of monthly and seasonal precipitation: A comparative study using data-driven methods versus hybrid approach. Measurement, 173, pp. 108512. DOI: 10.1016/j.measurement.2020.108512.

 

  • Shabani, S.,Samadianfard, S.,Sattari, M.T.,Mosavi, A.,Shamshirband, S.,Kmet, T., Várkonyi-Kóczy, A.R., 2020. Modeling pan evaporation using Gaussian process regression K-nearest neighbors random forest and support vector machines; comparative analysis. Atmosphere, 11(1), pp. 66. DOI: 3390/atmos11010066.

 

  • Shan, D.,Cao, G., Dong, H., 2013. LGMS-FOA: an improved fruit fly optimization algorithm for solving optimization problems. Mathematical problems in engineering, 2013, pp. DOI: 1155/2013/108768

 

  • Sun, X.,Bi, Y.,Karami, H.,Naini, S.,Band, S.S., Mosavi, A., 2021a. Hybrid model of support vector regression and fruitfly optimization algorithm for predicting ski-jump spillway scour geometry. Engineering Applications of Computational Fluid Mechanics, 15(1), pp. 272-291. DOI: 1080/19942060.2020.1869102

29- Sun, Z.,Zhu, G.,Zhang, Z.,Xu, Y.,Yong, L.,Wan, Q.,Ma, H.,Sang, L., Liu, Y., 2021b. Identifying surface water evaporation loss of inland river basin based on evaporation enrichment model. Hydrological Processes, 35(3), pp. e14093. DOI: 10.1002/hyp.14093.

 

30- Tikhamarine, Y.,Malik, A.,Pandey, K.,Sammen, S.S.,Souag-Gamane, D.,Heddam, S., Kisi, O., 2020. Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm. Environmental Monitoring and Assessment, 192, pp. 1-19.

 

31- Vapnik, V. and Chervonenkis, A., 1974. Theory of pattern recognition. Nauka, Moscow, pp.

 

32- Wanniarachchi, S. and Sarukkalige, R., 2022. A review on evapotranspiration estimation in agricultural water management: Past, present, and future. Hydrology, 9(7), pp. 123. DOI: 10.3390/hydrology9070123.

33- Wu, J.,Wang, Y.-G.,Burrage, K.,Tian, Y.-C.,Lawson, B., Ding, Z., 2020. An improved firefly algorithm for global continuous optimization problems. Expert Systems with Applications, 149, pp. 113340. DOI: 10.1016/j.eswa.2020.113340.

 

34- Yan, Z.,Wang, S.,Ma, D.,Liu, B.,Lin, H., Li, S., 2019. Meteorological factors affecting pan evaporation in the Haihe River Basin, China. Water, 11(2), pp. 317. DOI: 10.3390/w11020317.

 

35- Yoon, H.,Jun, S.-C.,Hyun, Y.,Bae, G.-O., Lee, K.-K., 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of hydrology, 396(1-2), pp. 128-138. DOI: 10.1016/j.jhydrol.2010.11.002.

Irrigation Sciences and Engineering
Volume 47, Issue 2
September 2024
Pages 53-68

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History

  • Receive Date: 17 April 2023
  • Revise Date: 25 June 2023
  • Accept Date: 28 June 2023
  • Publish Date: 22 August 2024

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