Application of Multivariate Regression and Gene Expression Programming in Modeling Reference Evapotranspiration (Case Study: Khorramabad Station)

Document Type : Research Paper

Authors

1 MSc student, Department of Water Engineering, Faculty of Agriculture and Natural Resources, Lorestan ‎University.‎

2 Assistant Professor, Department of Water Engineering, Faculty of Agriculture and Natural Resources, ‎‎Lorestan University.(

3 Assistant Professor, Department of Water Engineering, Faculty of Agriculture and Natural Resources, ‎‎Lorestan University.‎‏

4 Assistant Professor of Water Engineering ,University of Lorestan, Khorramabad, Iran

Abstract

Accurate estimation of water requirements of plants is a key factor in controlling several hydrological processes including: planning and management of water resources, especially in arid and semi-arid regions (Laaboudi et al., 2012; Wen et al., 2015) water pricing and water requirement for Irrigation (Yassin et al., 2016). In this study, multivariate regression methods and gene expression planning were evaluated to estimate reference evapotranspiration. For model input data, the Khorramabad Synoptic Station information including: maximum and minimum temperatures, maximum and minimum relative humidity, sunny hours and monthly wind speeds in the range of 1983-2017(420 months) were used. Based on the relationship between input and output parameters, six input patterns were determined for modeling.70% of the data were used for training and 30% were used for model validation.The results of multivariate regression showed that the proposed model had acceptable accuracy with R2 = 0.952.The analysis of model coefficients showed the greatest effect of maximum temperature with a coefficient of 0.604 on reference evapotranspiration. Gene expression planning results showed that the fifth pattern with four main operators was R2 = 0.958 and RMSE = 0.704 in the training phase and R2=0.977 and RMSE = 0.615 in the test phase had better performance.

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Main Subjects


  • Ahmadi, F., Aisham, S., Khalili, K. And Bahman, c., 2015. Application of adaptive neuro-fuzzy inference system and genetic programming to estimate monthly transpiration evaporation northwest of iran. Journal of Water Research in Agriculture, 29 (2), pp. 235-247. (In Persian).

 

  • 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 56. FAO, Rome, 300(9), pp. 300.‎

 

  • Almorox, J., Quej, V.H. and Marti, P., 2015. Global performance ranking of temperature-based approaches for evapotranspiration estimation considering Koppen climate classes. Journal of Hydrology, 528, pp. 514-522.

 

  • Ferreira C., 2001. Gene expression programming: a new adaptive algorithm for solving problems, Complex Systems, 13 (2), pp. 87-129.

 

  • Feyzollahpour, F., Delaware, M. and Hesami Afshar, M., 2017. Evaluation and analysis of uncertainty estimation of reference evapotranspiration using reference genetics. Journal of Soil and Water Science, 27 (4), pp. 135-147. (In Persian).

 

  • Gholami, V., Derakhshan, Sh. and Darwari, Z., 2012. Investigation of multivariate regression and artificial neural network in simulation of groundwater salinity in mazandaran province. Journal of Water Research in Agriculture, 26 (3), pp. 79-100. (In Persian).

 

  • Hargreaves, G. H., 1994. Defining and using reference evapotranspiration. Irrigation and Drainage ‎Engineering, 120(6), pp. 1132-1139.

 

  • Hosseini, S., Ganji Khoramdel, N. and Khalat Abadi Farahani, A.H., 2015. Evaluation and sensitivity analysis of different methods of estimating daily reference evapotranspiration in a cold climate. Journal of Applied Research in Water Sciences, 1 (2), pp. 29-40. (In Persian).

 

  • Kisi, O. and Alizamir, M., 2018. Modelling reference evapotranspiration using a new wavelet ‎conjunction heuristic method: Wavelet extreme learning machine vs wavelet neural networks. ‎Agricultural and Forest Meteorology, 263, pp. 41–48.‎

 

  • Laaboudi, A., Mouhouche, B. and Draoui, B., 2012. Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. International Journal of Biometeorology, 56(5), pp. 831-841.
  • Ladlani, I., Hauichi, L., Dhemili, L., Heddem, S. and Blouze, K.H., 2012. Estimation of daily refrence evapotranspiration in the north of Algeria using adaptive neuro-Fuzzy inference system (ANFIS) and multiple linear regression models: a comparative study. Arabian Journal for Science and Engineering, 39, pp. 5959-5969.

 

  • Lopes, H.S. and Weinert, W.R., 2004. EGYPSIS: An enhanced gene expression programming approach for symbolic regression problems. Journal of Applied Mathematics and Computer Science, 14 (3), pp. 375-384.

 

  • Marti, P., Gonzalez-Altozano, P., Lopez-Urrea, R., Mancha, L.A. and Shiri, J., 2015. Modeling reference evapotranspiration with calculated targets, assessment and implications. Agricultural Water Management, 149, pp. 81-90.

 

  • Mattar, M. A., 2018. Using gene expression programming in monthly reference evapotranspiration modeling: a case study in Egypt. Agricultural Water Management, 198, pp. 28-38.

 

  • MohammadRezapour, A., Amini, A. and Karandish, F., 2015. Modeling monthly potential evapotranspiration using genetic programming in sistan baluchestan province. Journal of Soil and Water Conservation Research. 22 (5), pp. 307-313. (In Persian).

 

  • Pour-Ali Baba, A., Shiri, J., Kisi, O., Fard, A.F., Kim, S. and Amini, R., 2013. Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 44(1), pp. 131-146.

 

  • Sattari, M.H. and Esmailzadeh, B., 2016. Comparison of the results of M5 tree model and genetic programming with FAO-Penman-Monteith method for reference evapotranspiration reference. Journal of Water Resources Engineering, 9, pp. 11-20.

 

  • Sarmadian, F., Mehrjerdi, R., Asgari, H. and Akbarzadeh, A., 2010. Comparison of neuro-fuzzy neural network and multivariate regression in predicting some soil properties, Journal of Watershed Research, 41 (1), pp. 211-220. (In Persian).

 

  • Seifi, A., Mir Latifi, S. M. and Riahi, H., 2010. Development of multiple regression-principal component and factor analysis (mlr-pca) hybrid model in predicting reference evapotranspiration (Case Study: Kerman Station). Watershed Magazine, 24 (6), pp. 1186-1196. (In Persian).

 

  • Shiri, J., 2017. Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran. Agricultural Water Management, 188, pp. 101-114.

 

  • Shiri, J., Nazemi, A.H., Sadraddini, A.A., Landeras, G., Kisi, O., Fard, A.F. and Marti, P., 2014. Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Computers and Electronics in Agriculture, 108, pp. 230-241.

 

  • Tabari, H., Kisi, O., Ezani, A. and Talaee, P.H., 2012. SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology, 444, pp. 78-89.

 

  • Wen, X., Si, J., He, Z., Wu, J., Shao, H. and Yu, H., 2015. Support-vector-machine-based models for modeling daily reference evapotranspiration with limited climatic data in extreme arid regions. Water Resources Management, 29(9), pp. 3195-3209.

 

  • Yassin, M.A., Alazba, A.A. and Mattar, M.A., 2016. Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agricultural Water Management, 163, pp. 110-124.
Volume 45, Issue 1
May 2022
Pages 35-48
  • Receive Date: 11 November 2019
  • Revise Date: 31 July 2020
  • Accept Date: 02 August 2020
  • Publish Date: 21 April 2022