University of Birjand Water Harvesting Research 2476-6976 7 2 2024 09 01 Canopy Temperature Estimation Using Gene Expression Programming Models and Artificial Neural Networks 234 243 3134 10.22077/jwhr.2024.8265.1155 EN Mehri Saeidinia Department of Water Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran. AmirHameh Haghiabi Department of Water Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran. Journal Article 2024 10 11 Canopy temperature<strong> </strong>(T_c)<strong> </strong>is one of the essential for irrigation scheduling. Measuring canopy temperature is expensive and time-consuming. Simple approaches such as soft computing can be a good tool for this purpose because there has been no documented research in this field. In this study, the ANN (MLP with two hidden layers) and GEP models were used to estimate Tc using limited data such as the dry (T_a) and wet bulb (T_W) temperatures, saturation vapor pressure (e_s), actual vapor pressure (e_a), and the vapor-pressure deficit (VPD). Six combinations of input variables were investigated. The perfect model was selected based on statistical indices during the training and testing. Results showed that the performance of the models were influenced by the number of the input variables. The MLP models outperformed GEP models during the training and testing processes. The MLP7 (input variables: e_s and e_a) with MSE of 1.08 ^°C, RMSE of 1.04 ^°C, and R² of 0.92 in the training phase and MSE of 1.02, RMSE of 1.00, and R² of 0.95 in the validation phase was selected as the perfect model among MLP models. The GEP11(input variables: T_a, T_W, e_s, e_a, and VPD) with MSE of 1.32, RMSE of 1.15, and R² of 0.89 in the training phase and MSE of 0.91, RMSE of 0.95, and R² of 0.95 in the validation phase was also the perfect model among GEP models. Accordingly, the proposed GEP and MLP models can be drawn on as a perfect model for estimating T_C. https://jwhr.birjand.ac.ir/article_3134_50053b40f9f031f457d5da2c05db11c4.pdf