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Canopy Temperature Estimation Using Gene Expression Programming Models and Artificial Neural Networks | ||
| Water Harvesting Research | ||
| دوره 7، شماره 2، 2024، صفحه 234-243 اصل مقاله (730.11 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2024.8265.1155 | ||
| نویسندگان | ||
| Mehri Saeidinia* ؛ AmirHameh Haghiabi | ||
| Department of Water Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran. | ||
| چکیده | ||
| Canopy temperature (Tc) 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 (Ta) and wet bulb (TW) temperatures, saturation vapor pressure (es), actual vapor pressure (ea), 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: es and ea) 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: Ta, TW, es, ea, 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 TC. | ||
| کلیدواژهها | ||
| ANN؛ Climate data؛ GEP؛ Irrigation scheduling؛ MLP | ||
| مراجع | ||
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Adisa, O. M., Botai, J. O., Adeola, A. M., Hassen, A., Botai, C. M., Darkey, D. & Tesfamariam, E. (2019). Application of artificial neural network for predicting maize production in south africa. Sustainability, 11, 1145.
Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. (1998). Crop evapotranspiration-guidelines for computing crop water requirements-fao irrigation and drainage paper 56. Fao, rome, 300, d05109.
Antonopoulos, V. Z. & Antonopoulos, A. V. (2017). Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Computers and electronics in agriculture, 132, 86-96.
Blonder, B. & Michaletz, S. T. (2018). A model for leaf temperature decoupling from air temperature. Agricultural and forest meteorology, 262, 354-360.
Chakrabarti, G., Grover, V., Aarts, B., Kong, X., Kudlur, M., Lin, Y., ... & Wang, J. Z. (2012). CUDA: Compiling and optimizing for a GPU platform. Procedia Computer Science, 9, 1910-1919.
Elbeltagi, A., Zhang, l., Deng, J., Juma, A. & Wang, K. (2020). Modeling monthly crop coefficients of maize based on limited meteorological data: a case study in nile delta, egypt. Computers and electronics in agriculture, 173, 105368.
Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027.
Gavahi, K., Abbaszadeh, P. & Moradkhani, H. (2021). Deepyield: a combined convolutional neural network with long short-term memory for crop yield forecasting. Expert systems with applications, 184, 115511.
Han, X., Wei, Z., Zhang, B., Li, Y., Du, T. & Chen, H. (2021). Crop evapotranspiration prediction by considering dynamic change of crop coefficient and the precipitation effect in back-propagation neural network model. Journal of hydrology, 596, 126104.
Heramb, P., Singh, P. K., Rao, K. R., & Subeesh, A. (2023). Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar, India. Information processing in agriculture, 10(4), 547-563.
Khalilov, D. A., Jumaboyeva, N. A. K., & Kurbonova, T. M. K. (2021). Advantages and Applications of Neural Networks. Academic research in educational sciences, 2(2), 1153-1159.
Kontoni, D. P. N., Onyelowe, K. C., Ebid, A. M., Jahangir, H., Rezazadeh Eidgahee, D., Soleymani, A., & Ikpa, C. (2022). Gene expression programming (GEP) modelling of sustainable building materials including mineral admixtures for novel solutions. Mining, 2(4), 629-653.
Küçükönder, H., Boyaci, S. & Akyüz, A. (2016). A modeling study with an artificial neural network: developing estimationmodels for the tomato plant leaf area. Turkish journal of agriculture and forestry, 40, 203-212.
Mahanti, N. K., Upendar, K. & Chakraborty, S. K. (2022). Comparison of artificial neural network and linear regression model for the leaf morphology of fenugreek (trigonella foenum graecum) grown under different nitrogen fertilizer doses. Smart agricultural technology, 2, 100058.
Maier, H. R., Jain, A., Dandy, G. C. & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental modelling & software, 25, 891-909.
Mattar, M. A. (2018). Using gene expression programming in monthly reference evapotranspiration modeling: a case study in egypt. Agricultural water management, 198, 28-38.
Monteiro, A. L., de Freitas Souza, M., Lins, H. A., da Silva Teófilo, T. M., Júnior, A. P. B., Silva, D. V., & Mendonça, V. (2021). A new alternative to determine weed control in agricultural systems based on artificial neural networks (ANNs). Field Crops Research, 263, 108075.
Mostafa, A. B., Thamer, A. M., Abdul, H. G. & Mohd, A. M. S. (2012). Prediction of evaporation in tropical climate using artificial neural network and climate based models. Scientific research and essays, 7, 3133-3148.
O'shaughnessy, S., Evett, S., Colaizzi, P. & Howell, T. (2011). Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agricultural water management, 98, 1523-1535.
Sammen, S. S. (2013). Forecasting of evaporation from hemren reservoir by using artificial neural network. Diyala journal of engineering sciences, 6, 38-33.
Sánchez-Piñero, M., Martín-Palomo, M., Andreu, L., Moriana, A. & Corell, M. (2022). Evaluation of a simplified methodology to estimate the cwsi in olive orchards. Agricultural water management, 269, 107729.
Seifi, A., Ehteram, M., Nayebloei, F., Soroush, F., Gharabaghi, B. & Torabi Haghighi, A. (2021). Glue uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables. Soft computing, 25, 10723-10748.
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, 101-114.
Taherei Ghazvinei, P., Hassanpour Darvishi, H., Mosavi, A., Yusof, K. B. W., Alizamir, M., Shamshirband, S. & Chau, K.W.( 2018). Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Engineering applications of computational fluid mechanics, 12, 738-749.
Valipour, M., Gholami Sefidkouhi, M. A., Raeini-Sarjaz, M. & Guzman, S. M. (2019). A hybrid data-driven machine learning technique for evapotranspiration modeling in various climates. Atmosphere, 10, 311.
Van Klompenburg, T., Kassahun, A. & Catal, C. (2020). Crop yield prediction using machine learning: a systematic literature review. Computers and electronics in agriculture, 177, 105709.
Walczak, S. (2019). Artificial neural networks. In Advanced methodologies and technologies in artificial intelligence, computer simulation, and human-computer interaction (pp. 40-53). IGI global.
Zeynali, M. J., & Hashemi, S. R. (2016). Compare Learning Function in Neural Networks for River Runoff Modeling. Iranian journal of Ecohydrology, 3(4), 659-667. doi: 10.22059/ije.2016.60374.
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