Development and comparison of artificial intelligence models for estimating daily reference evapotranspiration from limited input variables

• Published in: Smart agricultural technology Vol. 3; p. 100115
• Main Authors: Makwana, Jaydip J., Tiwari, Mukesh K., Deora, B.S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2023; Elsevier
• Subjects: Artificial neural network; Extreme learning machine; Limited weather data; M5 tree; Reference evapotranspiration; Semi arid region; Reference evapotranspiration; Semi arid region; Artificial neural network; Extreme learning machine; M5 tree; Limited weather data
• ISSN: 2772-3755; 2772-3755
• DOI: 10.1016/j.atech.2022.100115

The accurate and efficient computational modelling framework for computation of reference evapotranspiration (ET0) is the need of time and is highly essential for several agricultural, hydrological and hydro-climatological studies and applications. ET0 has its own role especially for the water resource system management including irrigation water allocation and management, utilization and demand assessments of water resources. Considering the potential and wide applicability of Artificial Intelligence (AI) models in input-output mapping, these were examined in this research for modelling ET0 utilising a variety of meteorological data, including maximum temperature (Tmax) and minimum temperature (Tmin), relative humidity (RH), wind speed (WS), and bright sunshine hours (BSS). The different AI models investigated in this study include Artificial Neural Network (ANN), Extreme Learning Machine (ELM), M5 Tree and Multiple Linear Regression (MLR). The implemented daily dataset was collected from the Sardarkrushinagar station which is located at the North Gujarat region of India. For performance verification of the developed AI models, the FAO-56 Penman-Monteith equation was employed as a benchmark. The developed model's accuracy was assessed using multiple quantitative metrics, including R2, NSE, RMSE, Pdv and MAE. The performance of ANN models was found better compared to the ELM, M5 Tree, and MLR models in terms of different performance indicators. The model's outcomes were highly practical and reliable for the investigated case studies. Another important finding in this research was that the selection of the appropriate input variables in models allows not only error minimization but also enhances the co-relationship amongst the dependant as well as independent variables.