Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling

Erdem Küçüktopcu (), Emirhan Cemek, Bilal Cemek and Halis Simsek
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Erdem Küçüktopcu: Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, 55139 Samsun, Türkiye
Emirhan Cemek: Hydraulics and Water Resources Engineering Program, Department of Civil Engineering, Istanbul Technical University, 34469 Istanbul, Türkiye
Bilal Cemek: Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, 55139 Samsun, Türkiye
Halis Simsek: Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA

Sustainability, 2023, vol. 15, issue 7, 1-15

Abstract: Machine learning (ML) models, including artificial neural networks (ANN), generalized neural regression networks (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when solving linear problems. To overcome this limitation, this paper proposes hybridizations of ML and autoregressive integrated moving average (ARIMA) models to provide a more accurate and general forecasting model for evapotranspiration (ET 0 ). The proposed models are developed and tested using daily ET 0 data collected over 11 years (2010–2020) in the Samsun province of Türkiye. The results show that the ARIMA–GRNN model reduces the root mean square error by 48.38%, the ARIMA–ANFIS model by 8.56%, and the ARIMA–ANN model by 6.74% compared to the traditional ARIMA model. Consequently, the integration of ML with ARIMA models can offer more accurate and dependable prediction of daily ET 0 , which can be beneficial for many branches such as agriculture and water management that require dependable ET 0 estimations.

Keywords: Box–Jenkins; time series modeling; evapotranspiration; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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