Hybrid time series and machine learning approach for predicting reference evapotranspiration in North Henan Province

• Published in: Guanʻgai paishui xuebao Vol. 44; no. 8; pp. 45 - 52
• Main Authors: CAO Ruizhe, QIN Anzhen
• Format: Journal Article
• Language: Chinese
• Published: Science Press 01.08.2025
• Subjects: numerical weather prediction; hybrid model; prophet model; autoregressive moving average model
• ISSN: 1672-3317
• DOI: 10.13522/j.cnki.ggps.2024373

【Objective】Accurate estimation of reference crop evapotranspiration (ET0) is essential for determining crop water requirements, improving irrigation efficiency and supporting sustainable water resource management, especially in regions facing water scarcity. The objective of this paper is to identify a reliable and practical model for estimating ET0 in Northern Henan Province.【Method】Daily meteorological data measured from 2021 to 2022 and numerical weather forecasts from 2023 for Xinxiang City, Henan Province, were used to develop and evaluate the following ET0 models: the Prophet model, the autoregressive integrated moving average model (ARIMA), the extreme learning machine (ELM) model, and their hybrid combinations. ET0 calculated using these models were compared with that calculated using the FAO-56 Penman-Monteith method.【Result】ET0 calculated in all models were correlated with maximum temperature, minimum temperature, solar radiation, and wind speed 2 m above the ground surface. They factors were thus s