| 摘要: |
| 【目的】提高豫北地区参考作物蒸散量（ET0）预测模型的精度。【方法】利用2021—2022年新乡市历史气象数据和2023年天气预报数据（最高气温、最低气温、太阳辐射量、2 m风速和日照时间）建立Prophet模型、差分自回归移动平均模型（ARIMA）、极限学习机（ELM）及混合ET0预测模型，并与FAO-56 Penman-Monteith模型的结果进行比较。【结果】最高气温（Tmax）、最低气温（Tmin）、辐射量（Ra）和2 m风速（U2）与ET0的相关性较高，可作为模型的输入因子。Prophet模型和ARIMA模型的预测值与实际ET0的周期变化基本一致，能够较好地预测ET0的季节性和周期性变化。ET0≥5.5 mm/d时，模型拟合结果存在明显误差，难以识别ET0时间序列中的非平稳性与波动性。极限学习机（ELM）模型能够较好地拟合ET0的复杂非线性变化，精度较时间序列模型提高11%。时间序列模型与机器学习混合模型能够较好地吸收不同模型的优点，表现出较高的预测精度。其中，ELM-ARIMA混合模型在中期（1~10 d）ET0预报中的表现最好，平均绝对误差（MAE）、均方根误差（RMSE）和平均偏差误差（MBE）较单一模型降低64.5%、72.9%和65.6%；相关系数（R）较单一模型提高12.9%。【结论】ELM-ARIMA混合模型的ET0预测值与真实值的相关性最高，R2达到0.945，可作为豫北地区ET0预测模型。 |
| 关键词: 数值天气预报；混合模型；Prophet模型；自回归移动平均模型 |
| DOI：10.13522/j.cnki.ggps.2024373 |
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| Hybrid time series and machine learning approach for predicting reference evapotranspiration in North Henan Province |
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CAO Ruizhe, QIN Anzhen
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1. Xinxiang Hydrology and Water Resources Reporting Subcenter, Xinxiang 453000, China;
2. Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
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| Abstract: |
| 【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 selected as inputs to the models. The time-series models (Prophet and ARIMA) effectively captured seasonal variation in ET0 but gave rise to notable errors when ET0 exceeded 5.5 mm/d. The ELM model better captured the nonlinear relationship between ET0 and these meteorological factors, achieving an increase of R2 value by 11%, compared with the time-series models. The ELM-ARIMA hybrid model was more accurate than other models for calculating ET0 in medium-term (1-10 day), with its MAE, RMSE and MBE reduced by 64.5%, 72.9% and 65.6%, respectively, compared to those in the non-hybrid model; its correlation with observed ET0 was R2=0.945, the highest among all models.【Conclusion】The ELM-ARIMA hybrid model is most accurate and reliable for calculating ET0 and is recommended for use in water resource management and agricultural planning in Northern Henan Province. |
| Key words: numerical weather prediction; hybrid model; Prophet model; autoregressive moving average model |
