| This article has been：Browse 372Times Download 856Times |
scan it! |
|
|
| DOI：10.13522/j.cnki.ggps.2024373 |
|
| Hybrid time series and machine learning approach for predicting reference evapotranspiration in North Henan Province |
|
CAO Ruizhe, QIN Anzhen
|
|
1. Xinxiang Hydrology and Water Resources Reporting Subcenter, Xinxiang 453000, China;
2. Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
|
| 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 |
|
|
