Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast
Anzhen Qin,
Zhilong Fan,
Liuzeng Zhang
Abstract:Accurate forecasting of reference crop evapotranspiration (ET0) is vital for sustainable water resource management. In this study, four popularly used single models were selected to forecast ET0 values, including support vector regression, Bayesian linear regression, ridge regression, and lasso regression models, respectively. They all had advantages of low requirement of data input and good capability of data fitting. However, forecast errors inevitably existed in those forecasting models due to data noise or… Show more
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