基于信息熵的混合模型提高了参考蒸散量预测的准确性

IF 2.1 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Anzhen Qin, Zhilong Fan, Liuzeng Zhang
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引用次数: 0

摘要

准确预测参考作物蒸散量(ET0)对可持续水资源管理至关重要。本研究选择了四种常用的单一模型来预测 ET0 值,包括支持向量回归模型、贝叶斯线性回归模型、脊回归模型和拉索回归模型。它们都具有对数据输入要求低、数据拟合能力强等优点。然而,由于数据噪声或过度拟合等原因,这些预测模型不可避免地存在预测误差。为了提高模型的预测精度,人们提出了混合模型来综合单一模型的优点。在构建混合模型之前,每个单一模型的权重是根据两种权重确定方法确定的,即方差倒数加权法和信息熵加权法。为了验证所提出的混合模型的准确性,以华北平原新乡市 2022 年 1 月 2 日至 2 月 1 日的 1-30 d 预报数据作为测试集。结果证实了基于信息熵的混合模型的可行性。具体而言,信息熵模型产生的平均绝对百分比误差为 11.9%,与单一模型和方差倒数混合模型相比减少了 48.9%。此外,该模型对 1-30 d ET0 预测的相关系数为 0.90,比其他模型增加了 13.6%。信息熵模型的标准偏差和均方根误差分别为 1.65 mm-d-1 和 0.61 mm-d-1,减少了 16.4% 和 23.7%。信息熵模型的最大精度和 F1 分数分别为 0.9618 和 0.9742。结果表明,基于信息熵的混合模型在华北平原具有最佳的中期(1-30 d)ET0预报性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast
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 overfitting. In order to improve the forecast accuracy of models, hybrid models were proposed to integrate the advantages of the single models. Before the construction of hybrid models, each single model’s weight was determined based on two weight determination methods, namely, the variance reciprocal and information entropy weighting methods. To validate the accuracy of the proposed hybrid models, 1–30 d forecast data from January 2 to February 1, 2022, were used as a test set in Xinxiang, North China Plain. The results confirmed the feasibility of the information entropy-based hybrid model. In detail, the information entropy model generated the mean absolute percentage errors of 11.9% or a decrease by 48.9% compared to the single and variance reciprocal hybrid models. Moreover, the model generated a correlation coefficient of 0.90 for 1–30 d ET0 forecasting or an increase by 13.6% compared to other models. The standard deviation and the root mean square error of the information entropy model were 1.65 mm·d−1 and 0.61 mm·d−1 or had a decrease by 16.4% and 23.7%. The maximum precision and the F1 score were 0.9618 and 0.9742 for the information entropy model. It was concluded that the information entropy-based hybrid model had the best midterm (1–30 d) ET0 forecasting performance in the North China Plain.
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来源期刊
Advances in Meteorology
Advances in Meteorology 地学天文-气象与大气科学
CiteScore
5.30
自引率
3.40%
发文量
80
审稿时长
>12 weeks
期刊介绍: Advances in Meteorology is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of meteorology and climatology. Topics covered include, but are not limited to, forecasting techniques and applications, meteorological modeling, data analysis, atmospheric chemistry and physics, climate change, satellite meteorology, marine meteorology, and forest meteorology.
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