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 regr...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2024/9922690 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832545222380224512 |
|---|---|
| author | Anzhen Qin Zhilong Fan Liuzeng Zhang |
| author_facet | Anzhen Qin Zhilong Fan Liuzeng Zhang |
| author_sort | Anzhen Qin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-354f445fd8124b51968e0ffa4f640541 |
| institution | Kabale University |
| issn | 1687-9317 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-354f445fd8124b51968e0ffa4f6405412025-02-03T07:26:21ZengWileyAdvances in Meteorology1687-93172024-01-01202410.1155/2024/9922690Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration ForecastAnzhen Qin0Zhilong Fan1Liuzeng Zhang2State Key Laboratory of Aridland Crop ScienceState Key Laboratory of Aridland Crop ScienceSchool of Computer Science and EngineeringAccurate 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.http://dx.doi.org/10.1155/2024/9922690 |
| spellingShingle | Anzhen Qin Zhilong Fan Liuzeng Zhang Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast Advances in Meteorology |
| title | Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast |
| title_full | Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast |
| title_fullStr | Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast |
| title_full_unstemmed | Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast |
| title_short | Information Entropy-Based Hybrid Models Improve the Accuracy of Reference Evapotranspiration Forecast |
| title_sort | information entropy based hybrid models improve the accuracy of reference evapotranspiration forecast |
| url | http://dx.doi.org/10.1155/2024/9922690 |
| work_keys_str_mv | AT anzhenqin informationentropybasedhybridmodelsimprovetheaccuracyofreferenceevapotranspirationforecast AT zhilongfan informationentropybasedhybridmodelsimprovetheaccuracyofreferenceevapotranspirationforecast AT liuzengzhang informationentropybasedhybridmodelsimprovetheaccuracyofreferenceevapotranspirationforecast |