A hybrid optimized model for predicting evapotranspiration in early and late rice based on a categorical regression tree combination of key influencing factors
•The ETC model based on key factors is proposed for early and late rice.•Rn shows the highest correlation with early and late rice evapotranspiration.•The SSA-LSSVM model performs best among the optimization models. Crop evapotranspiration (ETC) is an important component of the agricultural hydrolog...
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| Published in: | Computers and electronics in agriculture Vol. 211; p. 108031 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.08.2023
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| ISSN: | 0168-1699, 1872-7107 |
| Online Access: | Get full text |
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| Abstract | •The ETC model based on key factors is proposed for early and late rice.•Rn shows the highest correlation with early and late rice evapotranspiration.•The SSA-LSSVM model performs best among the optimization models.
Crop evapotranspiration (ETC) is an important component of the agricultural hydrological cycle, and its accurate estimation is important for assessing the hydrological environment of farmlands and guiding crop irrigation. To improve the prediction accuracy of ETC for early and late rice, this study analyzed the importance of the influencing factors based on a categorical regression tree (CART) machine learning algorithm. Based on the importance results of the influencing factors obtained from CART analysis, different combinations of input factors were constructed, and daily ETC prediction models were developed for early and late rice using the least squares support vector machine (LSSVM). A three-element heuristic optimization algorithm, Sine Cosine Algorithm (SCA), Slime Mold Algorithm (SMA), and Sparrow Search Algorithm (SSA), were used to optimize the ETC model to improve the performance of the early and late rice ETC prediction models. The results of this study revealed that the strongest correlation between early and late rice ETC was net solar radiation (Rn), with importance of 0.830 and 0.802, respectively. The next strongest correlations were temperature (T), wind speed (u), soil moisture (SM) and u, leaf area index (LAI), SM (0.092, 0.02, 0.014 and 0.058, 0.049, 0.034), respectively. The accuracy of the model tended to increase as the number of influencing factors in the input combinations constructed based on the importance results increased. The accuracy of the model increased more rapidly when the input factors were one to four and the accuracy of the model increased slightly when the input factors were more than four. The SSA-LSSVM model with four-factor combinations of inputs was the least-factor combination with guaranteed model accuracy of RMSE = 0.514 mm/d, R2 = 0.924, KGE = 0.565, WI = 0.978,and MAE = 0.414 mm/d; RMSE = 0.468 mm/d, R2 = 0.910, KGE = 0.670, WI = 0.985, and MAE = 0.368 mm/d. This study may provide a reference for the prediction of evapotranspiration as a key factor in the study of the agricultural hydrological environment of early and late rice. |
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| AbstractList | Crop evapotranspiration (ETC) is an important component of the agricultural hydrological cycle, and its accurate estimation is important for assessing the hydrological environment of farmlands and guiding crop irrigation. To improve the prediction accuracy of ETC for early and late rice, this study analyzed the importance of the influencing factors based on a categorical regression tree (CART) machine learning algorithm. Based on the importance results of the influencing factors obtained from CART analysis, different combinations of input factors were constructed, and daily ETC prediction models were developed for early and late rice using the least squares support vector machine (LSSVM). A three-element heuristic optimization algorithm, Sine Cosine Algorithm (SCA), Slime Mold Algorithm (SMA), and Sparrow Search Algorithm (SSA), were used to optimize the ETC model to improve the performance of the early and late rice ETC prediction models. The results of this study revealed that the strongest correlation between early and late rice ETC was net solar radiation (Rn), with importance of 0.830 and 0.802, respectively. The next strongest correlations were temperature (T), wind speed (u), soil moisture (SM) and u, leaf area index (LAI), SM (0.092, 0.02, 0.014 and 0.058, 0.049, 0.034), respectively. The accuracy of the model tended to increase as the number of influencing factors in the input combinations constructed based on the importance results increased. The accuracy of the model increased more rapidly when the input factors were one to four and the accuracy of the model increased slightly when the input factors were more than four. The SSA-LSSVM model with four-factor combinations of inputs was the least-factor combination with guaranteed model accuracy of RMSE = 0.514 mm/d, R² = 0.924, KGE = 0.565, WI = 0.978,and MAE = 0.414 mm/d; RMSE = 0.468 mm/d, R² = 0.910, KGE = 0.670, WI = 0.985, and MAE = 0.368 mm/d. This study may provide a reference for the prediction of evapotranspiration as a key factor in the study of the agricultural hydrological environment of early and late rice. •The ETC model based on key factors is proposed for early and late rice.•Rn shows the highest correlation with early and late rice evapotranspiration.•The SSA-LSSVM model performs best among the optimization models. Crop evapotranspiration (ETC) is an important component of the agricultural hydrological cycle, and its accurate estimation is important for assessing the hydrological environment of farmlands and guiding crop irrigation. To improve the prediction accuracy of ETC for early and late rice, this study analyzed the importance of the influencing factors based on a categorical regression tree (CART) machine learning algorithm. Based on the importance results of the influencing factors obtained from CART analysis, different combinations of input factors were constructed, and daily ETC prediction models were developed for early and late rice using the least squares support vector machine (LSSVM). A three-element heuristic optimization algorithm, Sine Cosine Algorithm (SCA), Slime Mold Algorithm (SMA), and Sparrow Search Algorithm (SSA), were used to optimize the ETC model to improve the performance of the early and late rice ETC prediction models. The results of this study revealed that the strongest correlation between early and late rice ETC was net solar radiation (Rn), with importance of 0.830 and 0.802, respectively. The next strongest correlations were temperature (T), wind speed (u), soil moisture (SM) and u, leaf area index (LAI), SM (0.092, 0.02, 0.014 and 0.058, 0.049, 0.034), respectively. The accuracy of the model tended to increase as the number of influencing factors in the input combinations constructed based on the importance results increased. The accuracy of the model increased more rapidly when the input factors were one to four and the accuracy of the model increased slightly when the input factors were more than four. The SSA-LSSVM model with four-factor combinations of inputs was the least-factor combination with guaranteed model accuracy of RMSE = 0.514 mm/d, R2 = 0.924, KGE = 0.565, WI = 0.978,and MAE = 0.414 mm/d; RMSE = 0.468 mm/d, R2 = 0.910, KGE = 0.670, WI = 0.985, and MAE = 0.368 mm/d. This study may provide a reference for the prediction of evapotranspiration as a key factor in the study of the agricultural hydrological environment of early and late rice. |
| ArticleNumber | 108031 |
| Author | Shi, Yi Yang, Ru Li, Hui Zhao, Long Bai, Jiayi Qing, Shunhao Hao, Haohao Xing, Xuguang |
| Author_xml | – sequence: 1 givenname: Long surname: Zhao fullname: Zhao, Long organization: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan Province 471000, China – sequence: 2 givenname: Shunhao surname: Qing fullname: Qing, Shunhao organization: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan Province 471000, China – sequence: 3 givenname: Jiayi surname: Bai fullname: Bai, Jiayi organization: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan Province 471000, China – sequence: 4 givenname: Haohao surname: Hao fullname: Hao, Haohao organization: Zhumadian Company, Henan Provincial Tobacco Company, Zhumadian, Henan Province 463000, China – sequence: 5 givenname: Hui surname: Li fullname: Li, Hui organization: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan Province 471000, China – sequence: 6 givenname: Yi surname: Shi fullname: Shi, Yi organization: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan Province 471000, China – sequence: 7 givenname: Xuguang surname: Xing fullname: Xing, Xuguang email: xgxing@nwsuaf.edu.cn organization: Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi Province, China – sequence: 8 givenname: Ru surname: Yang fullname: Yang, Ru organization: College of Information and Electrical Engineering, China Agricultural University, Yantai, Shandong Province 264670, China |
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| Keywords | Least Squares Support Vector Machine Categorical Regression Tree Meta-heuristic optimization algorithms Evapotranspiration Rice |
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| Snippet | •The ETC model based on key factors is proposed for early and late rice.•Rn shows the highest correlation with early and late rice evapotranspiration.•The... Crop evapotranspiration (ETC) is an important component of the agricultural hydrological cycle, and its accurate estimation is important for assessing the... |
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| SubjectTerms | agriculture Categorical Regression Tree electronics Evapotranspiration hybrids hydrologic cycle irrigation leaf area index Least Squares Support Vector Machine Meta-heuristic optimization algorithms prediction regression analysis Rice soil water solar radiation support vector machines temperature wind speed |
| Title | A hybrid optimized model for predicting evapotranspiration in early and late rice based on a categorical regression tree combination of key influencing factors |
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