A Coupled Least Absolute Shrinkage and Selection Operator–Backpropagation Model for Estimating Evapotranspiration in Xizang Plateau Irrigation Districts with Reduced Meteorological Variables
This study addresses the challenge of estimating reference crop evapotranspiration (ET<sub>O</sub>) in Xizang Plateau irrigation districts with limited meteorological data by proposing a coupled LASSO-BP model that integrates LASSO regression with a BP neural network. The model was appli...
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MDPI AG
2025-03-01
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| Series: | Agriculture |
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| author | Qiang Meng Jingxia Liu Fengrui Li Peng Chen Junzeng Xu Yawei Li Tangzhe Nie Yu Han |
| author_facet | Qiang Meng Jingxia Liu Fengrui Li Peng Chen Junzeng Xu Yawei Li Tangzhe Nie Yu Han |
| author_sort | Qiang Meng |
| collection | DOAJ |
| description | This study addresses the challenge of estimating reference crop evapotranspiration (ET<sub>O</sub>) in Xizang Plateau irrigation districts with limited meteorological data by proposing a coupled LASSO-BP model that integrates LASSO regression with a BP neural network. The model was applied to three irrigation districts: Moda (MD), Jiangbei (JB), and Manla (ML). Using ET<sub>O</sub> values calculated by the FAO-56 Penman–Monteith (FAO-56PM) model as a benchmark, the performance and applicability of the LASSO-BP model were assessed. Short-term ET<sub>O</sub> predictions for the three districts were also conducted using the mean-generating function optimal subset regression algorithm. The results revealed significant multicollinearity among six meteorological factors (maximum temperature, minimum temperature, average temperature, average relative humidity, sunshine duration, and average wind speed), as identified through tolerance, variance inflation factor (<i>VIF</i>), and eigenvalue analysis. The LASSO-BP model effectively captured the interannual variation of ET<sub>O</sub>, accurately identifying peaks and troughs, with trends closely aligned with the FAO-56PM model. The model demonstrated strong performance across all three districts, with evaluation metrics showing <i>MAE</i>, <i>RMSE</i>, <i>NSE</i>, and <i>R</i><sup>2</sup> values ranging from 4.26 to 9.48 mm·a<sup>−1</sup>, 5.91 to 11.78 mm·a<sup>−1</sup>, 0.92 to 0.96, and 0.82 to 0.94, respectively. Prediction results indicated a statistically insignificant declining trend in annual ET<sub>O</sub> across the three districts over the study period. Overall, the LASSO-BP model is a reliable and accurate tool for estimating ET<sub>O</sub> in Xizang Plateau irrigation districts with limited meteorological data. |
| format | Article |
| id | doaj-art-1d2008103e9a49f28a044a530c3d3fe0 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-1d2008103e9a49f28a044a530c3d3fe02025-08-20T02:53:19ZengMDPI AGAgriculture2077-04722025-03-0115554410.3390/agriculture15050544A Coupled Least Absolute Shrinkage and Selection Operator–Backpropagation Model for Estimating Evapotranspiration in Xizang Plateau Irrigation Districts with Reduced Meteorological VariablesQiang Meng0Jingxia Liu1Fengrui Li2Peng Chen3Junzeng Xu4Yawei Li5Tangzhe Nie6Yu Han7College of Water Conservancy and Civil Engineering, Xizang Agriculture & Animal Husbandry University, Linzhi 860000, ChinaCollege of Water Conservancy and Civil Engineering, Xizang Agriculture & Animal Husbandry University, Linzhi 860000, ChinaXingtai Hydrologic Survey and Research Center of Hebei Province, Xingtai 054000, ChinaCollege of Agricultural Science and Engineering, Hohai University, Nanjing 211100, ChinaCollege of Agricultural Science and Engineering, Hohai University, Nanjing 211100, ChinaCollege of Agricultural Science and Engineering, Hohai University, Nanjing 211100, ChinaSchool of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150080, ChinaSchool of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, ChinaThis study addresses the challenge of estimating reference crop evapotranspiration (ET<sub>O</sub>) in Xizang Plateau irrigation districts with limited meteorological data by proposing a coupled LASSO-BP model that integrates LASSO regression with a BP neural network. The model was applied to three irrigation districts: Moda (MD), Jiangbei (JB), and Manla (ML). Using ET<sub>O</sub> values calculated by the FAO-56 Penman–Monteith (FAO-56PM) model as a benchmark, the performance and applicability of the LASSO-BP model were assessed. Short-term ET<sub>O</sub> predictions for the three districts were also conducted using the mean-generating function optimal subset regression algorithm. The results revealed significant multicollinearity among six meteorological factors (maximum temperature, minimum temperature, average temperature, average relative humidity, sunshine duration, and average wind speed), as identified through tolerance, variance inflation factor (<i>VIF</i>), and eigenvalue analysis. The LASSO-BP model effectively captured the interannual variation of ET<sub>O</sub>, accurately identifying peaks and troughs, with trends closely aligned with the FAO-56PM model. The model demonstrated strong performance across all three districts, with evaluation metrics showing <i>MAE</i>, <i>RMSE</i>, <i>NSE</i>, and <i>R</i><sup>2</sup> values ranging from 4.26 to 9.48 mm·a<sup>−1</sup>, 5.91 to 11.78 mm·a<sup>−1</sup>, 0.92 to 0.96, and 0.82 to 0.94, respectively. Prediction results indicated a statistically insignificant declining trend in annual ET<sub>O</sub> across the three districts over the study period. Overall, the LASSO-BP model is a reliable and accurate tool for estimating ET<sub>O</sub> in Xizang Plateau irrigation districts with limited meteorological data.https://www.mdpi.com/2077-0472/15/5/544Xizang Plateau irrigation districtsreference crop evapotranspiration (ET<sub>O</sub>)BP neural networkLASSO regressionmean-generating function optimal subset regression (MGF-OSR) |
| spellingShingle | Qiang Meng Jingxia Liu Fengrui Li Peng Chen Junzeng Xu Yawei Li Tangzhe Nie Yu Han A Coupled Least Absolute Shrinkage and Selection Operator–Backpropagation Model for Estimating Evapotranspiration in Xizang Plateau Irrigation Districts with Reduced Meteorological Variables Agriculture Xizang Plateau irrigation districts reference crop evapotranspiration (ET<sub>O</sub>) BP neural network LASSO regression mean-generating function optimal subset regression (MGF-OSR) |
| title | A Coupled Least Absolute Shrinkage and Selection Operator–Backpropagation Model for Estimating Evapotranspiration in Xizang Plateau Irrigation Districts with Reduced Meteorological Variables |
| title_full | A Coupled Least Absolute Shrinkage and Selection Operator–Backpropagation Model for Estimating Evapotranspiration in Xizang Plateau Irrigation Districts with Reduced Meteorological Variables |
| title_fullStr | A Coupled Least Absolute Shrinkage and Selection Operator–Backpropagation Model for Estimating Evapotranspiration in Xizang Plateau Irrigation Districts with Reduced Meteorological Variables |
| title_full_unstemmed | A Coupled Least Absolute Shrinkage and Selection Operator–Backpropagation Model for Estimating Evapotranspiration in Xizang Plateau Irrigation Districts with Reduced Meteorological Variables |
| title_short | A Coupled Least Absolute Shrinkage and Selection Operator–Backpropagation Model for Estimating Evapotranspiration in Xizang Plateau Irrigation Districts with Reduced Meteorological Variables |
| title_sort | coupled least absolute shrinkage and selection operator backpropagation model for estimating evapotranspiration in xizang plateau irrigation districts with reduced meteorological variables |
| topic | Xizang Plateau irrigation districts reference crop evapotranspiration (ET<sub>O</sub>) BP neural network LASSO regression mean-generating function optimal subset regression (MGF-OSR) |
| url | https://www.mdpi.com/2077-0472/15/5/544 |
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