| This article has been：Browse 337Times Download 678Times |
scan it! |
|
|
| DOI：10.13522/j.cnki.ggps.2024409 |
|
| Machine learning-based methods for estimating evapotranspiration of winter wheat field in the regions of Nanjing |
|
LU Lifang, LIU Chunwei, ZHANG Baozhong, WANG Ranghui, ZHANG Pei, QIU Rangjian
|
|
1. Jiangsu Key Laboratory of Agricultural and Ecological Meteorology, Nanjing University of Information Science and Technology; Open Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing 210044, China;
2. State Key Laboratory of Basin Water Cycle Simulation and Regulation, China Institute of Water Resources and
Hydropower Research, Beijing 100038, China; 3. Jiangsu Climate Center, Nanjing 210008, China;
4. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
|
| Abstract: |
| 【Objective】Accurate estimation of crop evapotranspiration (ETc) is essential for improving irrigation and water resource management. This study aimed to reduce the uncertainty in ETc estimation for winter wheat in the region of Nanjing and improve the efficiency of agricultural water use. 【Method】 Four machine learning models: the Lasso Regression, the Adaptive Boosting (AdaBoost), the Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) were used to calculate ETc using meteorological data measured from 2011 to 2018. Bayesian optimization (BO) was used to fine-tune the model parameters. The calculated results were compared with data measured from lysimeters. Shapley Additive Explanations (SHAP) analysis was used to identify key meteorological factors. A logistic growth model for leaf area index (LAI) based on degree-day and effective accumulated temperature was developed to indirectly estimate ETc. 【Result】 ① The GBDT and RF models outperformed other model, with their R2 values being 0.951 and 0.926, respectively. The GBDT model was most accurate, with a MAE of 0.370 mm/d and RMSE of 0.541 mm/d. ② SHAP analysis showed that LAI, solar radiation (Rs) and net radiation (Rn) were the most influential factors in ETc estimation. ③The logistic-based LAI model enables indirect prediction of ETc using temperature-driven metrics.【Conclusion】The integration of machine learning with Bayesian optimization significantly improves ETc estimation for winter wheat. The GBDT and RF models offer robust alternatives to traditional methods, reducing dependence on costly instrumentation and dense meteorological networks. This scalable method supports data-driven irrigation scheduling and improves sustainable agricultural water management in winter wheat production in the study region. |
| Key words: winter wheat; evaporation; model machine learning; explainability; Bayesian optimization algorithm |
|
|
