| 引用本文: | 金秋湘,王 伟,邰 久,等.基于多种机器学习算法的淮河流域稻田蒸散模拟研究[J].灌溉排水学报,2025,44(10):120-132. |
| JIN Qiuxiang,WANG Wei,TAI Jiu,et al.基于多种机器学习算法的淮河流域稻田蒸散模拟研究[J].灌溉排水学报,2025,44(10):120-132. |
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| 摘要: |
| 【目的】淮河流域是我国重要的水稻产区，准确、简便且高效地模拟稻田蒸散（ETa）对该地农业水资源管理至关重要。【方法】基于2019年安徽省寿县国家气候观象台稻田的涡度相关通量、小气候和土壤要素观测数据，分析了稻田蒸散的时间变化特征及其影响因子，评估了3种常用机器学习算法：BP神经网络（BPNN）、随机森林（RF）和支持向量回归（SVR）对稻田蒸散的模拟效果，并与FAO推荐的单作物系数法（FAO56 PM）和最新的自动机器学习框架（FLAML）模拟结果进行对比。【结果】①ETa随生育期进程推进先增后降，最大值和最小值分别出现在孕穗—抽穗期和出苗—三叶期，入射短波辐射（K↓）与ETa的相关性最高且相对重要性最大。②加入K↓能显著提升机器学习算法对ETa的模拟效果，模拟值与观测值之间的决定系数（R2）提高了51.2%，RMSE降低了37.7%。含K↓、相对湿度（RH）和气温（Ta）的RF3是模拟ETa最适的变量组合模型。③相较于单作物系数法，RF3的输入变量更少，但对ETa的模拟精度更高，2019年和2020年的R2分别提高了6.5%、8.9%，RMSE分别降低了58.2%、48.9%。④含有相同输入变量的FLAML3模拟效果略优于RF3，R2提高了1.52%，RMSE降低了5.6%，分蘖期之前的模拟效果提升更明显。【结论】由于FLAML3模型模拟效果最佳，且可以自动地选择算法和设置超参数，推荐用于模拟淮河流域稻田蒸散。 |
| 关键词: 稻田；蒸散；机器学习算法；单作物系数法；全自动机器学习框架 |
| DOI：10.13522/j.cnki.ggps.2024407 |
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| Comparative analysis of different machine learning algorithms for calculating paddy field evapotranspiration in the Huai River Basin |
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JIN Qiuxiang, WANG Wei, TAI Jiu, ZHANG Yiting, DUAN Chunfeng, ZHANG Kaidi,
XU Min, LYU Heng, ZHU Zihan, WANG Bo, WEN Zhiwen
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1. Yale-NUIST Center on Atmospheric Environment, Key Laboratory of Ecosystem Carbon Source and Sink-China Meteorological Administration/Jiangsu Provincial University Key Laboratory of Agricultural and Ecological Meteorology/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology,
Nanjing 210044, China; 2. Anhui Institute of Meteorological Sciences, Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Hefei 230031, China; 3. Shouxian National Climatology Observatory/Huai River Basin Typical Farm Eco-meteorological Experiment Field of China Meteorological Administration/Shouxian National Special Test Feild for
Comprehensive Meteorological Observation of China Meteorological Administration, Shouxian 232200, China;
4. Climate Center of Jiangsu Province, Nanjing 210008, China
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| Abstract: |
| 【Objective】The Huai River Basin is one of China’s major rice-producing regions, where accurate and efficient estimation of paddy field evapotranspiration (ETa) is essential for sustainable agricultural water management. This study aimed to evaluate the performance of different machine learning (ML) algorithms in calculating ETa, and compare them with conventional and automated approaches.【Method】Using eddy covariance flux, microclimate, and soil observations from the Shouxian National Climate Observatory (Anhui Province, 2019), we first analyzed temporal ETa variations and their driving factors. We then estimated the ETa using three common machine learning algorithms: BP neural networks (BPNN), random forests (RF), and support vector regression (SVR). Results calculated by these methods were compared with in-situ observations. Furthermore, results calculated by the most accurate algorithm were compared with those calculated using the FAO-recommended single crop coefficient model (FAO56 PM) and the automated machine learning framework (FLAML). 【Result】As the crop grew, its ETa increased first and then decreased, with the maximum and minimum values observed in the panicle initiation-heading stage and the seedling-three leaf stage, respectively. Incoming shortwave radiation (K↓) showed the closest correlation with ETa and was the most important factor influencing ETa. Incorporating K↓ in the model could improve the accuracy of all three machine learning algorithms, with the associated R2 increasing by 51.2% and RMSE reduced by 37.7%. The RF3, which considers K↓, relative humidity (RH) and air temperature (Ta), was the optimal model for calculating ETa. Although using fewer input variables than the FAO56 PM, RF3 was more accurate in calculating ETa, increasing R2 by 6.5% in 2019 and 8.9% in 2020, while reducing RMSE by 58.2% in 2019 and 48.9% in 2020. Using the same input variables as RF3, FLAML3 worked better, increasing R2 by 1.52% and reducing RMSE by 5.6%. The improvement was more significant before the tillering stage.【Conclusion】Among all the methods we compared, FLAML3 is the most accurate and efficient for calculating ETa, due to its automated algorithm selection and hyperparameter tuning. It is robust and practical for estimating paddy field evapotranspiration in the Huai River Basin. |
| Key words: paddy field; evapotranspiration; machine learning algorithm; single crop coefficient model; automated machine learning framework |
