首页|基于四种算法比较分析Venlo型玻璃温室气温季节预报模型

基于四种算法比较分析Venlo型玻璃温室气温季节预报模型

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利用2021年2月27 日-2023年3月4日南京信息工程大学Venlo型玻璃温室内、外气象观测数据,基于多元回归(Multiple regression,MR)、BP 人工神经网络(BP artificial neural networks,BPANN)、随机森林(Random forest,RF)和支持向量机(Support vector machine,SVM)构建温室内日平均气温、日最低气温和日最高气温的季节预报模型,并进行验证。结果表明:温室内日平均气温、日最低气温季节预报模型的拟合精度明显高于日最高气温季节预报模型;各模型对春、夏、秋季温室内气温的拟合精度高于冬季。对于日平均气温和日最低气温季节预报模型而言,4种算法构建的春、夏、秋季预报模型的拟合精度均较高,RF模型模拟效果更为稳定,其模拟值与实际观测值决定系数(R2)均值均在0。94以上,均方根误差(RMSE)、绝对误差(MAE)均值在1。5℃以内;对于日最高气温季节预报模型,RF模型对春、夏、秋季的拟合精度整体高于其他模型,R2均值均在0。75以上。MR模型对冬季室内气温的拟合精度较好,更适用于预测冬季温室内气温。综合而言,选择RF模型预报春、夏、秋季的玻璃温室内气温,选择MR模型预报冬季玻璃温室内气温较为可行。
Based on Four Methods to Compare the Seasonal Temperature Forecasting Model for Venlo-type Glass Greenhouse
The seasonal temperature forecasting models in greenhouse based on multiple regression(MR),BP artificial neural networks(BPANN),random forest(RF)and support vector machine(SVM)were constructed using meteorological observations inside and outside the Venlo-type glass greenhouses from 27th February 2021 to 4th March 2023 at Nanjing University of Information Science & Technology,and then to validate all the models.The results showed that the fitting accuracy of the seasonal forecasting models for daily average and minimum air temperatures in the greenhouse was significantly higher than that of the seasonal forecasting models for daily maximum air temperature;the fitting accuracy of each model for air temperature inside the greenhouse in spring,summer and autumn was higher than that for winter.For the seasonal forecasting models of daily average air temperature and minimum air temperature,the fitting accuracy of the spring,summer and autumn forecasting models constructed by the four algorithms was higher,but the simulation effect of the RF model was more stable,and the coefficient of determination(R2)of the simulated values and the actual observed values were above 0.94,and the root mean square error(RMSE)and absolute error(MAE)were within 1.5℃.For the seasonal forecast models of daily maximum temperature,the fitting accuracy of RF model for spring,summer and autumn was higher than other models in general,and its R2 value was above 0.75.The MR model had higher accuracy for winter indoor air temperature and was more suitable for predicting winter greenhouse air temperature.The study concluded that it is feasible to choose the RF model to forecast the air temperature inside the glass greenhouse in spring,summer and autumn and the MR model to forecast the air temperature inside the glass greenhouse in winter,and this study can provide an important reference for the seasonal forecast of air temperature in Venlo-type glass greenhouse.

Venlo-type glass greenhouseSeasonal temperature forecasting modelNeural networksRandom forestSupport vector machine

吴慧臻、李东升、杨再强、张丰寅、陈旸

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南京信息工程大学江苏省农业气象重点实验室,南京 210044

南京信息工程大学应用气象学院,南京 210044

南京信息工程大学龙山书院,南京 210044

Venlo型玻璃温室 温度季节预报模型 神经网络 随机森林 支持向量机

国家自然科学基金面上项目大学生创新创业计划项目

42275200XJDC202310300298

2024

中国农业气象
中国农业科学院农业环境与可持续发展研究所

中国农业气象

CSTPCD
影响因子:1.679
ISSN:1000-6362
年,卷(期):2024.45(2)
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