A Prediction Scheme for Daily Maximum and Minimum Temperature Forecasts Using Recurrent Neural Network and Rough set

4th International Conference on Advances in Energy Resources and Environment Engineering

能源学;生态环境科学

Huang, Ying^1 ; Zhao, Huasheng^1 ; Huang, Xiaoyan^1

Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning, Guangxi

530022, China^1

关键词: Attribute reduction;    Attribute reduction method;    Maximum and minimum temperatures;    Mean absolute error;    Minimum temperatures;    Recurrent neural network (RNN);    Stepwise regression method;    Temperature prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/237/2/022005/pdf
DOI  :  10.1088/1755-1315/237/2/022005

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

A new nonlinear objective prediction scheme has been developed for predicting 24h daily maximum and minimum temperature forecasts at 14 stations in Guangxi, China during Jan, 2015-Jun, 2018 using Recurrent Neural Network (RNN) and based on the daily average, maximum, minimum temperature and precipitation data. Taking the climatology and persistence predictors as primary factors, the conditional attribute reduction method of rough set theory is adopted. By eliminating the unrelated attributes, the predictors direct correlated with the predictand (maximum and minimum temperature) are taken as the RNN model input by means of attribute reduction. This new scheme is validated with 24h short-range forecasts spanning Jan to Jun, 2018. Using identical predictors and sample cases, predictions of the RNN model are compared with the stepwise regression method, and results show that the former is more accurate. The mean absolute errors of RNN at 14 stations in Guangxi are lower than those of the stepwise regression method. The mean forecast accuracy with absolute errors being less than 2°C (1°C) of RNN is higher than that of the stepwise regression method. Moreover, the number of forecast errors larger than 2°C and the system deviation of daily maximum (minimum) temperature prediction are significantly reduced by RNN model, indicating a potentially better operational weather prediction tool.