Temperature Correction Model Based On Machine Learning And Multi-Meteorological Factor Model

Surface temperature is one of the most concerned meteorological factors in people's daily life,which has an important impact on agriculture,industry,service industry and other fields.In order to improve the accuracy of the model forecast temperature in the current daily forecast operation,we correct the model forecast temperature based on the ensemble control forecast historical data with longitude of 120-130 ° E and latitude of 20-40 ° N of European Centre for Medium-Range Weather Forecasts(ECMWF)and the temperature data of the actual stations in East China.It mainly includes the following three modules:(1)Analyze and process experimental data.The interpolation performance is evaluated by using the comprehensive evaluation index constructed by root mean square error,absolute error and temperature prediction accuracy.Finally,Kriging interpolation method is used to spatially correspond the model prediction data with the actual station temperature,and then the violin chart is used to analyze and clean the abnormal value of the interpolated data to obtain reliable experimental data.Then,in order to fully mine the potential relationship between the model prediction data and the actual temperature,the prediction features of the highest temperature and the lowest temperature are polynomial extended to obtain the input features of the subsequent modeling;(2)Multi-meterological factors model temperature prediction correction model based on traditional machine learning.On the basis of the first step polynomial feature expansion,linear regression,support vector machine,decision tree and random forest are used to establish the temperature correction model respectively for the highest temperature and the lowest temperature.According to the setting of different correlation coefficients,the data after the quadratic,cubic and quartic polynomials expansion are selected,and the features with the correlation between the quadratic expansion and the actual temperature above 0.2-0.8 and all features are selectedrespectively for modeling.The features with the correlation between the cubic expansion and the actual temperature above 0.2-0.8 and the features with the correlation between the quartic expansion and the actual temperature above 0.4-0.8 are built The most suitable model for the lowest and highest temperature correction was selected;(3)Multi-meterological factors model temperature prediction correction model based on deep learning.K-means is used to cluster the extended data of the highest temperature and the lowest temperature.In order to ensure the accuracy of clustering,elbow rule and contour coefficient are used to select the categories.The lowest temperature samples are classified into three categories,and the highest temperature samples are classified into four categories.After clustering,long-term memory network(LONG SHORT-TERM MEMEORY,LSTM)combined with attention mechanism is used to build a correction model of the highest temperature and the lowest temperature.After clustering,respectively targeted modeling can make full use of the diversity of samples to obtain the best model.Finally,the performance of the proposed deep learning temperature correction model and machine learning temperature correction model is compared with that of Kalman filter and neural network model.