Research And Application Of Multivariate Feature Oriented Prediction Algorithm For Meteorological Data

In recent years,the formation of China’s meteorological real-time business system has supported China’s forecasting and forecasting capabilities to a new level.Timely and accurate meteorological data forecasting has great economic benefits and social significance for the development of economic construction,the improvement of national defense construction,the improvement of people’s living standards,and the prevention and control of meteorological disasters.With the rapid development of modern information technology,the meteorological observation data with time series characteristics shows an accumulation trend of Exponential growth,and the Law of value and association relationship hidden in the intensive data cannot be extracted only by traditional methods.Therefore,in order to better understand the change law and element difference of meteorological data,this paper takes feature optimization and time series prediction as the entry point,and conducts research on the temperature data in ground meteorological stations,The main content includes the following aspects:1.A data feature optimization strategy RA(RF-ARIMA)has been designed.This method uses Random forest feature importance measurement and threshold backward selection method to eliminate redundant features.Using the idea that autoregressive differential moving average model can capture the linear relationship of time series data,time series information is extracted from historical temperature data,and new feature sets are obtained by splicing and integrating.The ablation experiment shows that compared to the RF-CLAT prediction model without the addition of data feature optimization strategy,the RA-CLAT model in this paper has reduced MSE,RMSE,and MAE values by 0.19,0.13,and 0.08,respectively.This proves that the data feature optimization strategy can greatly reduce the interference of redundant information in the multi element feature data model in hourly temperature prediction,while enhancing the feature representation of the dataset and improving information utilization.2.A hybrid neural network prediction model RA-CLAT based on multiple feature selection strategy was designed to address the issue of low accuracy in meteorological data prediction caused by complex feature interference and unstable temporal learning.The model uses Convolutional neural network and Long short-term memory neural network to carry out significant feature coding learning and time sequence processing for the input sequence,and then Species reintroduction the self attention mechanism to calculate the weight to focus on key feature information,and finally realizes the hourly temperature prediction task.By comparing the experimental results,it can be seen that compared to models such as CNN,LSTMs,MS-LSTM,and ECTM,RA-CLAT has achieved the best performance in various evaluation values on the 58012 station observation dataset,with MSE of 0.49406,RMSE of 0.7029,MAE of 0.52771,and R~2 of0.94671.This demonstrates good predictive superiority and indicates good application prospects in practical meteorological data prediction scenarios.3.Design and implementation of meteorological data management system.Based on the above research,a web-based meteorological data management system is constructed.Firstly,the engineering background and feasibility analysis of the system are elaborated.Three functional modules,namely homepage,data management,and system management,are designed according to the requirements.The database is also designed,followed by a presentation of the main user pages.Finally,the application scenarios and expected results of key functional points are tested to verify the feasibility of the system.