Research And Application Of Weather Data Prediction Methods Based On Deep Learning

In today’s society,meteorological data prediction has become an important issue related to the national economy and people’s livelihood.Meteorological changes directly or indirectly affect people’s food,clothing,housing,production and life.The occurrence of drought,heat wave,glacier melting,rainstorm,flood and other natural disasters will bring great trouble to people’s health and travel.At present,optimizing deep learning models can achieve more accurate meteorological predictions and intelligent meteorological prediction systems,bringing greater convenience and safety assurance to people’s production and life.This article focuses on historical meteorological data and uses an optimized deep learning model to predict meteorological elements.By analyzing and processing meteorological data,optimizing deep learning models to improve the accuracy of meteorological data prediction.The main content of this article includes the following three parts:(1)The first step in optimizing deep learning model experiments is data processing.The quality of data has a significant impact on the predictive performance of deep learning models.The processing methods used in this paper mainly include:filling the missing values and outlier in the data with linear interpolation;Normalize feature data with inconsistent dimensions;Using Spearman correlation analysis to extract effective features.(2)Aiming at the problem of large error in traditional weather prediction,this paper improves Harris Hawk algorithm and applies it to the parameter optimization of short-term memory network model,and proposes a prediction model based on IHHO-LSTM.Aiming at the problems of low precision,difficulty in escaping from local optimum and slow convergence speed of the original Harris Hawk optimization algorithm,piecewise linear chaotic mapping(PWLCM),elite Reverse learning mechanism,nonlinear escape energy update strategy and greedy strategy are introduced to determine the IHHO optimization algorithm.To address the difficulty in selecting parameters such as the number of layers,neurons,learning rate,and iteration times of the LSTM model,the IHHO algorithm is used to optimize its parameter set and reduce training costs.The results indicate that the RMSE,MSE,and MAPE values of the IHHO-LSTM prediction model have decreased to 2.4953,6.2267,and 0.0536,respectively,in predicting future weather by weather temperature.This indicates that the IHHO-LSTM prediction model has better convergence and accurate prediction results.(3)In order to demonstrate the effectiveness of prediction methods in practical applications,this article designs and implements a meteorological data prediction system,and introduces the proposed method to design a client system with multiple functional modules.Its main functions include: user and homepage functions,data management functions,model management functions,meteorological prediction functions,and related functions.