Analyse of Weather Forecasting Models Using LSTM with Noise Removal Methods

Abstract

Weather forecasting is one of the most important fields for all sectors such as transportation (air traffic, marine), industry, agriculture, forestry and even public health sector. The aim of this study is to analyse the weather forecasting models using filtering techniques which can remove the noise involved in the time series weather data. We utilize ten years weather data (2013 to 2022) in Hmawbi region, Yangon, Myanmar. The weather dataset includes maximum and minimum temperatures, humidity, wind speed, cloud amount and weather type. It is taken from the Department of Meteorology and Hydrology, Myanmar. The original data includes noise data. Thus, two noise removal methods, Simple Moving Average and Weighted Moving Average, are used for data cleaning. In this paper, three prediction models are developed by using Long Short-Term Memory (LSTM) with different datasets: one with the original data (without noise removal), one with data cleaned using the Simple Moving Average method, and another with data cleaned using the Weighted Moving Average method. The research goal is to compare and evaluate the performance of these three weather prediction models to determine which one gives the better results. To evaluate these models, Root Mean Square Error (RMSE) and Mean Square Error (MSE) are calculated for each model. LSTM achieves RMSE of 5.398 and MSE of 29.020, SMA achieves RMSE of 3.767 and MSE of 14.841, WMA achieves RMSE of 0.798 and MSE of 0.534. According to the experimental results, it is found that model with data cleaned using the Weighted Moving Average method is lower error rates than other two models and gives the better predicting results than other two models.