Weather prediction based on LSTM model implemented AWS Machine Learning Platform
Danny Hoang
Abstract
Climate change has affected the weather forecast on a regular basis compared to reality. Meanwhile, weather forecast plays an important role in daily life and especially it affects developed countries in agricultural fields around the world. When we apply information technology software, we can assess the general weather condition of a given city, and with the help of recent modern scientific methods for more accurate analysis and prediction of weather based on those collected weather data for a period of a week earlier or longer for future weather forecasts. Therefore, in this study, machine learning model was applied and it was allowed to study methods and feature engineering from data pre-processing, so through this, we find an unpredictable model of climatic conditions, a prognostic model is also monitored. For this work, various weather parameters were collected from the national climate data center through available software applications, with the application of the Long Short-Term Memory (LSTM) model is an artificial recurrent neural network (RNN) were trained for different combinations that are implemented on AWS machine learning platform. In the process of predicting weather conditions with today's modern technology, neural networks are practiced using different combinations of weather parameters such as temperature, precipitation, humidity, wind speed, and pressure. After training the LSTM model with these parameters, in addition to the results of the plot of model accuracy for the training and validation sets and performance evaluation metrics to test the model, this study also concluded the results of the weather condition by 2 values (good or bad).