Weather Forecasting with Machine Learning

Abstract

Due to its numerous applications in industries including agriculture, utilities, and daily life, weather forecasting has been a significant factor. In the past ten years, the world has faced real-time difficulties with weather forecasting. Because of the constantly shifting weather, the prediction is getting more difficult. The goal of weather forecasting is to foresee future changes in the atmosphere. Understanding the numerous contributing elements that lead to weather changes is essential for effective weather analysis. The process of recording meteorological variables, such as wind direction, wind speed, humidity, rainfall, temperature, etc., is known as weather forecasting. Since machine learning techniques are more robust to perturbations, in this project we applied Neural Network with DNN regressor models and LSTM to predict the weather such as temperature, humidity etc. and compare both approaches and analyzed it. We used two different datasets for the same. Coming to result that we got from each approach was quite amazing. In the Neural Network with DNN regressor approach, we got mean absolute error about mean absolute 1.49 mm and median absolute error 0.94 Celsius and explained variant 0.90 when performing rainfall and temperature prediction respectively whereas in the deep learning approach, the mean absolute error was 0.002268 degree Celsius, when performing temperature, wind speed and pressure prediction respectively. We could clearly see the difference between the outcomes. Keywords: Data Mining, Machine Learning, UCI Dataset, Weather Forecasting, Deep Learning.