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.
