Machine Learning Based Effective Fine Grained Weather Forecasting Model

It is well-known that numerical weather prediction (NWP) models require considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a novel light weight data-driven weather forecasting model by exploring temporal modelling approaches of More specifically Standard Linear Regression (SR), Ridge Regression (RR), Support Vector Regression (SVR), and Random Forest Regressor (RF), Decision Tree Regressor (DT) are implemented as the classical machine learning approaches, and Autoregressive Integrated Moving Average (ARIMA) is implemented as the statistical forecasting approaches. Furthermore, Weather information is captured by time-series data and LSTM is to predict future data. Our experiment shows that the proposed lightweight model produces better results.

Keywords · Machine Learning Algorithms (Random Forest, Support Vector Regressor, Linear Regression, Ridge and Decision Tree) with LSTM Time-series data analysis.