Data-Driven Approaches to Weather Prediction With Machine Learning

Weather forecasting joins among the most critical undertakings in agriculture, disaster management, transportation, and many other sectors. Though traditional forecasting techniques are commonly employed, they tend not to model the non-linear behavior of the atmospheric systems correctly. Thus, this study aims at improving the accuracy and efficiency of weather prediction through machine learning techniques. Considering a dataset composed of daily meteorology descriptive variables such as average temperature, humidity, wind speed, etc. and a barometric pressure, the research analyzes the effectiveness of the following machine learning algorithms as Linear Regression, Random Forest, Decision Tree and Support Vector Machine. Preprocessing of the data set included missing value treatment, feature enhancement and time-series modelling. Also, temporal analysis included season, trends and other variations which are time dependent. Models were further assessed by performance indices such as the Area Under the Curve, F1 Score and Accuracy. Results obtained suggest that complex weather patterns are best captured using more complex models such as neural networks and ensemble methods in comparison to simple models. As this study shows, machine learning may assist in improving the weather forecast by making it more accurate and more efficient. In addition, such use of these models can enhance the management of weather-sensitive sectors. The next steps will include the use of actual data collected in real-time and taking into consideration deep learning models to increase the prediction accuracy and scale, and also the ability to do multistep forecasting.