Comparative Analysis of Forecasting Techniques in Weather Forecasting

For planning and decision-making in a variety of industries, including agriculture, transportation, and tourism, accurate weather forecasting is crucial. To do this, a variety of forecasting methodologies, from statistical approaches to machine learning algorithms, have been created and put to use. This study compares and contrasts Support Vector Machines (SVM), Linear Regression (LR), and Random Forest (RF), three well-known machine learning techniques for forecasting the weather. Using a weather dataset with several meteorological factors, including temperature, pressure, humidity, and wind speed, we compared the effectiveness of these strategies. Using various performance indicators, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), we assessed the models' correctness. In order to determine the most crucial meteorological factors for weather forecasting, we also performed a feature selection analysis. With an MAE of 0.51 and an RMSE of 0.68, our findings demonstrate that the SVM algorithm performed better than the other two strategies in terms of accuracy. The MAE for the LR and RF algorithms was 0.67 and 0.58, respectively, demonstrating strong performance. Temperature, pressure, and humidity were found to be the most reliable indicators of meteorological conditions by the feature selection study. We created a project using the SVM algorithm for weather forecasting based on these discoveries. The project's objective is to give farmers precise weather forecasts to help them manage their crops. In conclusion, this study and research article shows how machine learning algorithms work for weather forecasting and offers insights into a comparison of various methods. The findings imply that SVM is the best technique for precise and trustworthy weather forecasting.