Short-Term Weather Forecasting

Weather forecasting , meteorological analysis play a vital role in relation to sustainable development to minimize the damages caused due to extreme events that have the potential to roll-back, by several years, the progress made in development. The overall surface temperature is a key indicator of climate parameter changes. In this study, we present a new machine learning model that integrates the temporal correlations of multiple meteorological characteristics for temperature prediction. model to forecast data can be helped by this spatial feature and temporal interpretations. The SFA captures the related features on the desired feature. Most informative time steps as well as weather variables are learned by SFA. The prediction accuracy, as well as their performance has the advantage of appropriate spatial feature interpretation ability, is achieved by SFA. From the magnitude of correlation with the desired feature, the learned SFA weights are calculated.