Exploring Advanced Deep Learning Techniques for Reliable Weather Forecasting

Weather forecasting has always been a domain where more and more reliability is looked up to when it comes to prediction and the models pursued. While in the past years, traditional methods included conventional probability-dominated dependency models, the reliability of these traditional methods was always in a contentious line which emphasized the necessity to improve and use modern technology. While in the past few years, we became familiar with the ways that can put the huge amount of data to an optimum utility, it only widened the horizons which makes room for us to explore various important domains where reliability is indeed a necessity, that could very much use the help of advanced technology to be more decisive. The materializing deep learning techniques tied up to the humongous availability of observed data concerning the field of weather prediction have enticed enough researchers to traverse the concealed hierarchical pattern in the voluminous weather datasets. The study was done to inquire into various deep learning techniques including discussing some of the advanced and recently under-the-light methods.