Publication

Abstract : In this fast developing world weather forecasts based on temperature, humidity and precipitation are of greater importance to agriculture. For example, farmers rely on temperature forecasts to decide day-to-day activities. Computer based Prediction techniques can be employed to predict the future temperature based on the existing data so that the farmers can plan their activities ahead which ultimately results in improved production yield. For a successful forecasting, Selection of right prediction algorithm is of paramount importance, so that the results will be highly accurate with less forecast error. Hence the aim of this paper is to focus on comparing various Machine learning and deep learning regression algorithms applied on temperature data, and thereby evaluating which regression algorithms are best in minimizing the overall prediction error. After the successful prediction of data, human effort is required to interpret the modeled data and to make it understandable to the end user.