Weather Forecasting Using Sliding Window Algorithm

To predict the future’s weather condition, the variation in the conditions in past years must be utilized. The probability that the weather condition of the day in consideration will match the same day in previous year is very less. But the probability that it will match within the span of adjacent fortnight of previous year is very high. So, for the fortnight considered for previous year a sliding window is selected of size equivalent to a week. Every week of sliding window is then matched with that of current year’s week in consideration. The window best matched is made to participate in the process of predicting weather conditions. The prediction is made based on sliding window algorithm. The monthwise results are being computed for three years to check the accuracy. The results of the approach suggested that the method used for weather condition prediction is quite efficient with an average accuracy of 92.2%. 1. Introduction Weather forecasting is mainly concerned with the prediction of weather condition in the given future time. Weather forecasts provide critical information about future weather. There are various approaches available in weather forecasting, from relatively simple observation of the sky to highly complex computerized mathematical models. The prediction of weather condition is essential for various applications. Some of them are climate monitoring, drought detection, severe weather prediction, agriculture and production, planning in energy industry, aviation industry, communication, pollution dispersal, and so forth, [1]. In military operations, there is a considerable historical record of instances when weather conditions have altered the course of battles. Accurate prediction of weather conditions is a difficult task due to the dynamic nature of atmosphere. The weather condition at any instance may be represented by some variables. Out of those variables, one found that the most significant are being selected to be involved in the process of prediction. The selection of variables is dependent on the location for which the prediction is to be made. The variables and their range always vary from place to place. The weather condition of any day has some relationship with the weather condition existed in the same tenure of precious year and previous week. A statistical model is designed [2] that could predict the rainfall and temperature with the help of past data by making use of time-delayed feed forward neural network. Artificial neural network was combined with the genetic algorithm to get the more optimized prediction [3]. An