Online Weather Forecaster

Abstract

Weather forecasting is one of the most challenging problems of the world because it uses multidimensional and linear data from various fields. This paper describes data mining algorithms namely regression algorithm and k-nearest neighbour. The algorithms are used for prediction. Using collected datasets, the Frequent Pattern Growth Algorithm for deleting the inappropriate data is applied. Temperature, humidity, and wind speed are mainly responsible for the weather prediction. On the percentage of these parameters, temperature, humidity and rainfall are predicted. Weather forecasting is an application of science and technology to predict the atmosphere of a particular location for specific range of time. It was one of the most scientifically and technologically challenging problems around the world in the last century. Since ancient times, weather prediction has been one of the most interesting and fascinating domain. Once an all-human endeavour based mainly upon changes in barometric pressure, humidity, temperature and sky condition, weather forecasting now relies on computer-based models that take many atmospheric factors into account. Human input is still required to pick the best possible forecast model to base the forecast upon, which involves pattern recognition skills and knowledge of model performance. The chaotic nature of the atmosphere, the massive computational power required to solve the equations that describe the atmosphere, error involved in measuring the initial conditions, and an incomplete understanding of atmospheric processes mean that forecasts become less accurate as the difference in current time and the time for which the forecast is being made increases. Use of data mining techniques in forecasting maximum and minimum temperature, rainfall, humidity and wind speed was investigated. This was carried out using regression technique and meteorological data collected from the previous years. The performances of these algorithms were compared with weather data for the predicted periods and the algorithm which gave the best results was used to generate classification rules for the mean weather variables. Responsible parameters for weather prediction are temperature, pressure and humidity.

Keywords: Algorithm, data mining, k-nearest, technology, weather forecasting

Cite this Article: Nidhi Verma, Niru Pandey, Krishna Giri. Online weather forecaster. International Journal of Algorithms Design and Analysis. 2020; 6(1): 13–23p.