Weather Prediction: Improving Accuracy Using Data Mining and Forecasting Techniques
This study is focused to provide the insights of weather to understand the significance of weather changes in any parameter. Weather forecasting contributes to the social and economic welfare in many sections of the society. Weather is extremely difficult to predict because it is a complex and chaotic system. This means that small errors in the initial conditions of a forecast grow rapidly and affect predictability. Nowadays, massive real-time data is being generated by IoT devices, radars, weather stations, and satellites. The need to adopt big data analytics in IoT applications is compelling. These two technologies have already been recognized in the fields of IT and business. Data mining techniques and machine learning algorithms need to be considered and trained with big data to improve the accuracy of weather forecasts. The contribution to this problem is to analyze the accuracy and correlation between weather conditions with the use of different data mining and forecasting techniques to predict precipitation for the next year.
Marquez, Pedro Alejandro, "Weather Prediction: Improving Accuracy Using Data Mining and Forecasting Techniques" (2020). ETD Collection for University of Texas, El Paso. AAI28088640.