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Analyzing Impact of Time on Early Detection of Rainfall Event

Rainfall is a critical feature of a climatic system, which has a chaotic impact on agriculture, water resource management and biological systems. An early and accurate prediction of rainfall is a very important task and has vital effects on human life. However rainfall prediction is a challenging task in meteorology. Rainfall data mostly have high inconstancy and irregular patterns which are rare in other time series data. The rainfall data changes greatly with time, so the time factor has a high importance in such time series data. In order to develop efficient forecast models, one should deeply analyze the effect of time on prediction accuracy. Therefore, in this paper, we analyze how early we can predict a rainfall event with accuracy. We have performed the experiments on a 5-year daily rainfall data obtained from National Oceanic and Atmospheric Administration (NOAA) at 1-, 2-, 3-, and 4-day prior forecasting horizons using a machine learning technique to observe the trends in the prediction accuracy. Furthermore, we have also identified some most important input features from the dataset which plays a major role in the prediction of a rainfall event. Our results conclude that average wind speed and minimum temperature are most important weather variables in classifying rainfall events. We also observe that the forecasting error gradually increases with increasing lead times.