Sourav Dey Roy
ABSTRACT
Rainfall prediction is an important research topic because of its wide range of applications in disaster and agricultural communities. It depends on several features of earth's atmosphere such as cloud information, speed and direction of wind, temperature, dew point, atmospheric pressure, etc. Most of the existing rainfall prediction models are based on time series dataset. Considering the computational complexity, and cost factors of time series dataset, in this paper we extensively explored the performance of different Convolutional Neural Networks (CNNs) architectures for rainfall prediction using cloud images in different scenarios. In our work, we have used two different stages for effective prediction of rainfalls from cloud images. Experiment results on SWIMCAT dataset reveals that usefulness and effectiveness of CNNs for rainfall prediction. This study can be a useful contribution for the research community of weather forecasting with broad range of applications i.e., flight navigation to agriculture and tourism.
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Index Terms
- Cloud Pattern Classification for Rainfall Prediction using Convolutional Neural Network
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