Machine Learning-based Short-term Rainfall Prediction from Sky Data

To predict rainfall, our proposed model architecture combines the Convolutional Neural Network (CNN), which uses the ResNet-152 pre-training model, with the Recurrent Neural Network (RNN), which uses the Long Short-term Memory Network (LSTM) layer, for model training. By encoding the cloud images th...

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Bibliographic Details
Published in:ACM transactions on knowledge discovery from data 2022-12, Vol.16 (6), p.1-18, Article 102
Main Authors: Tey, Fu Jie, Wu, Tin-Yu, Chen, Jiann-Liang
Format: Article
Language:English
Subjects:
Computer vision
Computing methodologies
Neural networks
Supervised learning
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Summary:To predict rainfall, our proposed model architecture combines the Convolutional Neural Network (CNN), which uses the ResNet-152 pre-training model, with the Recurrent Neural Network (RNN), which uses the Long Short-term Memory Network (LSTM) layer, for model training. By encoding the cloud images through CNN, we extract the image feature vectors in the training process and train the vectors and meteorological data as the input of RNN. After training, the accuracy of the prediction model can reach up to 82%. The result has proven not only the outperformance of our proposed rainfall prediction method in terms of cost and prediction time, but also its accuracy and feasibility compared with general prediction methods.
ISSN:1556-4681
1556-472X
DOI:10.1145/3502731