DOI : https://doi.org/10.18517/ijaseit.14.1.18658

Improving Convective Cloud Classification with Deep Learning: The CC-Unet Model

Humuntal Rumapea (1), Mohammad Zarlis (2), Syahril Efendy (3), Poltak Sihombing (4)
(1) Department of Computer Science, Universitas Sumatera Utara, Medan, 20155, Indonesia
(2) Department of System Information Management, Bina Nusantara University, Jakarta, 11480, Indonesia
(3) Department of Computer Science, Universitas Sumatera Utara, Medan, 20155, Indonesia
(4) Department of Computer Science, Universitas Sumatera Utara, Medan, 20155, Indonesia
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How to cite (IJASEIT) :
Rumapea, Humuntal, et al. “Improving Convective Cloud Classification With Deep Learning: The CC-Unet Model”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 1, Feb. 2024, pp. 28-36, doi:10.18517/ijaseit.14.1.18658.
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Analyzing and mitigating natural disasters can be a challenging task, which is why the field of computer science, specifically artificial intelligence (AI) is necessary to aid in the complexity of disaster management. AI provides the tools and analytical models to help solve the intricacies of handling natural disasters. Convective clouds, closely related to rain and can lead to large-scale, prolonged hydrometeorological disasters, are a crucial component to consider. To improve the classification of these clouds, a predictive-analytical model based on deep learning, called the CC-Unet model, was developed. This model utilizes a U-Net architecture and is trained using a dataset of convective cloud images. The researchers used satellite image data from the Himawari 8 satellite collected in May and October 2021. The images were pre-processed and verified using observational data. The model was tested using a random train-test split method, showing that the CC-Unet model had a higher accuracy of 97.29% compared to the U-Net model, which had an accuracy of 94.17%. Additionally, the significance test using the Wilcoxon method showed that the CC-Unet model had significantly different performance results from the U-Net model. The ground truth image was also compared with the predicted image, showing a low root mean square error value of 0.0218, indicating a high level of similarity between the two. Overall, this research demonstrates the potential of AI and deep learning in classifying convective clouds to aid in natural disaster management.

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