Improvement of Rainfall Estimation Accuracy Using a Convolutional Neural Network with Convolutional Block Attention Model on Surveillance Camera
Iqbal Iqbal, Adhi Harmoko Saputro, Alhadi Bustamam, Ardasena Sopaheluwakan · International Journal of Advanced Computer Science and Applications · 2025
Accurate rainfall estimation is essential for various applications, including transportation management, agriculture, and climate modeling. Traditional measurement methods, such as rain gauges and radar systems, often face challenges due to limited spatial resolution and susceptibility to environmental interferences. These constraints affect the ability of the model to deliver high-resolution, real-time rainfall data, allowing the model to be challenging to capture localized variations effectively. Therefore, this study aimed to introduce a hybrid deep learning architecture that combined a Convolutional Neural Network (CNN) with a Convolutional Block Attention Module (CBAM) to improve rainfall intensity estimation using images captured by surveillance cameras. The proposed model was evaluated using standard datasets and previous unseen images collected at different times of the day, including morning, noon, afternoon, and night, to assess its toughness against temporal variations. The experimental results showed that VGG-CBAM architecture performed better than ResNet (Residual Network)-CBAM across all evaluation metrics, achieving a coefficient of determination (R²) of 0.93 compared to 0.89. Furthermore, when tested on unseen images captured at different periods, the model showed strong generalization capability, with correlation values (R) ranging from 0.77 to 0.98. These results signified the effectiveness of the proposed method in improving the accuracy and adaptability of image-based rainfall estimation, offering a scalable and high-resolution alternative to conventional measurement methods.