Advanced Ensemble Deep Learning Framework for Enhanced River Water Level Detection: Integrating Transfer Learning

Abstract

The precise monitoring and prediction of river water levels are crucial for effective environmental management, flood prevention, and ensuring water security. This paper introduces an advanced deep learning framework that utilizes an ensemble of state-of-the-art neural networks, namely InceptionV3, VGG16, Xception, MobileNet, and ResNet152, to enhance the accuracy of water level detection from river imagery. The proposed system integrates these models through a robust ensemble methodology that leverages hard voting to improve predictive performance and reliability. Through rigorous preprocessing, including normalization, resizing, and augmentation, alongside strategic transfer learning, the framework achieves an impressive accuracy of 99.5833%, precision of 99.5929%, recall of 99.5762%, and an F1 score of 99.5838%. The ensemble approach not only addresses the variability in image data but also ensures robustness against overfitting and data imbalances. Furthermore, the application of Gradient-weighted Class Activation Mapping (Grad-CAM) enhances the interpretability of the model's decisions, facilitating trust and transparency in its predictions. This study not only demonstrates the potential of ensemble deep learning in hydrological applications but also sets the stage for future enhancements such as real-time processing and integration into comprehensive flood management systems. Future research will explore scalability, the incorporation of additional predictive variables, and the expansion of the model to include real-time monitoring capabilities, aiming to provide a more dynamic tool for disaster readiness and environmental conservation.