Integrating Multi-Modal Data to Predict Flooding Impacts on Agriculture and land energy infrastructure.

This study introduces a novel framework for integrating multi-modal data to enhance the prediction of flooding impacts on agriculture and energy grids. We focus on synthesizing various advanced data fusion techniques—such as cross-modal learning and joint representation—to identify the most effective approaches to employ with multi-modal data. Our research compares different cutting-edge AI architectures, such as, convolutional neural networks (CNNs), transformers, graph neural networks (GNNs), and hybrid models to evaluate which techniques are best suited for use in our proposed AI framework for multi-modal data integration. These models are tailored to work with diverse data sources, including satellite and radar data, demographic surveys, ancillary physical data, and non-traditional data like social media information. We emphasize the importance of addressing uncertainties in both data and model predictions, considering biases introduced into the models, and exploring how uncertainty propagation can be managed within this multi-modal framework. By critically evaluating each method’s potential and limitations for predicting damages from flooding events and uncertainty quantification, we aim to provide a robust, transparent, and reliable framework for easy deployment. This framework has the goal of facilitating reductions in flooding related economic losses, identification of vulnerabilities in energy grids, and improved risk communication strategies for at-risk populations. Our study seeks to advance practical applications of AI for managing severe weather-related impacts in critical sectors as well as scientific understanding.