AI-Enabled Agricultural World Models for Drought and Flood Prediction Using Multimodal Data

Abstract: This research paper delves into the realm of AI-enabled agricultural world models for predicting droughts and floods by leveraging multimodal data. The rapid advancements in artificial intelligence (AI) offer promising solutions to address the challenges posed by unpredictable weather patterns and their effects on agriculture. By integrating diverse sources of data such as satellite imagery, weather patterns, soil moisture levels, and crop health indicators, the proposed models aim to provide accurate and timely predictions of droughts and floods. The utilization of AI algorithms, including machine learning and deep learning techniques, enables the analysis of vast amounts of data to identify patterns and generate actionable insights for farmers and policymakers. The integration of multiple data modalities enhances the robustness and reliability of the predictive models, ultimately contributing to improved decision-making processes in agriculture to mitigate the impacts of extreme climatic events. Keywords: AI-enabled, agricultural world models, drought prediction, flood prediction, multimodal data, artificial intelligence, machine learning, deep learning, predictive analytics,griculture