Synergizing Remote Sensing, Machine Learning, and Iot: Cloud-Based Innovations in Drought Prediction

This paper explores cutting-edge advancements in leveraging remote sensing, machine learning (ML), and Internet of Things (IoT) technologies within cloud-based frameworks for enhanced drought prediction. By integrating data from satellites and IoT sensors, ML algorithms generate predictive models to identify drought patterns. Cloud computing facilitates scalable infrastructure for data processing and model deployment, foster- ing collaboration and resource optimization. Challenges including data integration and user engagement are addressed through interdisciplinary collaboration. It illustrates the efficacy of this integrated approach in informing decision-making and bolstering resilience in water resource management. Synergizing remote sensing, ML, and IoT within cloud-based frameworks offers a promising avenue f ,r mitigating the complexities of drought prediction and adaptation in the face of a changing climate.