A review of integrated use of machine learning algorithms, GIS and remote sensing techniques in the prediction of rainfall patterns and floods in the U. S.

This paper systematically reviews the integrated use of machine learning algorithms, Geographic Information Systems (GIS), and Remote Sensing (RS) techniques in the prediction of rainfall patterns and flood events in the U.S. With increasing climate variability, accurate forecasting of rainfall and flood risks has become critical to safeguarding communities and infrastructure. GIS enables spatial analysis and mapping of flood-prone areas, supporting risk assessment and disaster preparedness. RS contributes real-time satellite imagery and environmental data, essential for tracking rainfall patterns and assessing surface conditions. Machine learning algorithms enhance these technologies by providing predictive modeling capabilities, allowing for more precise forecasts of rainfall intensity and flood potential. This paper explores the synergy between GIS, RS, and machine learning, emphasizing their combined impact on improving flood prediction accuracy and decision-making in disaster management. Key challenges, including data heterogeneity, computational demands, and the integration of diverse datasets, are discussed. Additionally, the paper reviews current U.S. policies on data-sharing and technology adoption, highlighting the need for regulatory frameworks that support innovation while ensuring data privacy and accuracy. Through an analysis of recent studies, this paper presents a comprehensive overview of the advantages and constraints of using these integrated technologies for flood prediction, offering insights into future directions and recommendations for enhancing flood management systems. The review concludes that advancing integrated GIS, RS, and machine learning applications will require addressing data-related challenges and fostering collaborative efforts across agencies to strengthen flood prediction and resilience capabilities in the U.S.