A review on flood management technologies related to image processing and machine learning

Abstract Flood management, which involves flood prediction, detection, mapping, evacuation, and relief activities, can be improved via the adoption of state-of-the-art tools and technology. Focusing on ways to mitigate floods and provide a quick response after floods is critical to ensuring fatalities are minimized, along with reducing environmental and economic damages. In the literature, techniques from different domains including remote sensing, machine learning, image processing and data analysis have been explored to manage different tasks related to flood management. This study proposes a new framework that categorizes the recent research that has been conducted on flood management systems. The framework addresses the following significant research questions: (1) What are the major techniques deployed in flood management? (2) What are the phases of flood management which existing studies tend to focus on? (3) What are the systems that are proposed to tackle problems related to flood management? (4) What are the research gaps identified in the literature when it comes to deploying technology for flood management? A classification framework for flood management has been proposed to group the various technologies reviewed. Lack of hybrid models, which combine image processing and machine learning, for flood management was observed. In addition, the application of machine learning-based methods in the post-disaster scenario was found to be limited. Thus, future efforts need to focus on combining disaster management knowledge, image processing techniques and machine learning tools to ensure effective and holistic disaster management across all phases.