Smart City Flood Monitoring: A Systematic Review and Integration of XAI to Address Data Inconsistencies

Real-time flood monitoring systems are important in smart cities to reduce the effects of floods by informing the citizens in advance. However, incorporating AI in this system is difficult due to issues of standardization of data gathering approaches, formats, and sources. The innovation proposed for this research paper is an explainable artificial intelligence (XAI) based flood monitoring system that can effectively handle these challenges. With the proposed system, the ministry will be able to predict the occurrence of floods accurately and on time with the help of machine learning algorithms, weather forecasts, cameras and historical flood records. Most critically, the XAI component builds accountability and reliability of the AI models, helping the stakeholders comprehend the system's thinking process. With the focus on data integration and increasing the transparency of AI prediction, this system should assist better decisions in flood management and try to eliminate data disparities to promote the 'smartness' of smart cities.