Artificial Intelligence Based Urban Flood Control System For Smart City

Abstract

newlineUrban flooding is a major problem faced by many countries around the world. With the rapid increase in urbanization and climate change, the frequency and intensity of flooding events are expected to increase in the future. The increasing trend of urban flooding is a universal phenomenon and poses a great challenge to city administrations and urban planners the world over. Problems associated with urban floods range from relatively localized incidents to major incidents, resulting in cities being inundated for a few hours to several days. In recent years, Artificial Intelligence (AI) has emerged as a promising technology for addressing various urban challenges, including flood management. Internet of Things (IoT) has been applied in areas such as flood monitoring, flood detection, and flood control. In recent years, machine learning, Fuzzy logic, and Artificial Neural Network, have played an important role in forecasting of weather and floods. Floods are unpredictable events, even with the numerous developments in flood prediction technology. These methods can be helpful for warning people, but not when a flood event is happening. The present study on managing urban floods focuses on a review of the existing strategies for managing urban floods and determining the scope of the research in terms of smart city development. Rainfall, water level, temperature, humidity, drainage water level, water discharge, as well as other parameters are generally viewed in flood prediction models, including artificial neural networks (ANN), fuzzy inference processes, regression models, deep learning, gradient-boosting decision trees, and Self-Organizing feature mapping networks (SOM). Real-time flood parameters were considered in the flood detection and warning system. Real-time flood characteristics were considered in the flood detection and warning system, and the system was constructed utilizing the Internet of Things (IoT). The accuracy of flood prediction using computational intelligence techniques is only 76.48% on averag