A Review on Urban Flood Management Techniques for the Smart City and Future Research

Flooding in cities is a worldwide occurrence that presents a significant problem to municipal administrations and urban planners. The loss of the life, delays in public transportation, damage to public and private property, the interruption of services such as the water supply and power supply are some of the effects of urban flooding which leads to economic losses as well as public health issues. The motive of this research paper is to review the various strategies for managing urban floods and to determine the research scope in terms of smart city development. The flood is one of the most prevalent natural catastrophes that may strike any city. 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 IoT. The accuracy of flood prediction of computational intelligence techniques is only 76.48% in average.