Real-Time Rainfall Monitoring and Flood Inundation Forecasting Through Deep Transfer Learning

Urban flooding is becoming an increasingly pressing issue due to the impact of both climate change and urbanization on stream and river flow patterns. The rapid and unexpected rise in water levels, known as flash floods, can pose a particularly serious threat in urban areas where water movement is occasionally constrained by man-made obstacles. By constructing channel straightening and shortening, riverbank walkways, and other river regulation projects, the likelihood of flooding is increased within this framework. Included in the proposed system are model training and preprocessing. Mapping and principal component analysis are part of the preprocessing phase. By incorporating solar power projections into renewable energy systems, the model was able to attain a training accuracy of 90.11% using Model DTCN. When it comes to flood forecasting, the only thing that gets thoroughly examined is how well the model reproduces discharge hydrographs by assuming that rainfall is known accurately throughout the event. In order to increase the area's overall resistance to flash floods, the results show that the suggested methodology produces trustworthy behaviour.