Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

Abstract Natural disasters can be devastating to the environment and natural resources. Flood inundation mapping and hydraulic modeling are essential to forecast critical flood information, including flood depth and water surface height. In this research, several factors that influence floods were studied. These factors include the intensity of the rainstorm, the depth of precipitation, soil types, geologic settings, and topographic features. Furthermore, the research carried out hydraulic modeling of storm flows for 50- and 100-Year return periods and estimated that the water depth in Wadi Al Wala could reach 15m at 50 years of storm and 25m at 100 return years of storms. A DNN model is developed with good accuracy to predict flood flow based on historical records from 1980 to 2018 meteorological data. The goal of this research is to improve flood prediction, and risk assessment with the use of DNN integrated with hydrological and hydraulic models.