Urban Rainfall-Runoff Modeling Using HEC-HMS and Artificial Neural Networks: A Case Study

Abstract

Urban flooding nowadays becomes common throughout the world. The main reason for these floods is rapid urban development and climate change. During the monsoon, the flows in the urban drains will be high and the main reason for these high flows is the existence of a combined network system (i.e. drainage and stormwater). Further, the flow in the city (under study) drainage network was very high and some areas of the network exceeds more than discharge carrying capacity. Hence, this may result in overflow from the manholes and create an overland flood problem. Rainfall-Runoff modeling in these situations in the urban catchment will be essential and required to understand the flow pattern that helps in flood management. Therefore, the current study chose Hydrologic Modeling System (HEC-HMS) and Artificial Neural Network (ANN) for rainfall-runoff modeling at an hourly period for the Kukataplly (zone-12) watershed of Hyderabad city, Telangana State in India. This zone-12 watershed was one of the most affected hydraulic zones of Greater Hyderabad Municipal Corporation (GHMC) during the monsoon period in the past 21 years. The present study focuses on a comparative study between HEC-HMS and ANN has been carried out to comprehend the flood scenario in the study area. Finally, the performance of the model is checked with statistical indices such as Nash-Sutcliff Efficiency (NSE), and Coefficient of Determination (R2). HEC-HMS yielded good results (NSE = 0.74 and R2 = 0.76) when it has taken care of the maximum possible nonlinear complex data to be analysed.