FLASH-FLOOD MODELLING WITH ARTIFICIAL NEURAL NETWORKS USING RADAR RAINFALL ESTIMATES

• Main Authors: Dinu Cristian, Drobot Radu, Pricop Claudiu, Blidaru Tudor Viorel
• Format: Article
• Language: English
• Published: Sciendo 2017-09-01
• Series: Mathematical Modelling in Civil Engineering
• Subjects: artificial neural network; flash-floods; radar rainfall estimates; rainfall-runoff modelling
• Online Access: https://doi.org/10.1515/mmce-2017-0008

The use of artificial neural networks (ANNs) in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.