1. bookVolume 13 (2017): Issue 3 (September 2017)
Journal Details
License
Format
Journal
eISSN
2784-1391
First Published
12 Apr 2013
Publication timeframe
4 times per year
Languages
English
access type Open Access

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

Published Online: 07 Nov 2017
Volume & Issue: Volume 13 (2017) - Issue 3 (September 2017)
Page range: 10 - 20
Journal Details
License
Format
Journal
eISSN
2784-1391
First Published
12 Apr 2013
Publication timeframe
4 times per year
Languages
English
Abstract

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.

Keywords

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