Real-Time Flood Forecasting Using Artificial Neural Networks (ANN) and Flood Inundation Mapping

No Thumbnail Available



Journal Title

Journal ISSN

Volume Title


Addis Ababa University


Flooding is one of the most destructive and harmful natural disasters occurring in many parts of the world and there is increasing evidence that losses are rising largely because more people are settling in flood prone areas. In many regions of the world, flood forecasting is one among the few feasible options to manage floods and Ethiopia has no exceptional. Flooding in the country is mainly linked with heavy rainfall and the topography of the highland mountains and lowland plains with river banks system formed by the major river basins such as Baro River. This study presents Real Time Flood Forecasting system using Artificial Neural Network (ANN) and HEC-RAS integrated modeling in Baro River. ANN hydrological flood forecasting model set up using both deterministic and stochastic approach with Rainfall, Temperature and Topographic Wetness Index (TWI) as parametric inputs and trained random neurons weights as stochastic variable. The hydrological model trained and validated using 7 years (1999-2005) and three years (2006-2008) observed stream flow data respectively. And its performance also evaluated with 0.84 and 0.87 NSE values at calibration and validation period respectively. Similarly, for hydraulics modeling, using Normal Difference water Index (NDWI) revealed that both recorded flood events and flood extent area obtained from HEC-RAS are overlapped up to 96% during calibration and validation. The Real time forecasting of flood and its inundation area also evaluated using forecasted daily rainfall and temperature for 3, 7 and 10-days during (May 27, 2019-June 05, 2019) rainy period and these results further compared with the real time condition after 3, 7 and 10 days and showed very good performance. In addition to these, three decades future flood affected areas with different climate change scenarios identified to warn the inhabitants and development investments.



Artificial Neural Networks (ANN), HEC-RAS, Inundation Mapping and Flood, prone Areas, Normal Difference water index, Real-time flood forecasting