Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

Abstract

This study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.

A coordinated drainage and regulation model of urban water systems in China: A case study in Fuzhou city

“…2D hydrodynamic modeling is often used to simulate the evolution of the vertical and horizontal flow velocity distribution in areas such as urban surfaces, rivers, and lakes. Mainstream simulation software for urban flood and waterlogging disasters includes SWMM (Koc et al, 2021; Paule‐Mercado et al, 2017; Rai et al, 2018; Sun et al, 2022; Y. Yang et al, 2021; Zeng et al, 2021), HEC‐RAS (Bush et al, 2022; Shustikova et al, 2019; Tamiru & Wagari, 2022; Vozinaki et al, 2017), LISFLOOD‐FP (Z. Liu & Merwade, 2018; Wilson et al, 2007; X. Wu, Wang, et al, 2018), InfoWorks ICM (Chen et al, 2022; Hosseinzadehtalaei et al, 2021; Peng et al, 2016; Z. Zhu et al, 2016), MIKE (Bisht et al, 2016; C. Q. Li & Wang, 2012; H. L. Liu et al, 2007; M. M. Rahman et al, 2011), and FLO‐2D (Erena et al, 2018; Hsu et al, 2010; M. H. Li et al, 2011). In addition, variations in the flow fields of some key areas, including estuaries, coasts, water pumps, and sluices, are also simulated with 3D hydrodynamic modeling, while the anisotropy of fluids is fully considered.…”

“…Mainstream simulation software for urban flood and waterlogging disasters includes SWMM (Koc et al, 2021;Paule-Mercado et al, 2017;Rai et al, 2018;Sun et al, 2022;Y. Yang et al, 2021;Zeng et al, 2021), HEC-RAS (Bush et al, 2022;Shustikova et al, 2019;Tamiru & Wagari, 2022;Vozinaki et al, 2017), LISFLOOD-FP (Z. Liu & Merwade, 2018;Wilson et al, 2007;X.…”

A combined hydrological and hydraulic model for flood prediction in Puah river subsystem area, Palembang city

“…By integrating the artificial neural network (ANN) model as a hydrological model and HEC-RAS as a hydraulic model, Tamiru et al [10] (2021) mapped flood inundation in Ethiopia's Baro watershed. Rainfall and daily discharge data for seven years (1999)(2000)(2001)(2002)(2003)(2004)(2005) were used to create a hydrological model on a grid map (30 x 30) m topographical wetness index (TWI).…”

A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir

“…The hydrological hybrid model was created to combine the advantages of both types of models. Many authors have investigated the interaction of data-driven models with physically based models such as SWMM (Kim & Han, 2020), SWAT (Yuan & Forshay, 2021), and HEC-RAS (Tamiru & Wagari, 2021) to forecast floods, and urban flooding and shown the superiority of the hybrid model. However, because the application of ANN to hydrological problems in Vietnam is still in its early stages, the tailored study is required to demonstrate both the effectiveness and limitations that may be encountered when using it for flood forecasting decision support on reservoir operation.…”