Three-dimensional hydrodynamic modeling of permeable and impermeable river training works using CCHE 3D model and laboratory experiments

Kakati, Riddick; Chembolu, Vinay; Dutta, Subashisa

doi:10.1007/978-981-16-9933-7_10

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…On the other hand, with the development of computer science, many software and numerical models have been presented for flood modeling. Among software models, HEC‐RAS (Ghimire et al, 2022; Namara et al, 2022; Sejati et al, 2022), HEC‐HMS (Altaf & Romshoo, 2022; Nadeem et al, 2022; Shah & Lone, 2022), MIKEs (Mike11, Mike21, Mike Flood, Mike Urban) (Dikici et al, 2022; Goumas et al, 2022; Li et al, 2022; Zhang et al, 2022; Zhou, Teng, et al, 2022; Zhou, Zheng, et al, 2022), Bentley Open Flow (de Andrade et al, 2022; Ellwood et al, 2022; Khakhar et al, 2022), Flo‐2D (Gerundo et al, 2022; Komolafe, 2022; Liu et al, 2022; Wang, Luo, et al, 2022), and CCHE (Lee et al, 2020; Kakati et al, 2022; Poorzaman et al, 2022) models can be mentioned. Also, the most applied numerical models include artificial neural network (Ahmad et al, 2022; Dahri et al, 2022; Wang, Yang, et al, 2022), random forest (Abedi et al, 2022; El‐Magd & Ahmed, 2022; Ha & Kang, 2022; Zhu & Zhang, 2022), and analytic hierarchy process (Bouamrane et al, 2022; Roy et al, 2023; Souissi et al, 2022; Vilasan & Kapse, 2022).…”

Section: Introductionmentioning

confidence: 99%

Using geomorphologic indicators in preparation for flood zoning and flood risk maps in the Kashafroud basin, Iran

Panahi,

Khodashenas,

Faridhosseini

2024

J Flood Risk Management

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The risk of flooding has become more significant in many parts of the world due to climate change and increased urbanization. Flood has devastating effects on infrastructure, and communities, causing damage to property and loss of life. Simulation of flood extent in a particular area is done by using various mathematical models, hydrologic‐hydraulic models, and datasets. Flood modeling using hydraulic‐hydrological models has many errors due to the lack of hydraulic‐hydrologic data and insufficient statistical period length. This study demonstrates the fact that the geomorphological index (GI) method, which is based on the digital elevation model and requires little hydraulic‐hydrologic data, is an effective method for flood modeling. Flood zoning based on GI was performed within the Kashafroud basin with 25, 100, and 200‐year return periods by using geomorphic flood area (GFA) plugin in QGIS software. The true positive rates were 0.985, 0.989, and 0.992, respectively, which showed the high accuracy of flood zoning based on the GI method. Here proposed method showed that using the GFA plugin offers a good way for the flood risk assessment in a basin with the lack of measured data as an alternative to the hydraulic‐hydrological methods.

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