A hybrid machine learning-based multi-DEM ensemble model of river cross-section extraction: Implications on streamflow routing

Biswal, Sabinaya; Sahoo, Bhabagrahi; Jha, Madan K.; Bhuyan, Mahendra K.

doi:10.1016/j.jhydrol.2023.129951

Cited by 7 publications

(2 citation statements)

References 88 publications

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“…This model is wellsuited for simulating the dynamic changes resulting from various flood control projects, such as gates, pumps, and culverts, in urbanized areas. It is commonly employed in tasks related to flood forecasting, urban flood risk management, and the optimal scheduling of water projects [40][41][42]. For instance, Hu et al [11] employed the MIKE 1D model to investigate flood responses to extreme scenarios and assess future flood risks in Suzhou City under the backdrop of climate change.…”

Section: Methods Designmentioning

confidence: 99%

Coordination of Flood Control under Urbanization on the Taihu Plain: Basin, City and Region Perspectives

Lu,

Kang,

et al. 2023

Water

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Floods have become increasingly frequent and pose more significant risks in delta plains due to rapid urbanization. While previous studies have primarily focused on urban flood management, there has been a limited exploration of coordinated flood control strategies that encompass cities, regions, and basins holistically. In response to this gap, our research aims to quantitatively assess flood control coordination under various scheduling rules and propose optimized strategies to enhance this coordination. Using the Wu-Cheng-Xi-Yu region as our case study, we observed that flood characteristics under different flood control coordination schemes varied slightly, especially the FI. Moreover, the effectiveness of different scheduling optimization schemes varied across different levels. Specifically, at the basin–region, basin–city, and region–city levels, Schemes S8, S7, and S5 demonstrated the highest coordination coefficients, with values of 0.80, 0.67, and 0.68, respectively. In comparison to the current scheduling Scheme (S0), these schemes resulted in significant improvements in flood coordination, with enhancements of 0.68, 0.37, and 0.22, respectively. Furthermore, our findings indicate that the most effective approach to strengthening flood control coordination involves implementing scheduling measures that reduce urban drainage while simultaneously improving the discharge capacity of the basin. Our results could help to alleviate the contradictions of flood control at different levels and provide a solid guarantee for water security.

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Section: Methods Designmentioning

confidence: 99%

Coordination of Flood Control under Urbanization on the Taihu Plain: Basin, City and Region Perspectives

Lu,

Kang,

et al. 2023

Water

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“…Azizian and Brocca [32] performed a comprehensive evaluation of remotely sensed DEMs for flood inundation mapping including the recently available Advanced Land Observing Satellite (ALOS) DEM. Biswal et al [33] suggested a multi-DEM approach using machine learning techniques to demarcate cross-sections adopting the medium resolution DEMs such as shuttle radar topography mission (SRTM) and ASTER. Petikas, Keramaris and Kanakoudis [28] proposed a novel method to automatically extract river cross-sections from a DEM along with a parametric cross-section extraction algorithm.…”

Section: Introductionmentioning

confidence: 99%

KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X

Lee,

Hwang,

Singh

2024

Remote Sensing

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Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from the motion technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps and lined shapes. Therefore, a nonparametric estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with synthetically simulated trapezoidal, U-shape and V-shape channels. In addition, the proposed KLR model was compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). The experimental study was performed with the river experiment center in Andong, South Korea. Furthermore, the KLR model was applied to two real case studies in the Migok-cheon stream on Hapcheon-gun and Pori-cheon stream on Yecheon-gun and compared to the other models. With the extensive applications to the feasible river channels, the results indicated that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps as well as different shapes. Finally, the limitation of the UAV-driven demarcation approach was also discussed due to the penetrability of RGB sensors to water.

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Enhancing flood forecasting accuracy in Data-Scarce regions through advanced modeling approaches

Okacha,

Salhi,

Bouchouou

et al. 2024

Journal of Hydrology

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A hybrid machine learning-based multi-DEM ensemble model of river cross-section extraction: Implications on streamflow routing

Cited by 7 publications

References 88 publications

Coordination of Flood Control under Urbanization on the Taihu Plain: Basin, City and Region Perspectives

Coordination of Flood Control under Urbanization on the Taihu Plain: Basin, City and Region Perspectives

KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X

Enhancing flood forecasting accuracy in Data-Scarce regions through advanced modeling approaches

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