Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

Xiong, Liyang; Jiang, Ruqiao; Lu, Qinghui; Wang, Jinhu; Li, Fayuan; Tang, Guoan

doi:10.1111/tgis.12516

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…The data preprocessing work mainly includes the establishment of digital elevation model, filling of digital elevation model, hydrological space analysis, and division of drainage area. The digital elevation model (DEM) is built using the discrete elevation points of the airport construction area, and the improved PF algorithm [19] is used to level the depression in the Surface runoff always flows from high to low ground and finally flows to a water outlet. Therefore, to scientifically and accurately identify the watershed boundary, the flow direction of water flow in each grid unit must be determined first.…”

Section: Data Preprocessingmentioning

confidence: 99%

A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model

et al. 2020

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To address the problems of high overflow rate of pipe network inspection well and low drainage efficiency, a rainwater control optimization design approach based on a self-organizing feature map neural network model (SOFM) was proposed in this paper. These problems are caused by low precision parameter design in various rainwater control measures such as the diameter of the rainwater pipe network and the green roof area ratio. This system is to be combined with the newly built rainwater pipe control optimization design project of China International Airport in Daxing District of Beijing, China. Through the optimization adjustment of the pipe network parameters such as the diameter of the rainwater pipe network, the slope of the pipeline, and the green infrastructure (GI) parameters such as the sinking green area and the green roof area, reasonable control of airport rainfall and the construction of sustainable drainage systems can be achieved. This research indicates that compared with the result of the drainage design under the initial value of the parameter, the green roof model and the conceptual model of the mesoscale sustainable drainage system, in the case of a hundred-year torrential rainstorm, the overflow rate of pipe network inspection wells has reduced by 36% to 67.5%, the efficiency of drainage has increased by 26.3% to 61.7%, which achieves the requirements for reasonable control of airport rainwater and building a sponge airport and a sustainable drainage system.

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Section: Data Preprocessingmentioning

confidence: 99%

A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model

et al. 2020

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“…The D‐infinity algorithm, as a widely used traditional flow direction algorithm, uses the maximum downslope of a 3 × 3 window as the flow direction value; however, this algorithm performed poorly in flat areas (Tarboton, 1997). Flat areas, as a unique object in digital elevation models (DEMs), make flow direction calculation more challenging (Xiong et al., 2019, 2023; H. Zhang et al., 2017). Moreover, flat areas in DEMs can be divided into two types: flat areas representing geographical objects (e.g., lakes) and flat areas caused by data production errors.…”

Section: Introductionmentioning

confidence: 99%

“…Flat areas, as a unique object in digital elevation models (DEMs), make flow direction calculation more challenging (Xiong et al, 2019(Xiong et al, , 2023H. Zhang et al, 2017).…”

mentioning

confidence: 99%

Hydrological Object‐Based Flow Direction Model for Constructing a Lake‐Stream Topological System

Xiong

Zhao

Zhang

et al. 2023

Water Resources Research

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Worldwide, streams and lakes are important resources for human survival and development (Giuliani et al., 2022;Sauer et al., 2021;Vörösmarty et al., 2010). Topological systems can reflect the connectivity and flow rules between streams and lakes, which is the basis for simulating the process of material and energy transport in stream network systems (including streams and lakes) (Dai et al., 2021;Verdin, & Verdin, 1999). In some multilake areas, especially inland plateaus, lakes play an important role in stream network systems; therefore, constructing the topological systems between streams and lakes is of great interest to hydrologists, climate scientists and geomorphologists.Flow direction is the key factor in constructing a lake-stream topological system, as flow direction provides natural routing information for streams and lakes, allowing for the organisation of stream networks based on certain physical rules. Accurate flow direction is the basis for many geoscience studies, such as hydrological modeling, Earth's surface process simulation, and terrain classification (C. Z. Qin et al., 2011). Although geoscience studies relating to stream networks have greatly contributed to promoting the development of geography, the basic flow direction calculation is still not realistic in many situations (Gutierrez & Abad, 2014;Whipple & Viers, 2019). This problem is due to the lack of objects during the process of flow direction calculation. However, the Earth's surface is a complex system (S. Li et al., 2020;. There is a significant difference in the flow process of stream networks under different underlying surface conditions. Regardless of the complexity of relevant geoscience studies, object-oriented flow direction algorithms can improve the reliability of flow direction calculations.The D-infinity algorithm, as a widely used traditional flow direction algorithm, uses the maximum downslope of a 3 × 3 window as the flow direction value; however, this algorithm performed poorly in flat areas (Tarboton, 1997).

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“…Accordingly, the simulation and analysis of water flow processes have received widespread attention in terms of theoretical research and practical applications [4][5][6][7]. The automatic extraction of the flow direction from the digital elevation model (DEM) is one of the most critical methods for simulating water flow, which relies heavily on flow direction algorithms [8][9][10]. Flow direction algorithms define the direction of runoff and the flow distribution at each point on the ground, which directly determine the path of the sediment transport process and the amount of material transferred [11].…”

Section: Introductionmentioning

confidence: 99%

An Improved Flow Direction Algorithm That Considers Mass Conservation for Sediment Transport Simulations

Wei,

Dai,

Wang

et al. 2023

Water

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The sediment transport process in watersheds is an important research component of geomorphology and surface dynamics. Previous work has inferred the spatial distribution of the sediment transport rate (STR) by the flow direction algorithm and measured topographic variation; however, the simple application of the flow direction algorithm contributes to mass non-conservation during a simulation. This study designs an improved flow direction algorithm for a sediment transport process simulation by judging the mass conservation situation in the simulation process. The specific implementation is to evaluate the existence of negative values for the STR; if they exist, the negative values of the STR are reset to stop the propagation of the negative values downstream. Experiments are conducted to improve the classical D8, MFD–se, and MFD–md flow algorithms in this paper, and the experimental results show that the method in this paper can effectively improve the simulation effect of STR. The STR simulations of the three models, D8, MFD–se, and MFD–md, improved by 1.26%, 4.17%, and 4.54%, respectively. Moreover, the MFD–se model is more suitable for the simulation of the STR when comparing the three models. The improved flow algorithm can be used to simulate the STR, sediment content, and pollutant migration in watersheds, providing a new method for the fine-grained characterization of surface processes in watersheds.

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Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

Cited by 11 publications

References 27 publications

A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model

A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model

Hydrological Object‐Based Flow Direction Model for Constructing a Lake‐Stream Topological System

An Improved Flow Direction Algorithm That Considers Mass Conservation for Sediment Transport Simulations

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