A parallel method for extracting the flow path based on CUDA

Wu, Qianjiao; Chen, Yumin; Yang, Jiaxin; Zhang, Jingyi; Ba, Qianqian; Zhang, Xinyi; Chen, Zhongchao

doi:10.1109/geoinformatics.2017.8090908

Cited by 1 publication

(4 citation statements)

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“…Second, the compound method (Zhou and Chen, 2011) is used to generate the drainage-constrained TIN by combining the retrieved critical points and drainage networks. In addition, to avoid narrow triangular facets over the TIN, the distances between the critical points and between the critical points and drainage networks are expected to be greater than the DEM resolution (Wu et al, 2020(Wu et al, , 2021 because the long-narrow triangular facet is averse to determine the flow direction depending on the aspect and slope and still increases the volume calculation during the generation of the FPN. Third, the resampling method was used to obtain the flow source points from the no-depression DEM.…”

Section: Generating a Flow Path Network (Fpn)mentioning

confidence: 99%

“…algorithm presented by Wu et al (2021), is employed to obtain the slope length factor from a no-depression DEM. The high accuracy of the flow-path-network-flow-path-curvature (FPN_C) algorithm proposed by (Wu et al, 2020), is utilized to obtain the flow path curvature from a no-depression DEM.…”

Section: Improvingmentioning

confidence: 99%

“…Figure 2: Simulating the surface flow dynamics based on a one-dimensional flow path network (FPN).Therefore, the two functions are converted into the velocity estimation kernel function and flow discharge count kernel function based on CUDA to enhance the computing efficiency. The whole parallelization process is similar to that of the parallel algorithm proposed byWu and Chen (2020) and mainly includes data transmission, thread partition mode in CUDA and kernel function implementation. The concrete parallel processes were described in detail byWu and Chen (2020).…”

mentioning

confidence: 99%

“…The whole parallelization process is similar to that of the parallel algorithm proposed byWu and Chen (2020) and mainly includes data transmission, thread partition mode in CUDA and kernel function implementation. The concrete parallel processes were described in detail byWu and Chen (2020).…”

mentioning

confidence: 99%

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An Improved Algorithm for Simulating the Surface Flow Dynamics based on the Flow-Path Network Model

Chen

Xie

et al. 2022

Preprint

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Abstract. This paper proposes an improved algorithm for simulating the surface flow dynamics based on the flow-path network model. This algorithm utilizes the parallel-multi-point method to extract the critical points and the D8 algorithm to retrieve the drainage networks from the regular-grid digital elevation model (DEM) for constructing a drainage-constrained triangulated irregular network (TIN). Then, it combines the flow directions of triangular facets over TIN with resampled flow source points to track flow lines to generate the flow path network (FPN) based on the flow-path network model. On this basis, the proposed algorithm employs three terrain parameters (slope length factor, topographic wetness index and flow path curvature) to improve the classical Manning equation based on the analytic hierarchy process (AHP) to enhance the accuracy of the flow velocity calculation. The topographic wetness index and flow path curvature are derived by the flow-path-network-triangular-facet-network (FPN_TFN) algorithm, a new flow-path-network-topographic-wetness-index (FPN_TWI) algorithm and the flow-path-network-flow-path-curvature (FPN_C) algorithm, respectively. Finally, the velocity estimation function and surface flow discharge simulation function are parallelized by the Compute Unified Device Architecture (CUDA) to enhance its computational efficiency. The outcomes are compared with the algorithm before improvement (TIN_based algorithm) and the SWAT model. The results demonstrate that the speedup ratio reaches 15.7 compared to the TIN_based algorithm. The Nash coefficient increases by 6.49 %, the correlation coefficient decreases slightly, and the balance coefficient increases by 19.08 %. Compared with the SWAT model, the Nash coefficient and correlation coefficient increase by 97.56 % and 4.60 %, respectively. The balance coefficient is close to 1 and outperforms the compared algorithms.

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Section: Generating a Flow Path Network (Fpn)mentioning

confidence: 99%

Section: Improvingmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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