Identifying Flow Patterns in Water Pipelines Using Complex Network Theory

Pei, Shengwei; Liu, Haixing; Zhu, Yanhe; Zhang, Chao; Zhao, Mengke; Fu, Guangtao; Yang, Kun; Yuan, Yixing; Zhang, Chi

doi:10.1061/(asce)hy.1943-7900.0001882

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…The SWAT-WARM model [29] is the coupling of the SWAT [8] model and the resource allocation model. It divides the calculated units (CUs) based on the hydrolo…”

Section: Description Of the Swat-warm Modelmentioning

confidence: 99%

“…The SWAT-WARM model [29] is the coupling of the SWAT [8] model and the water resource allocation model. It divides the calculated units (CUs) based on the hydrological response units and water allocation units (Figure 2) and realizes the bidirectional data transmission of the two models, forming a complete natural-artificial water resource simulation system.…”

Section: Description Of the Swat-warm Modelmentioning

confidence: 99%

“…The water resources development and utilization of the previous period's activities directly affects the next period and the spatial distribution of water resources [3,7]. The high-intensity development of water resources development and utilization has enhanced the role of artificial collateral water circulation and created a series of water resource problems [8,9]. Thus, the water cycle and response mechanism under the disturbance of human activities must be fully considered to conduct reasonable water resources management.…”

Section: Introductionmentioning

confidence: 99%

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Modified Multi–Source Water Supply Module of the SWAT–WARM Model to Simulate Water Resource Responses under Strong Human Activities in the Tang–Bai River Basin

Yang

Yin

et al. 2022

Sustainability

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In the past few decades, the water resources in the Tang–Bai River Basin showed a declining trend, due to the human–driven alteration of surface water and groundwater management. There are potential risks to the sustainable utilization of future water resources in response to agricultural, industrial, and domestic water supply. In this work, we used the water allocation and regulation model based on SWAT (SWAT–WARM model) to quantify the characteristics of water resources response under human activities in this basin. The multi–source water supply module was modified to improve the applicability of the SWAT–WARM model in this basin. We validated our simulations against observed runoff, water consumption, and supply. The main results were as follows: (a) We used the percent bias, the correlation coefficient, and the Nash–Sutcliffe efficiency coefficient to measure the model validity and found that the modified model did not show obvious advantages in runoff simulations, whereas it reproduced water consumption and supply better than the original model. The modified model had more advantages in reflecting the process of water resources transformation and utilization in the basin driven by strong human activities. (b) By comparing the variation of watershed water circulation fluxes under natural and human disturbance conditions in the Tang–Bai River Basin from 1995 to 2016, we found that human activities increased evapotranspiration by 6.8% and surface runoff increased by 10.0%, while groundwater resources decreased by 0.23 million m3/yr. (c) There was water shortage in the basin at different flow frequencies, among which agricultural water shortage accounted for the largest proportion, >70%. The basin should further strengthen agricultural and industrial water saving, reduce water consumption fundamentally, and ensure the sustainable development of economy and society.

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“…The SWAT-WARM model [29] is the coupling of the SWAT [8] model and the resource allocation model. It divides the calculated units (CUs) based on the hydrolo…”

Section: Description Of the Swat-warm Modelmentioning

confidence: 99%

Section: Description Of the Swat-warm Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modified Multi–Source Water Supply Module of the SWAT–WARM Model to Simulate Water Resource Responses under Strong Human Activities in the Tang–Bai River Basin

Yang

Yin

et al. 2022

Sustainability

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Shallow Learning vs. Deep Learning in Engineering Applications

Jafari,

Moradi,

Shafiee

2024

The Springer Series in Applied Machine Learning

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Factors and Interactions that Influence the Pressure Drop Across An Air Volume Reducing Device on Low-Pressure Water Distribution Networks

Carpintero

Canales

Fábregas

et al. 2021

Iran J Sci Technol Trans Civ Eng

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Efficient water supply systems are necessary for the development and sustainability of human societies. One relevant aspect of these systems is the metering function, recorded employing water meters, which determines the charges levied to the clients and estimates the water losses in the network. Inaccurate measurements are detrimental for both the client and the supplier. For allowing more precise metering, one option is to use an air volume reducing device, an accessory similar to a check valve that minimizes the air volume entrapped in the pipelines, thus improving metering accuracy. This research used an experimental design to determine the influence of four factors and their interactions on the pressure drop across these devices as a preliminary step for allowing their extended use on low-pressure water supply systems. The results showed that the diameter, the spring stiffness, and the flow rate are significant factors in the pressure drop. The shape of the valve stem is statistically significant only when interacting with other factors.

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Identifying Flow Patterns in Water Pipelines Using Complex Network Theory

Cited by 5 publications

References 48 publications

Modified Multi–Source Water Supply Module of the SWAT–WARM Model to Simulate Water Resource Responses under Strong Human Activities in the Tang–Bai River Basin

Modified Multi–Source Water Supply Module of the SWAT–WARM Model to Simulate Water Resource Responses under Strong Human Activities in the Tang–Bai River Basin

Shallow Learning vs. Deep Learning in Engineering Applications

Factors and Interactions that Influence the Pressure Drop Across An Air Volume Reducing Device on Low-Pressure Water Distribution Networks

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