A systematic framework for dynamic nodal vulnerability assessment of water distribution networks based on multilayer networks

Tornyeviadzi, Hoese Michel; Owusu-Ansah, E.; Hadi, Muhamad Sukri; Seidu, Razak

doi:10.1016/j.ress.2021.108217

Cited by 13 publications

(3 citation statements)

References 40 publications

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“…The urban rainwater pipe network is a complex system characterized by extensive pipelines, significant diameter variations, and substantial flow fluctuations. Taking into account its inherent rainwater discharge properties as well as the economic and social environment during urbanization, an index system is constructed that encompasses external factors, structural factors, and operational factors [48][49][50]. External factors include the impact of geological disasters, human-induced damage, road construction, ground load, and rainfall.…”

Section: Index Selectionmentioning

confidence: 99%

Enhancing the Vulnerability Assessment of Rainwater Pipe Networks: An Advanced Fuzzy Borda Combination Evaluation Approach

2023

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A vulnerability assessment system for rainwater pipe networks, comprising 13 indexes, was developed to facilitate the rational allocation and timely updating of urban storm drainage systems. An enhanced Borda combination evaluation method, which considers both the optimal and worst solutions, was proposed, accompanied by the operation procedure and numerical calculation method. Five stormwater systems in Central China were selected as case studies, and their vulnerability was evaluated and compared using five distinct evaluation methods: the entropy weight method, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the efficacy coefficient method, the fuzzy comprehensive evaluation method, and the improved fuzzy Borda combination evaluation method. The results demonstrated that the correlation coefficients between the fuzzy Borda combination evaluation method and the four individual evaluation methods were equal to or greater than 0.88, indicating strong agreement. Additionally, the compatibility of the combination evaluation method was found to be 0.96. This study holds both theoretical significance and practical value for preventing urban waterlogging and contributes to the development of more resilient urban storm drainage systems.

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Section: Index Selectionmentioning

confidence: 99%

Enhancing the Vulnerability Assessment of Rainwater Pipe Networks: An Advanced Fuzzy Borda Combination Evaluation Approach

2023

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“…The single-path neural network algorithm (SPNN) calculates subnet flow through the following process: First, initializes neurons and obtains parameters through steps ( 1) -( 2). Then, the path flow is calculated through steps ( 3) -( 8), where step (4) initializes the path and path flow; step (5) find a path through the neural network; step (6) update network flow and neuron state; step (7) update the arc flow. Finally, the subnet flow is output through step (9).…”

Section: Single-path Neural Network Algorithmmentioning

confidence: 99%

“…These infrastructures have great significance to economic production and residential life of human beings [5]. Y. Zhang [6] and H. M. Tornyeviadzi [7,8] have respectively studied the water distribution networks damage caused by natural disasters. Y. Shi [9] has evaluated the vulnerability of the distribution network with pipeline failures.…”

Section: Introductionmentioning

confidence: 99%

A available-flow neural network for solving the dynamic groundwater network maximum flow problem

Zhang

Huang

Zhao

2022

Preprint

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The normal operation of infrastructure networks such as groundwater networks maintains people's life and work. Therefore, it is of great significance to estimate the residual flow when these networks are damaged to evaluate their anti-risk ability. This paper abstracts these problems as the damage-network time-varying maximum flow problem (DTMFP), where the arc capacity in the network is set as a time-varying function. Since the water network is subject to electric-driven periodic changes, and the network damage is defined by some arcs or nodes in the network doesn't work at all. Although the existing maximum flow algorithms such as Cai-Sha can solve the time-varying maximum flow problem, the uncertainty of the network topology that suffers from random damage makes it difficult for the existing maximum flow algorithms to solve such problems. The uncertainty of network topology which suffer random damage makes such problems difficult to solve by existing maximum flow algorithms. Therefore, the key to solve DTMFPs is to find the topology of damaged network. In this paper, we propose an available flow neural network (AFNN) algorithm for solving DTMFPs. The idea of the AFNN algorithm is to determine the network topology through the back-information neural networks (BINN) algorithm at first, then obtain the residual maximum flow through the single-path neural networks (SPNN) algorithm. In the BINN algorithm, the departure node is continuously activated for a given time period and sends waves along the network to the destination node. The wave contains arcs which can be activated. The Utah channel impact response wireless sensor network, American north marin water district network and New York road network are used to confirm the effectiveness of the proposed AFNN algorithm.

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RETRACTED ARTICLE: An available-flow neural network for solving the dynamic groundwater network maximum flow problem

2023

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A systematic framework for dynamic nodal vulnerability assessment of water distribution networks based on multilayer networks

Cited by 13 publications

References 40 publications

Enhancing the Vulnerability Assessment of Rainwater Pipe Networks: An Advanced Fuzzy Borda Combination Evaluation Approach

Enhancing the Vulnerability Assessment of Rainwater Pipe Networks: An Advanced Fuzzy Borda Combination Evaluation Approach

A available-flow neural network for solving the dynamic groundwater network maximum flow problem

RETRACTED ARTICLE: An available-flow neural network for solving the dynamic groundwater network maximum flow problem

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