scholarly journals Establishment of a large-diameter pipeline failure risk matrix in water distribution systems in Taiwan

2019 ◽  
Vol 68 (5) ◽  
pp. 358-367
Author(s):  
Kochin Huang ◽  
Paul Chuo ◽  
Kim-loong Lin ◽  
Mengsyu Yu ◽  
Chihpin Huang
Keyword(s):  
High Risk ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Risk Model ◽  
Large Diameter ◽  
Low Risk ◽  
Risk Matrix ◽  
Risk Levels ◽  
Pipeline Failure

Abstract The bursting of large-diameter water pipelines in distribution systems will lead to industrial, economic, and public safety impacts. Therefore, multi-criteria analysis (MCA) was applied in this study, which utilizes a series of quantifiable parameters to establish the mathematical method of a risk model. The risk matrix of a pipeline was defined as the multiple of the probability of pipeline failure and potential consequences of pipeline failure. By combining the GIS (geographic information system), each evaluation unit was assigned to different risk levels. The large-diameter (above 800 mm) pipeline length statistic for various risk evaluation units of Taiwan Water Corporation reveals the length of high risk is 171 km (7.7%), secondary high risk 574.6 km (25.8%), middle risk 714.3 km (32%), low risk 701.5 km (31.5%), and the unranked length is 67.8 km (3.0%). Finally, the detection frequencies were classified as high risk with a term check of every five years, sub-high risk with planned check every five to ten years, medium risk with checking/monitoring if needed, and low risk with quick repair and no need to take measurements and monitoring. Therefore, we can significantly lower the probability of bursting for large-diameter pipelines in the water distribution system.

scholarly journals Application of genetic programming to modeling pipe failures in water distribution systems

2010 ◽  
Vol 13 (3) ◽  
pp. 419-428 ◽  
Author(s):  
Qiang Xu ◽  
Qiuwen Chen ◽  
Weifeng Li
Keyword(s):  
Genetic Programming ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Pipe Failure ◽  
Beijing City ◽  
Failure Data ◽  
Processing Strategies ◽  
Gis Data ◽  
Failure Models

The water loss from a water distribution system is a serious problem for many cities, which incurs enormous economic and social loss. However, the economic and human resource costs to exactly locate the leakage are extraordinarily high. Thus, reliable and robust pipe failure models are demanded to assess a pipe's propensity to fail. Beijing City was selected as the case study area and the pipe failure data for 19 years (1987–2005) were analyzed. Three different kinds of methods were applied to build pipe failure models. First, a statistical model was built, which discovered that the ages of leakage pipes followed the Weibull distribution. Then, two other models were developed using genetic programming (GP) with different data pre-processing strategies. The three models were compared thereafter and the best model was applied to assess the criticality of all the pipe segments of the entire water supply network in Beijing City based on GIS data.

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

scholarly journals A Head Formulation for the Steady-State Analysis of Water Distribution Systems Using an Explicit and Exact Expression of the Colebrook–White Equation

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1163
Author(s):  
Mengning Qiu ◽  
Avi Ostfeld
Keyword(s):  
Steady State ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Solution Method ◽  
Water Distribution System ◽  
Academic Research ◽  
Exact Expression ◽  
Contamination Source

Steady-state demand-driven water distribution system (WDS) solution is the bedrock for much research conducted in the field related to WDSs. WDSs are modeled using the Darcy–Weisbach equation with the Swamee–Jain equation. However, the Swamee–Jain equation approximates the Colebrook–White equation, errors of which are within 1% for ϵ/D∈[10−6,10−2] and Re∈[5000,108]. A formulation is presented for the solution of WDSs using the Colebrook–White equation. The correctness and efficacy of the head formulation have been demonstrated by applying it to six WDSs with the number of pipes ranges from 454 to 157,044 and the number of nodes ranges from 443 to 150,630. The addition of a physically and fundamentally more accurate WDS solution method can improve the quality of the results achieved in both academic research and industrial application, such as contamination source identification, water hammer analysis, WDS network calibration, sensor placement, and least-cost design and operation of WDSs.

scholarly journals Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1247
Author(s):  
Lydia Tsiami ◽  
Christos Makropoulos
Keyword(s):  
Neural Networks ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Attack Detection ◽  
Convolutional Network ◽  
Irreversible Damage ◽  
Physical Attack ◽  
Temporal Graph ◽  
Graph Neural Networks

Prompt detection of cyber–physical attacks (CPAs) on a water distribution system (WDS) is critical to avoid irreversible damage to the network infrastructure and disruption of water services. However, the complex interdependencies of the water network’s components make CPA detection challenging. To better capture the spatiotemporal dimensions of these interdependencies, we represented the WDS as a mathematical graph and approached the problem by utilizing graph neural networks. We presented an online, one-stage, prediction-based algorithm that implements the temporal graph convolutional network and makes use of the Mahalanobis distance. The algorithm exhibited strong detection performance and was capable of localizing the targeted network components for several benchmark attacks. We suggested that an important property of the proposed algorithm was its explainability, which allowed the extraction of useful information about how the model works and as such it is a step towards the creation of trustworthy AI algorithms for water applications. Additional insights into metrics commonly used to rank algorithm performance were also presented and discussed.

scholarly journals Comparison of Searching Behaviour of Three Evolutionary Algorithms Applied to Water Distribution System Design Optimization

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 695 ◽  
Author(s):  
Weiwei Bi ◽  
Yihui Xu ◽  
Hongyu Wang
Keyword(s):  
Evolutionary Algorithms ◽  
Real Time ◽  
Distribution System ◽  
Optimization Design ◽  
Distribution Systems ◽  
Water Distribution ◽  
Optimal Solutions ◽  
Great Ability ◽  
Searching Behaviour ◽  
Run Time

Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs, thereby offering guidance for the selection of the appropriate EAs for practical implementations. Such comparisons are mainly based on the final solution statistics and, hence, are unable to provide knowledge on how different EAs reach the final optimal solutions and why different EAs performed differently in identifying optimal solutions. To this end, this paper aims to compare the real-time searching behaviour of three widely used EAs, which are genetic algorithms (GAs), the differential evolution (DE) algorithm and the ant colony optimization (ACO). These three EAs are applied to five WDS benchmarking case studies with different scales and complexities, and a set of five metrics are used to measure their run-time searching quality and convergence properties. Results show that the run-time metrics can effectively reveal the underlying searching mechanisms associated with each EA, which significantly goes beyond the knowledge from the traditional end-of-run solution statistics. It is observed that the DE is able to identify better solutions if moderate and large computational budgets are allowed due to its great ability in maintaining the balance between the exploration and exploitation. However, if the computational resources are rather limited or the decision has to be made in a very short time (e.g., real-time WDS operation), the GA can be a good choice as it can always identify better solutions than the DE and ACO at the early searching stages. Based on the results, the ACO performs the worst for the five case study considered. The outcome of this study is the offer of guidance for the algorithm selection based on the available computation resources, as well as knowledge into the EA’s underlying searching behaviours.

scholarly journals Modelling both the continual erosion and regeneration of discolouration material in drinking water distribution systems

2013 ◽  
Vol 14 (1) ◽  
pp. 81-90 ◽  
Author(s):  
W. R. Furnass ◽  
R. P. Collins ◽  
P. S. Husband ◽  
R. L. Sharpe ◽  
S. R. Mounce ◽  
...  
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Transport Model ◽  
Water Distribution Systems ◽  
Sensitivity Analyses ◽  
Drinking Water Distribution Systems ◽  
Proactive Management ◽  
Drinking Water Distribution ◽  
Mass Transport Model

The erosion of the cohesive layers of particulate matter that causes discolouration in water distribution system mains has previously been modelled using the Prediction of Discolouration in Distribution Systems (PODDS) model. When first proposed, PODDS featured an unvalidated means by which material regeneration on pipe walls could be simulated. Field and laboratory studies of material regeneration have yielded data that suggest that the PODDS formulations incorrectly model these processes. A new model is proposed to overcome this shortcoming. It tracks the relative amount of discolouration material that is bound to the pipe wall over time at each of a number of shear strengths. The model formulations and a mass transport model have been encoded as software, which has been used to verify the model's constructs and undertake sensitivity analyses. The new formulations for regeneration are conceptually consistent with field and laboratory observed data and have potential value in the proactive management of water distribution systems, such as evaluating change in discolouration risk and planning timely interventions.

scholarly journals Infiltration and sealing for managing non-cavitated proximal lesions: a systematic review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanyuan Chen ◽  
Dongru Chen ◽  
Huancai Lin
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Meta Analysis ◽  
Low Risk ◽  
Caries Risk ◽  
Moderate Risk ◽  
Risk Levels ◽  
Non Invasive ◽  
Randomised Controlled ◽  
Caries Lesions

Abstract Background Infiltration and sealing are micro-invasive treatments for arresting proximal non-cavitated caries lesions; however, their efficacies under different conditions remain unknown. This systematic review and meta-analysis aimed to evaluate the caries-arresting effectiveness of infiltration and sealing and to further analyse their efficacies across different dentition types and caries risk levels. Methods Six electronic databases were searched for published literature, and references were manually searched. Split-mouth randomised controlled trials (RCTs) to compare the effectiveness between infiltration/sealing and non-invasive treatments in proximal lesions were included. The primary outcome was obtained from radiographical readings. Results In total, 1033 citations were identified, and 17 RCTs (22 articles) were included. Infiltration and sealing reduced the odds of lesion progression (infiltration vs. non-invasive: OR = 0.21, 95% CI 0.15–0.30; sealing vs. placebo: OR = 0.27, 95% CI 0.18–0.42). For both the primary and permanent dentitions, infiltration and sealing were more effective than non-invasive treatments (primary dentition: OR = 0.30, 95% CI 0.20–0.45; permanent dentition: OR = 0.20, 95% CI 0.14–0.28). The overall effects of infiltration and sealing were significantly different from the control effects based on different caries risk levels (OR = 0.20, 95% CI 0.14–0.28). Except for caries risk at moderate levels (moderate risk: OR = 0.32, 95% CI 0.01–8.27), there were significant differences between micro-invasive and non-invasive treatments (low risk: OR = 0.24, 95% CI 0.08–0.72; low to moderate risk: OR = 0.38, 95% CI 0.18–0.81; moderate to high risk: OR = 0.17, 95% CI 0.10–0.29; and high risk: OR = 0.14, 95% CI 0.07–0.28). Except for caries risk at moderate levels (moderate risk: OR = 0.32, 95% CI 0.01–8.27), infiltration was superior (low risk: OR = 0.24, 95% CI 0.08–0.72; low to moderate risk: OR = 0.38, 95% CI 0.18–0.81; moderate to high risk: OR = 0.20, 95% CI 0.10–0.39; and high risk: OR = 0.14, 95% CI 0.05–0.37). Conclusion Infiltration and sealing were more efficacious than non-invasive treatments for halting non-cavitated proximal lesions.

Dynamic updating of post-earthquake damage and functional restoration forecasts of water distribution systems using Bayesian inferencing

2021 ◽  
pp. 875529302110380
Author(s):  
Agam Tomar ◽  
Henry V Burton ◽  
Ali Mosleh
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Epistemic Uncertainty ◽  
Discrete Event ◽  
Complete Recovery ◽  
Current Data ◽  
Functional Restoration ◽  
Mitigation Measures ◽  
Total Recovery

A framework for dynamically updating post-earthquake functional recovery forecasts is presented to reduce the epistemic uncertainty in the predictive model. A Bayesian Network (BN) model is used to provide estimates of the total recovery time, and a process-based discrete event simulation (PBDES) model generates forecasts of the complete recovery trajectory. Both models rely on component damage and duration-based input parameters that are dynamically updated using Bayes’ theorem, as information becomes available throughout the recovery process. The effectiveness of the proposed framework is demonstrated through an application to the pipe network of the City of Napa water distribution system. More specifically, pipe damage and repair data from the 2014 earthquake are used as a point of comparison for the dynamic forecasts. It is shown that, over time, the mean value of the total recovery duration generated by the BN-based model converges to the observed value and the dispersion is reduced. Also, despite a crude initial estimate, the median trajectory generated by the PBDES model provides a reasonable approximation of the observed recovery within 30 days following the earthquake. The proposed framework can be used by emergency managers to investigate the efficacy of post-event mitigation measures (e.g. crew allocation, resource prioritization) utilizing the most current data and knowledge.

scholarly journals Design of Water Distribution System: Appropriateness of Structured System in Large Irrigation Projects in Nepal

2016 ◽  
Vol 19 ◽  
pp. 25-30
Author(s):  
Basistha Adhakari
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Irrigation System ◽  
Command Area ◽  
Irrigation Project ◽  
Irrigation Projects ◽  
Structured System ◽  
Large Irrigation ◽  
Area Development

Many large irrigation projects in Nepal operate under command area development works that emphasize on-farm water distribution and management. These projects have specific design characteristics that were planned to comply with available water resources, climatic conditions, soil type, and water distribution technology. The water distribution technologies differ based on the design needs of each individual project and the design preferences of various foreign consulting firms. This article focuses on the issues of planning and designing water distribution systems of large irrigation systems at the irrigation service delivery level. The layout planning of an irrigation system is an important aspect of design for water distribution, typically guided by hierarchical system. This article also highlights the existing canal hierarchy of these systems and their appropriateness for efficient water distribution. Furthermore, the appropriateness of the structured system is also examined in the Sunsari Morang Irrigation Project. The article concluded with some suggestions for planning and designing command area development works of forthcoming large irrigation projects such as the Sikta Irrigation Project, the Babai Irrigation Project, and the Mahakali Irrigation Project Stage-III.HYDRO Nepal JournalJournal of Water, Energy and EnvironmentIssue: 19Page: 25-30

scholarly journals Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure

2021 ◽  
Author(s):  
Faisal Rahman ◽  
Noam Finkelstein ◽  
Anton Alyakin ◽  
Nisha Gilotra ◽  
Jeff Trost ◽  
...  
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Cardiogenic Shock ◽  
Risk Model ◽  
Acute Decompensated Heart Failure ◽  
Clinical Care ◽  
Delayed Diagnosis ◽  
Low Risk ◽  
Cohort Identification ◽  
High Risk Cohort

Abstract Objective: Despite technological and treatment advancements over the past two decades, cardiogenic shock (CS) mortality has remained between 40-60%. A number of factors can lead to delayed diagnosis of CS, including gradual onset and nonspecific symptoms. Our objective was to develop an algorithm that can continuously monitor heart failure patients, and partition them into cohorts of high- and low-risk for CS.Methods: We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Healthcare system. Our cohort identification approach is based on logistic regression, and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care. Results: Our algorithm identified patients at high-risk of CS. Patients in the high-risk cohort had 10.2 times (95% confidence interval 6.1-17.2) higher prevalence of CS than those in the low-risk cohort. Patients who experienced cardiogenic shock while in the high-risk cohort were first deemed high-risk a median of 1.7 days (interquartile range 0.8 to 4.6) before cardiogenic shock diagnosis was made by their clinical team. Conclusions: This risk model was able to predict patients at higher risk of CS in a time frame that allowed a change in clinical care. Future studies need to evaluate if CS analysis of high-risk cohort identification may affect outcomes.

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