The Research and Practice of Water Quality Safety Evaluation for Ji Nan urban Water Supply System

Li, Hongwei; Lv, Mou; Song, Yuchen

doi:10.1016/j.proenv.2011.12.180

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…RDWS has been the focus of numerous research studies in China. Some of this work focused on RDWS monitoring and evaluation and the use of assorted qualitative and quantitative methods at provincial-or county-level scales [10][11][12][13]. Other RDWS research often focused on the impacts of water (drinking water especially) on sustainable socioeconomic development goals [14], or water supply, equity, and basic needs (often with a focus on northern areas of China where water resources are particularly scarce) [15].…”

Section: Introductionmentioning

confidence: 99%

The Impacts of Socioeconomic Development on Rural Drinking Water Safety in China: A Provincial-Level Comparative Analysis

Cohen

et al. 2018

Sustainability

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In China, achieving rural drinking water safety—meaning access to a safe, affordable, sufficient, and sustainable drinking water supply—remains a key challenge for government agencies and researchers. Using cross-sectional data at the provincial level, in this paper we examine the impacts of socioeconomic development on drinking water safety in rural China. Using a theoretical framework called Pressure-State-Response (PSR), existing data were organized into state and pressure indicators. Canonical Correlation Analysis was then used to analyze provincial-level relationships between the indicators. Significant drinking-water-safety-related differences were found across provinces. Our analyses suggest that, overall, China’s recent and rapid socioeconomic development yielded substantial benefits for China’s rural drinking water safety. However, this same development also negatively impacted rural drinking water safety via increased groundwater over-abstraction, reductions in water supply, and environmental contamination. The paper closes with a discussion of implications and options for improving drinking water policy, management, and regulation in rural China.

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Section: Introductionmentioning

confidence: 99%

The Impacts of Socioeconomic Development on Rural Drinking Water Safety in China: A Provincial-Level Comparative Analysis

Cohen

et al. 2018

Sustainability

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“…The water distribution system (WDS) plays a pivotal role in the municipal facilities in modern cities. Acute leakage incidents in the WDS usually cause serious social panic and economic losses [1,2]. A key issue in WDS maintenance is to detect and make up the leakage in a timely and accurate manner [3].…”

Section: Introductionmentioning

confidence: 99%

A Tree-Based Machine Learning Method for Pipeline Leakage Detection

Shen

Cheng

2022

Water

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Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak detection operations in water distribution systems (WDSs). However, studies on leakage detection based on on-site leak signals are limited compared to studies on lab-scale leak detection. The on-site leak signals have stronger interference and randomness, while leak signals in the laboratory are relatively simpler. To better assist on-site leak detection operations, the present paper develops and compares three ML-based models. For this purpose, many on-site tests were carried out, and tens of thousands of sets of on-site leak detection signals were collected. More than 6000 sets of these signals were marked and the signal features were extracted and analyzed from a statistical point of view. It was found that features such as the main frequency, the spectral roll-off rate, the spectral flatness, and one-dimensional (1-D) Mel Frequency Cepstrum Coefficient (MFCC) could well distinguish the leakage signals from non-leakage signals. After training the decision tree model, the performances of the random forest and Adaboost models were thoroughly compared. It was found that the false positive rates of the three models were 9.80%, 8.27% and 7.35%, all lower than 10%. In particular, the Adaboost model had the lowest false positive rate of 7.35%. The recall rate of the random forest and Adaboost models were 100% and 99.52%.

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Research of development strategy on China’s rural drinking water supply based on SWOT–TOPSIS method combined with AHP-Entropy: a case in Hebei Province

Feng

Yang

2015

Environ Earth Sci

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The Research and Practice of Water Quality Safety Evaluation for Ji Nan urban Water Supply System

Cited by 7 publications

References 3 publications

The Impacts of Socioeconomic Development on Rural Drinking Water Safety in China: A Provincial-Level Comparative Analysis

The Impacts of Socioeconomic Development on Rural Drinking Water Safety in China: A Provincial-Level Comparative Analysis

A Tree-Based Machine Learning Method for Pipeline Leakage Detection

Research of development strategy on China’s rural drinking water supply based on SWOT–TOPSIS method combined with AHP-Entropy: a case in Hebei Province

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