scholarly journals Packaged Drinking Water Analysis by Classification Technique of Data Mining

2019 ◽  
Vol 9 (2S3) ◽  
pp. 352-355
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Drinking Water ◽  
Classification Model ◽  
Quality Level ◽  
Earth Surface ◽  
Safe Water ◽  
Classification Technique ◽  
Harmful Substances ◽  
Drinking Water Analysis

In a world where sufficient and safe water is not available everywhere, either because of harmful substances are found in the layer of earth surface which enter into a water or may be because of some other microorganisms. However what about packaged drinking water is safest is the big question. To analyze this proposed a model which helps to compare few brands of packaged drinking water and checked certain water quality level through customized classification technique of data mining. Proposed customized classification model can predict safe water based on the parameters of water quality which make ease of work of the laboratory technician by predicting which packaged water will be safest to drink

Off-flavor as the consumer's measure of drinking water safety

1995 ◽  
Vol 31 (11) ◽  
pp. 1-8 ◽  
Author(s):  
Michael J. McGuire
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Utilities ◽  
Regulatory Environment ◽  
Safe Drinking Water ◽  
Water Safety ◽  
Safe Water ◽  
Drinking Water Safety ◽  
Off Flavors ◽  
Health Related

If consumers detect an off-flavor in their drinking water, they are likely to believe that it probably is not safe. Water utilities will be defeating their best efforts to provide safe drinking water if they only meet health-related regulations and do not provide water that is free of off-flavor problems. The purpose of this paper is to explore the current U.S. regulatory environment and discuss how these regulations can adversely impact the control of off-flavors in drinking water. Utilities should adopt a water quality goal that allows them to not only meet the minimums of the regulations, but also meet the customer's highest standards - water that is free of off-flavors.

scholarly journals Brief Communication: Use of field test kit for detection of lead in drinking water in Philippines post the disaster typhoon Haiyan

2015 ◽  
Vol 3 (9) ◽  
pp. 5287-5295 ◽  
Author(s):  
K. Y. Liu ◽  
L. M. Cong ◽  
Z. J. Lan ◽  
R. P. Ma ◽  
L. Yu ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Local Government ◽  
Water Supply ◽  
Field Test ◽  
Water Samples ◽  
Safe Water ◽  
Test Kit ◽  
Relief Efforts ◽  
Concentration Levels

Abstract. On 8 November 2013, super typhoon Haiyan made landfall in Philippines. On 24 November, the Chinese hospital ship arrived in Philippines to help with disaster relief efforts. Drinking water was collected at a variety of locations, and the concentration levels of lead were determined with field test kit. The results showed that the levels of lead in 67% of total collected water samples exceeded WHO's standard. Afterwards, the local government had taken many measures to ensure a safe water supply in next few months. This is the first report about water quality in Philippines after the disaster.

Comparing health outcomes and point-of-use water quality in two rural indigenous communities of Baja California, Mexico before and after receiving new potable water infrastructure

2014 ◽  
Vol 4 (4) ◽  
pp. 672-680 ◽  
Author(s):  
Paula Stigler-Granados ◽  
Penelope J. E. Quintana ◽  
Richard Gersberg ◽  
María Luisa Zúñiga ◽  
Thomas Novotny
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Baja California ◽  
Indigenous Communities ◽  
Gastrointestinal Illness ◽  
Source Water ◽  
Water Infrastructure ◽  
Safe Water ◽  
Point Of Use ◽  
Before And After

One of the United Nations Millennium Development Goals is to reduce the global proportion of people who do not have access to safe drinking water. In the past, the typical strategy to reach this goal has been the use of investment-intensive centralized infrastructure development for water supplies. However, there is increasing evidence suggesting that improving water quality at the source does not guarantee safe water at point-of-use. This study examined water quality, waterborne disease incidence and water system use over time in two small rural indigenous communities of Baja California, Mexico, before and after drinking-water infrastructure improvements. Community Promotoras collected data on the incidence of gastrointestinal illness through face-to-face surveys. Concurrently, water samples from the old and new water sources and household water storage containers were analyzed for fecal coliforms. Although source water quality was significantly improved in both communities (p < 0.05), neither community had a significant decrease in the level of contaminated drinking water sampled at the household level. No significant decrease in gastrointestinal illness was found after the improvements to the source water supply. These results indicate that point-of-use contamination and acceptance of the new sources may be a critical point for intervention when attempting to assure access to safe water, especially in rural communities.

Surveillance of Drinking Water Quality for Safe Water Supply—A Case Study from Shillong, India

2011 ◽  
Vol 25 (13) ◽  
pp. 3321-3342 ◽  
Author(s):  
Gajanan Kisan Khadse ◽  
Moromi D. Kalita ◽  
S. N. Pimpalkar ◽  
Pawan K. Labhasetwar
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Supply ◽  
Drinking Water Quality ◽  
Safe Water

scholarly journals Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

2019 ◽  
Vol 34 (1) ◽  
pp. 139-154 ◽  
Author(s):  
Mehrdad Jeihouni ◽  
Ara Toomanian ◽  
Ali Mansourian
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Drinking Water ◽  
Decision Tree ◽  
Rule Induction ◽  
Effective Parameters ◽  
High Quality ◽  
Chi Square ◽  
Distribution Maps ◽  
Data Mining Algorithms

AbstractGroundwater is an important source to supply drinking water demands in both arid and semi-arid regions. Nevertheless, locating high quality drinking water is a major challenge in such areas. Against this background, this study proceeds to utilize and compare five decision tree-based data mining algorithms including Ordinary Decision Tree (ODT), Random Forest (RF), Random Tree (RT), Chi-square Automatic Interaction Detector (CHAID), and Iterative Dichotomiser 3 (ID3) for rule induction in order to identify high quality groundwater zones for drinking purposes. The proposed methodology works by initially extracting key relevant variables affecting water quality (electrical conductivity, pH, hardness and chloride) out of a total of eight existing parameters, and using them as inputs for the rule induction process. The algorithms were evaluated with reference to both continuous and discrete datasets. The findings were speculative of the superiority, performance-wise, of rule induction using the continuous dataset as opposed to the discrete dataset. Based on validation results, in continuous dataset, RF and ODT showed higher and RT showed acceptable performance. The groundwater quality maps were generated by combining the effective parameters distribution maps using inducted rules from RF, ODT, and RT, in GIS environment. A quick glance at the generated maps reveals a drop in the quality of groundwater from south to north as well as from east to west in the study area. The RF showed the highest performance (accuracy of 97.10%) among its counterparts; and so the generated map based on rules inducted from RF is more reliable. The RF and ODT methods are more suitable in the case of continuous dataset and can be applied for rule induction to determine water quality with higher accuracy compared to other tested algorithms.

Data mining model for predicting the quality level and classification of construction projects

2021 ◽  
pp. 1-15
Author(s):  
Ching-Lung Fan
Keyword(s):  
Data Mining ◽  
Construction Management ◽  
Association Rule ◽  
Project Managers ◽  
Classification Model ◽  
Support Vector ◽  
Quality Level ◽  
Data Mining Algorithm ◽  
Construction Quality ◽  
Mining Model

Project managers supervise projects to ensure their smooth completion within a stipulated time frame and budget while guaranteeing construction quality. The relationships of various attributes with quality can be quantified and classified to facilitate such supervision. Therefore, this study used a data mining algorithm to analyze the relationships between defects, quality levels, contract sums, project categories, and progress in 1,015 inspection projects. In the first part, association rule mining (ARM), an unsupervised data mining approach, was used to obtain 11 rules relating two defect types (i.e., quality management system and construction quality) and determine the relationships between the four attributes (i.e., quality level, contract sum, project category, and progress). The resulting association rule may be beneficial for construction management because project managers can use it to determine the correlations between defects and attributes. In the second part, supervised data mining techniques, namely neural network (NN), support vector machine (SVM), and decision tree (C5.0 and QUEST) algorithms, were applied to develop a classification model for quality prediction. The target variable was quality, which was divided into four levels, and the decision variables comprised 499 defects, 3 contract sums, 7 project categories, and 2 progress variables. The results indicated that five defects were important. Finally, the four indicators of gain chart, break-even point (BEP), accuracy, and area under the curve (AUC) were calculated to evaluate the model. For the SVM model, the actual value predicted by the gain chart was 96.04%, the BEP was 0.95, and the AUC was 0.935. The SVM yielded optimal classification efficiency and effectively predicted the quality level. The data mining model developed in this study can serve as a reference for effective construction management.

Water Quality Avoidance Behavior: Bridging the Gap between Perception and Reality

2019 ◽  
Vol 06 (02) ◽  
pp. 1950012 ◽  
Author(s):  
Samrat B. Kunwar ◽  
Alok K. Bohara
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Treatment ◽  
Risk Perception ◽  
Health Concern ◽  
Quality Level ◽  
Household Level ◽  
Water Education ◽  
Treatment Measures ◽  
Averting Behavior

Water quality remains a significant issue and a source of serious health concern in the developing world. This paper investigates the water averting behavior at the household level by using a primary survey data from Siddharthangar, Nepal. While past studies have generally attributed averting behaviors to risk perception, we place a particular emphasis on the divergence between the household’s perception of their drinking water quality and the actual water quality level in driving the averting behavior. The findings indicate that the perception of the water quality affects a household’s decision to employ water treatment measures. Households that considered their water to be safe were less likely to treat their water. Furthermore, in addition to perception, the result also suggests the deviation between actual and perceived water quality level could also be a crucial element in the decision to employ water treatment measures. Households with divergence between risk perception and the objective water quality levels were less likely to treat their water and this result held across different specifications. In contrast, households with minimal deviation were more likely to employ treatment measures. Findings also suggest the source of drinking water, education level, income and the taste of the drinking water also drives the averting behavior.

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