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Journal articles on the topic 'Water Consumption'

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Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Komarenko, Oleksandr, and Ivan Hrytsyk. "PSOC4 Based Intelligent Water Consumption Meter." Advances in Cyber-Physical Systems 4, no. 2 (September 23, 2019): 75–83. http://dx.doi.org/10.23939/acps2019.02.075.

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2

TSUDA, Morimasa, and Yoichi IWAMI. "ESTIMATION OF DAILY HOUSEHOLD WATER CONSUMPTION USING METERED WATER CONSUMPTION DATA." Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 72, no. 6 (2016): II_79—II_85. http://dx.doi.org/10.2208/jscejer.72.ii_79.

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3

Özlem Vurmaz, Merve, and Hülya Boyacioglu. "Airport Water Consumption Footprinting." Environment and Ecology Research 6, no. 6 (November 2018): 519–24. http://dx.doi.org/10.13189/eer.2018.060601.

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4

Vertommen, Ina, Roberto Magini, and Maria da Conceição Cunha. "Scaling Water Consumption Statistics." Journal of Water Resources Planning and Management 141, no. 5 (May 2015): 04014072. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000467.

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5

Zhang, Xinxin, Junguo Liu, Xu Zhao, Hong Yang, Xiangzheng Deng, Xiaohui Jiang, and Yiping Li. "Linking physical water consumption with virtual water consumption: Methodology, application and implications." Journal of Cleaner Production 228 (August 2019): 1206–17. http://dx.doi.org/10.1016/j.jclepro.2019.04.297.

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6

Edmonds, Caroline J., Rosanna Crombie, Haiko Ballieux, Mark R. Gardner, and Lynne Dawkins. "Water consumption, not expectancies about water consumption, affects cognitive performance in adults." Appetite 60 (January 2013): 148–53. http://dx.doi.org/10.1016/j.appet.2012.10.016.

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7

Meireles, I., V. Sousa, B. Bleys, and B. Poncelet. "Domestic hot water consumption pattern: Relation with total water consumption and air temperature." Renewable and Sustainable Energy Reviews 157 (April 2022): 112035. http://dx.doi.org/10.1016/j.rser.2021.112035.

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8

Barraj, Leila, Carolyn Scrafford, Jennifer Lantz, Carrie Daniels, and Gary Mihlan. "Within-day drinking water consumption patterns: Results from a drinking water consumption survey." Journal of Exposure Science & Environmental Epidemiology 19, no. 4 (May 14, 2008): 382–95. http://dx.doi.org/10.1038/jes.2008.28.

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9

Chao, Fang-Lin, Ching-Lin Lu, and Je-Ming Lin. "Ergonomic Study for Elderly Aquatic Exercises with Less Water Consumption." International Journal of Materials, Mechanics and Manufacturing 7, no. 2 (April 2019): 95–99. http://dx.doi.org/10.18178/ijmmm.2019.7.2.438.

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10

Dr. S. Satheesbabu, Dr. S. K. Somasundaram, Dr A. Thomas Paul Roy,. "Water consumption analysis using IoT." Psychology and Education Journal 58, no. 1 (January 15, 2021): 4302–6. http://dx.doi.org/10.17762/pae.v58i1.1499.

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Water is a fundamental asset for people, and its administration is a central point of contention. To conserve water, this system improves the expanded use of water. Internet of things is arrangement of interrelated processing gadgets, computing entities, vehicles, home machines and different things installed with electronic chips and sensors. The system is planned utilizing Nodemcu, ESP8266 and sensors. ESP8266, which is a less cost cloud microchip. This framework will comprise of a water pipe with water flow meter associated with it and a Nodemcu board and ESP8266 associated with it. First we utilize a water flow meter and gather the information as water moves through it.ESP8266 Wi-Fi module is a minimal effort CPU that gathers and sends the data to the cloud. We utilize the Nodemcu to arrange between water flow meter and the ESP 8266 module and afterward utilize the Thing speak Internet of things investigation stage to break down and show the information in visual organization. The yield of this system will be utilized for checking the water and it tends to be shown visually through the graph. The venture can be fundamentally valuable for household and agricultural purposes as it assists with limiting the loss of water.. Index Terms: Internet of Things (IoT), Embedded systems, Wi-Fi module, water flow Sensors.

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11

Stewart, Jacquiline, Nicole Davis, and Linda Roberts. "Educating Customers on Water Consumption." Water e-Journal 5, no. 2 (2020): 1–13. http://dx.doi.org/10.21139/wej.2020.008.

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12

Pisarenko, P. V., and L. S. Mishukova. "Water consumption of winter wheat." Agrarian innovations, no. 1 (2020): 63. http://dx.doi.org/10.32848/agrar.innov.2020.1.10.

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13

Alhumoud, Jasem M. "Water consumption evaluation in Kuwait." Journal of Water Supply: Research and Technology-Aqua 51, no. 8 (December 2002): 483–88. http://dx.doi.org/10.2166/aqua.2002.0042.

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14

NAGAI, Masaru, Kouichi HACHIMURA, and Kiyoshi TAKAHASHI. "Water Consumption in Suckling Pigs." Journal of Veterinary Medical Science 56, no. 1 (1994): 181–83. http://dx.doi.org/10.1292/jvms.56.181.

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15

Hirrel, Timothy D. "Automating Water Consumption Data Manipulation." Journal - American Water Works Association 78, no. 8 (August 1986): 49–52. http://dx.doi.org/10.1002/j.1551-8833.1986.tb05798.x.

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16

Pereira, Maria Clara Nogueira, Kayo Renato da Costa de Castro, José Sampaio de Oliveira, and Gustavo Pinheiro. "Prototype for water consumption control." Journal of Engineering Research 3, no. 34 (September 27, 2023): 2–7. http://dx.doi.org/10.22533/at.ed.3173342326092.

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17

van der Donck, Jacques C. J., Jurrian Bakker, Jeroen A. Smeltink, Robin B. J. Kolderweij, Ben C. M. B. van der Zon, and Marc H. van Kleef. "Physical Chemistry of Water Droplets in Wafer Cleaning with Low Water Use." Solid State Phenomena 219 (September 2014): 134–37. http://dx.doi.org/10.4028/www.scientific.net/ssp.219.134.

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Reduction of water and energy consumption is of importance for keeping viable industry in Europe. In 2012 the Eniac project Silver was started in order to reduce water and energy consumption in the semiconductor industry by 10% [1]. Cleaning of wafers is one of the key process steps that require a high volume of Ultra-Pure Water (UPW). For the production of a single wafer more than 120 cleaning steps may be required [2]. Furthermore, the reduction of the feature size makes devices more vulnerable to damage by mechanical action. This trend gives rise to the need for new, gentler cleaning processes.

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18

Pusztafalvi, H., A. Szabó, T. Barcsi, I. Boncz, and O. Máté. "Examination of Water Consumption: Knowledge About Water Consumption In Population of Bátya And Pécs." Value in Health 18, no. 7 (November 2015): A563. http://dx.doi.org/10.1016/j.jval.2015.09.1839.

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19

DAVYDENKO, N., and S. KUNYTSKYI. "DEVELOPMENT OF A PROTOTYPE DATABASE OF INFORMATION TECHNOLOGY FOR MONITORING WATER CONSUMPTION FROM THE WATER SUPPLY NETWORK OF SETTLEMENTS." Herald of Khmelnytskyi National University. Technical sciences 307, no. 2 (May 2, 2022): 53–57. http://dx.doi.org/10.31891/2307-5732-2022-307-2-53-57.

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The availability of reliable and systematic information on water consumption modes is the basis for optimizing the operation modes of water supply facilities. The necessity of water consumption monitoring is substantiated in order to detect changes in the operation conditions of water supply system facilities due to the influence of seasonal and social factors. The daily water consumption graph was used as an indicator of the operation mode of water supply facilities. The procedure of analysis of actual water consumption graphs is the methodological basis of the proposed information technology. It involves the search for hidden regularities in the formation of water consumption and is based on the description of irregularities and the form of daily water consumption graphs. The design of the information technology prototype is performed using a unified modeling language. The functionality and behavior of information monitoring technology is described using a use case diagram. A structured database is the information basis of information technology. It contains a structured set of initial data on daily water consumption graphs, the results of the description of their irregularities and shapes, the results of the classification of daily water consumption graphs and a formalized description of the analysis results. A relational data model was used to construct the database. The developed database contains twelve tables. Relationship between tables is defined as «one to many». Implementation of monitoring on a regular basis will ensure the accumulation of new data. The integration of the developed prototype database and information technology into the information system for monitoring the functioning of the water supply system will help to identify new knowledge about the peculiarities of the formation of the water consumption mode. This will identify changes in the actual operation conditions of water supply facilities.

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20

Kashkoush, Ismail, and R. Novak. "Optimization of Deionized Water Consumption in Wafer Wet Processing." Solid State Phenomena 65-66 (November 1998): 49–52. http://dx.doi.org/10.4028/www.scientific.net/ssp.65-66.49.

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21

Graydon, Ryan Christopher, Paola Andrea Gonzalez, Abdiel Elias Laureano-Rosario, and Guillermo Reginald Pradieu. "Bottled water versus tap water." International Journal of Sustainability in Higher Education 20, no. 4 (May 7, 2019): 654–74. http://dx.doi.org/10.1108/ijshe-01-2019-0003.

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Purpose Bottled water consumption continues to break records worldwide and its environmental impact is often underestimated by the consumer. Many factors affect individuals’ choices to consume tap water and bottled water including perceived health risks and water quality. The University of South Florida (USF) has joined a nationwide initiative to become carbon-neutral, and reducing bottled water consumption was a chosen strategy. The purpose of this study was to assess the risk perceptions and drinking water choices of the USF-Tampa campus community. Design/methodology/approach A sample of 561 students, faculty and staff members responded to an online survey. The survey contained questions about the use of bottled water and tap water, reusable water bottles, risk perceptions and demographics. Findings The results revealed that certain groups – undergraduate students and ethnic/racial minorities (e.g. black/African American, Hispanic/Latino) – drank significantly more bottled water. Among political ideologies, Liberals drank the least bottled water. Females and minorities had significantly greater risk perceptions of the tap water on campus. Important perceived benefits were tap water being less expensive and better for the environment than bottled water. Important perceived barriers were poor tasting tap water and the desire for filtered water. Originality/value The findings suggest the need for public health campaigns to increase awareness of health, environmental and financial consequences of bottled water consumption. Such campaigns should aim to discourage bottled water and any potential increased sugar-sweetened beverage consumption while promoting tap water consumption.

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22

Pukhkal, Viktor, and Boris Jurmanov. "Selection of Hot Water Supply Metering Devices for Residential Buildings." Applied Mechanics and Materials 725-726 (January 2015): 1267–72. http://dx.doi.org/10.4028/www.scientific.net/amm.725-726.1267.

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Experimental measurements of actual hot water consumption in residential building have been performed. It was established that distribution of hot water for domestic needs has random character and constitutes non-stationary process. It was proposed for hot water supply systems calculation to employ “rated mode of water consumption”. Rated mode is a simplified mathematical description of entire ordered collection of hourly consumptions anticipated during rated period of hot water supply system operation. Most precise description of water consumption rated mode may be obtained with coefficient of general hourly discontinuity at 98% coverage. Selection of hot water metering devices for residential house requires that operating flow rate of water meter should correspond by maximum to residential building water consumption parameters. Methods of calculation of daily water volumes passing through water meter at flow rates lower than transient flow rate value for the specified device were proposed.

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23

Chen, An Qiang, Gang Cai Liu, and Dan Zhang. "A Study on the Relationship between Crown Characteristics, Fractal Dimension and Water Consumption of Poplars." Advanced Materials Research 255-260 (May 2011): 2919–24. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2919.

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The relationships among crown characteristics, crown fractal dimensions and water consumption were studied in seven 5-year-old poplar clones that were selected from the nursery garden of the Gaomi Forestry Bureau in China. The closest positive correlation was between single tree leaf area (TLA) and water consumption followed by the tree crown area (TCA). The leaf area index (LAI) and crown layer density (CLD) were positively correlated with water consumption but were not significantly correlated. There was a negative correlation between the crown shape ratio (CSR) of the L35, NL895 and I-107 poplars and water consumption. The CSR of the other clones was positively correlated with water consumption. But neither the positive nor negative correlations of CSR with water consumptions were significant. The crown characteristics of the fractal dimension had a similar variation trend. The fractal dimension had a significant positive correlation with TLA, LAI and CLD but no significant correlation with CSR and TCA. The fractal dimension of TCA (FDa) and the fractal dimension of crown volume (FDv) had a significant positive correlation with water consumption, and FDa was more closely correlated with water consumption than FDv. More fractal dimension resulted in more water consumption. Correlation analysis indicated that the presence of more foliage distributed in the crown surface or the more evenly distributed foliage in the inner crown resulted in more water consumption with the same leaf area.

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24

Tariq, Muhammad Atiq Ur Rehman, Riley Raimond Damnics, Zohreh Rajabi, Muhammad Laiq Ur Rahman Shahid, and Nitin Muttil. "Identification of Major Inefficient Water Consumption Areas Considering Water Consumption, Efficiencies, and Footprints in Australia." Applied Sciences 10, no. 18 (September 4, 2020): 6156. http://dx.doi.org/10.3390/app10186156.

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Due to population growth, climatic change, and growing water usage, water scarcity is expected to be a more prevalent issue at the global level. The situation in Australia is even more serious because it is the driest continent and is characterized by larger water footprints in the domestic, agriculture and industrial sectors. Because the largest consumption of freshwater resources is in the agricultural sector (59%), this research undertakes a detailed investigation of the water footprints of agricultural practices in Australia. The analysis of the four highest water footprint crops in Australia revealed that the suitability of various crops is connected to the region and the irrigation efficiencies. A desirable crop in one region may be unsuitable in another. The investigation is further extended to analyze the overall virtual water trade of Australia. Australia’s annual virtual water trade balance is adversely biased towards exporting a substantial quantity of water, amounting to 35 km3, per trade data of 2014. It is evident that there is significant potential to reduce water consumption and footprints, and increase the water usage efficiencies, in all sectors. Based on the investigations conducted, it is recommended that the water footprints at each state level be considered at the strategic level. Further detailed analyses are required to reduce the export of a substantial quantity of virtual water considering local demands, export requirements, and production capabilities of regions.

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25

Meletis, Zoë A., and Lisa M. Campbell. "Call It Consumption! Re-Conceptualizing Ecotourism as Consumption and Consumptive." Geography Compass 1, no. 4 (July 2007): 850–70. http://dx.doi.org/10.1111/j.1749-8198.2007.00048.x.

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26

Ethem Karadirek, I. "An experimental analysis on accuracy of customer water meters under various flow rates and water pressures." Journal of Water Supply: Research and Technology-Aqua 69, no. 1 (November 19, 2019): 18–27. http://dx.doi.org/10.2166/aqua.2019.031.

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Abstract Apparent losses are mainly due to metering errors in well-managed water supply systems. There are many types of water meters based on mechanisms to measure flow passing through. Therefore, selection of water meter type is important as meter type effects measurement accuracy. In this study, a total of 50 domestic water meters were tested under varying flow rates and different water pressures. Water consumptions of end-users show temporal changes depending on the life style of consumers. Flow rates passing through the water meter and water consumption profiles affect water meter accuracy. Water consumption of a couple of end-users was monitored and consumption patterns were extracted and obtained water consumption patterns were used to determine water meter errors. The collection method was applied for determination of water meter errors. Starting flow rates, error curves and weighted error of water meters were measured in a laboratory setup. Tested volumetric-type water meters have the lowest starting flow rate and the highest accuracy whereas single-jet water meters have the lowest accuracy and the highest starting flow rate. This study aimed to provide insights on the accuracy of water meters under varying flow rates and water pressures, and advantageous information for water meter-type selection.

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27

Travassos, Kaline Dantas, Hans Raj Gheyi, Helder Morais Mendes Barros, Frederico Antônio Loureiro Soares, Claudio Augusto Uyeda, Marcelo Gurgel Tavares, Nildo Da Silva Dias, and René Chipana-Rivera. "Water consumption of the sunflower crop irrigatedwith saline water." DYNA 86, no. 208 (January 1, 2019): 221–26. http://dx.doi.org/10.15446/dyna.v86n208.73203.

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The objective was to evaluate the water consumption of sunflower cultivars irrigated with saline waters. The experimental design utilized was in randomized blocks of a 2 x 4 factorial arrangement. This was composed of two levels of electrical conductivity (ECw) of irrigation water (N1 - 0.6 and N2 - 3.0 dS m-1 at 25 ºC) and four cultivars of sunflower (C1 - Helio 863, C2 - Embrapa 122-V2000, C3 - Catissol 01 and C4 - Multissol), with three replications, each of them with three plants totaling 72 experimental units. There was a reduction in water consumption of sunflower cultivars irrigated with saline water (N2 - 3.0 dS m-1). The irrigation with saline water resulted in a greater volume of drained water in all sunflower cultivars.

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28

Davenport, Matt. "Dial Down Water Use: Technology To Reduce Water Consumption." Sustainability: The Journal of Record 6, no. 1 (February 2013): 15–17. http://dx.doi.org/10.1089/sus.2013.9898.

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29

Liu, Moucheng, Lun Yang, and Qingwen Min. "Water-saving irrigation subsidy could increase regional water consumption." Journal of Cleaner Production 213 (March 2019): 283–88. http://dx.doi.org/10.1016/j.jclepro.2018.12.152.

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30

Rofi, A. "Water Pollution and Its Correlation to Mineral Water Consumption Expenditure." IOP Conference Series: Earth and Environmental Science 1313, no. 1 (March 1, 2024): 012012. http://dx.doi.org/10.1088/1755-1315/1313/1/012012.

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Abstract Water is a basic need for human life. However, challenges of access to clean and healthy water are increasingly being by pollution. This condition forces households to pay for clean and healthy mineral water. This study examines the distribution of water pollution levels and household mineral water consumption in Indonesia. It examines the relationship between water pollution and the spending the household mineral water consumption. The data used in this study are water pollution level data for 2021 from the raw data of PODES 2021 and spending of consumption the household mineral water data from the raw data of the SUSENAS 2021. Both data were analyzed using correlation analysis to calculate the r value. Then the data is presented in a map of the distribution of water pollution and mineral water consumption spending by districts in Indonesia. The results showed that the correlation of the two variables had a positive value of 0.026. Even though the relationship is not very strong, this proves that the higher the water pollution, the greater the spending for household consumption of mineral water.. Kalimantan Island is an area with a high water pollution level. In the distribution of mineral water consumption expenditure for households, Indonesia is classified as having a low consumption level, while DKI Jakarta Province is an area that has high mineral water consumption expenditure of an average of IDR 183,396/month.

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31

Zhao, Jinjin. "The cubic water Kuznets curve: patterns of urban water consumption and water policy effects." Water Policy 19, no. 1 (July 16, 2016): 28–45. http://dx.doi.org/10.2166/wp.2016.146.

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Utilizing panel data collected in 27 countries from 1960 to 2010, we demonstrate that the relationship between per capita urban water consumption and per capita gross domestic product exhibits an N-shaped pattern. Following the environmental Kuznets curve literature, this relationship can be named the cubic water Kuznets curve. We also demonstrate that water policies significantly influence per capita urban water consumption, which implies that appropriate policy interventions might allow developing countries to achieve economic development with less per capita water consumption.

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32

Otaki, Yurina, Hidehito Honda, and Kazuhiro Ueda. "Historical Self-Comparison of Water Consumption as a Water Demand Management Tool." Water 11, no. 4 (April 22, 2019): 844. http://dx.doi.org/10.3390/w11040844.

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This study aimed to identify effective presentation methods used for historical self-comparisons of residential water consumption that will lead to the efficient use of water. To compare each household’s current and previous water consumption, illustrations of water droplets were used as feedback every other week for five months, with the number of water droplets indicating an increase or decrease in water consumption. When only using the same blue water droplets, there was no change in water consumption. However, when using yellow and red droplets in cases of increased water use, we observed that water consumption declined. By improving the method of communication, historical self-comparisons of water consumption can realize an efficient use of water at all consumption levels.

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Saihi, A., and A. Alzaatreh. "Comparing the utilities consumptions in Dubai per category and community: MANOVA and cluster analysis approaches." IOP Conference Series: Earth and Environmental Science 958, no. 1 (December 1, 2021): 012022. http://dx.doi.org/10.1088/1755-1315/958/1/012022.

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Abstract UAE is marked by the increasing demand for water and electricity due to demographic, environmental and economic factors, coupled with the dependence on water desalination process, which is costly, consumes a lot of energy and is non-environmentally friendly. Like most of the authorities in UAE, Dubai Electricity and Water Authority is facing the challenges of balancing supply with demand and responding to consumer requirements, from one side, and addressing the continuously increasing consumption and slowing it down from another side. Therefore, policy makers can benefit from statistical data analysis in order to make informed decisions. This study aims to equip decision makers with useful tools and analysis to address some of their short- and long-term objectives related to production and consumption. The current study focused on three main objectives: (i) analysing the production of the desalination plants in Dubai, (ii) comparing the consumptions of water and electricity based on the four categories residential, commercial, industrial and others, and (iii) segmenting the various communities in Dubai depending on their consumption behavior. The data used for this study is collected from the open government data and SAS Programming is adopted for data analysis. The results of the analysis revealed that the desalinated water production follows an upward trend, yet still not in line with the consumption growth. Furthermore, there are significant differences between the four categories for both water and electricity consumptions. The highest levels of consumptions are associated with the residential and commercial categories. Finally, the cluster analysis technique revealed fifteen clusters of communities depending on the consumption levels.

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34

Zhong, Shane, Nick Turich, and Patrick Hayde. "AUTOMATED ANALYSIS OF METERED WATER CONSUMPTION." Water e-Journal 1, no. 1 (2016): 1–5. http://dx.doi.org/10.21139/wej.2016.002.

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35

Dimkić, Dejan. "Temperature Impact on Drinking Water Consumption." Environmental Sciences Proceedings 2, no. 1 (August 19, 2020): 31. http://dx.doi.org/10.3390/environsciproc2020002031.

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The production of water in a drinking water supply system (WSS) comprises all drinking water enter in the net, while the consumption of water generally comprises all billed amounts of water in a WSS. The production and consumption of water in a drinking WSS depend on different factors. Consumption rates depend on the consumer structure and habits, industrial demand, time of year, water pricing, climatic variables, secondary water losses and many other factors. One of the interesting factors is air temperature. It is especially important in the frame of climate change and global warming. Temperature impact on water consumption in a WSS is not uniform temporally (particularly throughout the year) and spatially (different climate regions and countries, different habits and different conditions in each WSS). Obtained correlations for the two biggest cities in Serbia (Belgrade and Niš) are presented in the paper and compared with some examples worldwide.

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36

Zhang, Xianqi, Mingliang Yue, Yuan Yao, and Hao Li. "Regional annual water consumption forecast model." DESALINATION AND WATER TREATMENT 114 (2018): 51–60. http://dx.doi.org/10.5004/dwt.2018.22358.

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37

Westrell, Therese, Yvonne Andersson, and Thor Axel Stenström. "Drinking water consumption patterns in Sweden." Journal of Water and Health 4, no. 4 (December 1, 2006): 511–22. http://dx.doi.org/10.2166/wh.2006.0034.

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Estimates on drinking water consumption are necessary in risk assessments on microbial hazards in drinking water. Large differences in consumption habits between countries have been reported. In order to establish estimates for the Swedish population, water consumption data from a waterborne outbreak investigation (157 people), a small water consumption study (75 people) and a large study on health and environmental factors (10,957 people) were analysed. A lognormal distribution for the daily direct/cold water intake in litres with μ=−0.299 and σ=0.570 was fitted to the quantitative data, representing the general population. The average daily consumption of tap water as plain drinking water and as heated tap water, e.g. in coffee and tea, was 0.86±0.48 l and 0.94±0.69 l, respectively. Women consumed more cold tap water than did men, while men appeared to have a higher consumption of heated tap water. Cold tap water intake was highest in the oldest age group, (≥70 years). The consumption of bottled water was very low (mean 0.06 l/d) when compared to other countries.

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38

Trigg, Mark. "Domestic water consumption in rural Guatemala." Waterlines 18, no. 2 (October 1999): 21–23. http://dx.doi.org/10.3362/0262-8104.1999.045.

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39

Huarote Zegarra, Raul, and Monica Romero Valencia. "Backpropagation to predict drinking water consumption." Journal of Sciences and Engineering 2, no. 2 (December 30, 2018): 31. http://dx.doi.org/10.32829/sej.v2i2.64.

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The present research article is based on the use of artificial neural networks as a tool for the prediction applied to the consumption of drinking water, where the artificial learning of a multilayer backpropagation network with the historical data consumed in m3 is used. In a computer with a regular feature, the backpropagation network was implemented in the Python 3.7.0 language, taking as a study object a case published on its SUNEDU website regarding drinking water consumption in 2017 and checking the results in January of 2018, taking these data for learning and the respective tests. It has been achieved as a result to predict the amount of water consumption of the institution. The test performed resulted in an excess of 23 m3, which represents 2.7% in excess given that in January 2018 a consumption of 833m3 was recorded. Bear in mind that it was done for the training process with a maximum error for the training of 0.000099 and with a maximum number of iterations of 100000. After training with the historical data of drinking water consumption, it was possible to predict the consumption of SUNEDU to January 2018.

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40

Kamyshova, Galina Nickolaevna, Dmitry Alexandrovich Kolganov, and Nadezhda Nickolaevna Terekhova. "Neural network modeling of water consumption." Agrarian Scientific Journal, no. 5 (May 26, 2021): 88–92. http://dx.doi.org/10.28983/asj.y2021i5pp88-92.

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Optimizing water management for irrigated agriculture requires the development of modern approaches to determining and predicting water consumption, despite the large number of already developed models. The article presents approaches to neural network modeling of water consumption. The advantage of such modeling is high accuracy and ability to adapt to changing parameters of the model, which distinguishes them from traditional methods and allows you to provide optimal results in terms of minimizing errors and increasing the tightness of the relationship between variables.

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41

Syme, Geoffrey J., Clive Seligman, and John F. Thomas. "Predicting Water Consumption From Homeowners' Attitudes." Journal of Environmental Systems 20, no. 2 (January 1, 1990): 157–68. http://dx.doi.org/10.2190/fnv4-vcv2-l1t9-4r62.

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42

Al-Jalali, E., and S. M. Shirreffs. "Water consumption in free-living adults." British Journal of Sports Medicine 44, no. 14 (November 1, 2010): i14. http://dx.doi.org/10.1136/bjsm.2010.078972.42.

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43

Chmielewska, Agnieszka. "Monthly model of hot water consumption." E3S Web of Conferences 100 (2019): 00007. http://dx.doi.org/10.1051/e3sconf/201910000007.

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The article presents a statistical analysis of the monthly consumption of hot water in apartment buildings. On this basis, a monthly model of hot water consumption in multi-family buildings was proposed. This model consists of two sub models: model of average daily consumption of domestic hot water and a model describing changes in consumption of hot water in individual months of the year. To construct the model of average daily consumption of hot water, data on the consumption of hot water was used out of 16 multi-family buildings from the period of two years. The data was supplemented with information on the housing structure in the building. The study of changes in hot water consumption in the following months was based on data obtained from 30 buildings. Statistical analysis showed a strong correlation between the monthly consumption of hot water, the outside temperature and holiday period.

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de Juan, J. A., J. M. Tarjuelo, J. F. Ortega, M. Valiente, and P. Carrión. "Management of water consumption in agriculture." Agricultural Water Management 40, no. 2-3 (May 1999): 303–13. http://dx.doi.org/10.1016/s0378-3774(99)00005-0.

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Randolph, Bill, and Patrick Troy. "Attitudes to conservation and water consumption." Environmental Science & Policy 11, no. 5 (August 2008): 441–55. http://dx.doi.org/10.1016/j.envsci.2008.03.003.

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46

Assaf, G., and N. Zieslin. "Night water consumption by rose plants." Journal of Horticultural Science 71, no. 5 (January 1996): 673–78. http://dx.doi.org/10.1080/14620316.1996.11515447.

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Wakeel, Muhammad, and Bin Chen. "Energy Consumption in Urban Water Cycle." Energy Procedia 104 (December 2016): 123–28. http://dx.doi.org/10.1016/j.egypro.2016.12.022.

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Carrington, C. G., D. M. Warrington, and Y. C. Yak. "Structure of domestic hot water consumption." International Journal of Energy Research 9, no. 1 (January 1985): 65–75. http://dx.doi.org/10.1002/er.4440090107.

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Sundar S, Et al. "Automated Pet Water Consumption Monitoring System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (November 2, 2023): 692–98. http://dx.doi.org/10.17762/ijritcc.v11i10.8565.

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More than half of all people on the planet voluntarily keep pets as companions, but these animals are starting to strain their owners. While feeding their pets, owners experience a lot of stress. One of the pet feeders that may be managed online through a mobile application is the IoT pet feeder. This paper details an IoT based monitoring system which automates the process of detecting and replacing dirty water in a pet’s bowl in a cheap manner. It also describes methods of tracking the overall water consumption of the pet via the system and monitoring the pet’s health via detection of consumption anomalies.

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Tian, Guiliang. "Effect of Consumption of Livestock Products on Water Consumption in China based on Virtual Water Theory." IERI Procedia 5 (2013): 112–17. http://dx.doi.org/10.1016/j.ieri.2013.11.079.

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