Automated water management system with ai-based demand prediction

• Autorzy: Nakib Arman Mohammad

Identyfikatory

DOI

10.35784/iapgos.6106

Warianty tytułu

PL

Zautomatyzowany system zarządzania wodą z prognozowaniem zapotrzebowania opartym na sztucznej inteligencji

• Języki publikacji: EN

Abstrakty

EN

Access to fresh water has become a headache for many nations around the world today due to water scarcity. Since this aspect is increasing,this paper introduces a system to estimate the households’water needs in a day taking into consideration, thesize of thefamily, zone, temperature, season, working status, location, and religion. By applying machine learning, the system determines the balance of water distributionbetweenthe families by these parameters. The predicted values determine the water requirements for each household hence the distribution of water in the households on a particular day. This includes the use of an Arduino microcontroller, water flow meter, and solenoid valves that make the water supply system to be an automatic system. As a result of this, the system helps to control thewastage of water and also ensures that the amount of water required in each home is assessed accurately.

PL

Dostęp do świeżej wody stał się obecnie problemem dla wielu krajów na całym świecie z powodu jej niedoboru. W związku z narastającym problemem, niniejszy artykuł wprowadza system, który szacuje dzienne zapotrzebowanie gospodarstw domowych na wodę, biorąc pod uwagę takie czynniki jak wielkość rodziny, strefa, temperatura, pora roku, status zatrudnienia, lokalizacja oraz religia. Dzięki zastosowaniu uczenia maszynowego, system określa równowagę w dystrybucji wody między gospodarstwami domowymi na podstawie tych parametrów. Przewidywane wartości określają zapotrzebowanie na wodę w każdym gospodarstwie domowym, a tym samym dystrybucję wody w danym dniu. System obejmuje również użycie mikrokontrolera Arduino, miernika przepływu wody oraz zaworów elektromagnetycznych, co sprawia, że system dostarczania wody jest automatyczny.W wyniku tego system pomaga kontrolować marnotrawstwo wody oraz zapewnia, że ilość wody potrzebna w każdym domu jest dokładnie oceniona.

Słowa kluczowe

EN

factors influencing water consumption; machine learning model; water distribution control; water wastage control

PL

czynniki wpływające na zużycie wody; model uczenia maszynowego; kontrola dystrybucji wody; kontrola marnotrawstwa wody

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2024
• Tom: T. 14, nr 4
• Strony: 136--140
• Opis fizyczny: Bibliogr. 44 poz., rys., wykr.

Twórcy

autor

Nakib Arman Mohammad

• Nanjing University of Information Science & Technology, Artificial Intelligence, Nanjing, Jiangsu, China

• https://orcid.org/0009-0006-4986-8806

Bibliografia

• [1] Almulhim Abdulaziz I., Abubakar I. A.: A segmentation approach to understanding water consumption behavioral patterns among households in Saudi Arabia for a sustainable future. Resources, Environment and Sustainability 15, 2024, 100144.
• [2] Ali A. et al.: Design of an automatic rooftop water tank filling system and measurement of consumed water for home appliance. International Journal of Automation and Smart Technology 13(1), 2023, 2371–2371.
• [3] Adu Manu et al.: Leakage detection and automatic billing in water distribution systems using smart sensors. Digital Transformation for Sustainability: ICT-supported Environmental Socio-economic Development. Springer International Publishing, Cham 2022, 251–270.
• [4] Ali S. et al.: Controlled fluid flow without controlling pump using Arduino. Archives of Advanced Engineering Science 2024, 1-11.
• [5] Bergel T., Młyńska A.: Analysis of the impact of the air temperature on water consumption for household purposes in rural households. Journal of Ecological Engineering 22(3), 2021, 289–302.
• [6] Babu P., Rajasekaran C.: Economically precise water resource management for domestic usage in India. Circuits and Systems 7(10), 2016, 2821.
• [7] Baranidharan S. et al.: Smart water distribution system using internet of things (IoT). AIP Conference Proceedings 2831(1), 2023.
• [8] Chang H. et al.: Sensitivity of urban water consumption to weather and climate variability at multiple temporal scales: The case of Portland, Oregon. International Journal of Geospatial and Environmental Research 1(1), 2014, 7.
• [9] Crouch M. L. et al.: Defining domestic water consumption based on personal water use activities. AQUA—Water Infrastructure, Ecosystems and Society 70(7), 2021, 1002–1011.
• [10] Chavhan S. et al.: IoT-based Remote Control Water Distribution System. International Journal of Intelligent Systems and Applications in Engineering 12(7s), 2024, 572–583.
• [11] Dimkić D.: Temperature impact on drinking water consumption. Environmental Sciences Proceedings 2(1), 2020, 31.
• [12] Fan L. et al.: Factors affecting domestic water consumption in rural households upon access to improved water supply: Insights from the Wei River Basin, China. PloS one 8(8), 2013, e71977.
• [13] Gore S. et al.: Innovations in smart city water supply systems. International Journal of Intelligent Systems and Applications in Engineering 11(9s), 2023, 277–281.
• [14] Jiménez B., Asano T.: Water reuse: An international survey of current practice, issues and needs. IWA publishing 2008.
• [15] Kalyani C. et al.: Regular and equal water supply system. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology 15(1), 2023, 34–37.
• [16] Kumar B. S. et al.: Water management and control systems for smart city using IoT and artificial intelligence. International Conference on Edge Computing and Applications (ICECAA). India, Tamilnadu, 2022, 653–657.
• [17] Lenzen M. et al.: International trade of scarce water. Ecological Economics 94, 2013, 78–85.
• [18] Matos C. et al.: Water and energy consumption in urban and rural households. 39th International Symposium CIB W062 on Water Supply and Drainage for Buildings. Japan, Nagano 2013.
• [19] Mule A. et al.: Smart Water Supply Management System With Anti-Theft Measures. International Research Journal of Modernization in Engineering Technology and Science 5(5), 2023, 3607–3614.
• [20] Mekonnen M. M., Hoekstra A. Y.: A global and high-resolution assessment of the green, blue and grey water footprint of wheat. Hydrology and earth system sciences 14(7), 2010, 1259–1276.
• [21] Nirmala D. et al.: System for water quality monitoring and distribution. E3S Web of Conferences 399, EDP Sciences, 2023.
• [22] Nur’Im J., Setyawan G.: Prototype design of clean water distribution system on residential scale using Arduino Mega microcontroller. AIP Conference Proceedings 2540(1), 2023.
• [23] Nakib A. M. et al.: Machine learning-based water requirement forecast and automated water distribution control system. Computer Science & IT Research Journal 5(6), 2024, 1453–1468.
• [24] Nakib A. M., Barua B.: Aqua Flow Master: Intelligent Liquid Flow Control And Monitoring System. International Research Journal of Modernization in Engineering Technology and Science 6(4), 2024, 8232–8237.
• [25] Ogidan O. K., Olla M., Odey I.: Water distribution control with real-time monitoring. IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON). 2022.
• [26] Pratama M., Firmansyah M.: AWAS (Automatic Water System) as a clean water distribution device to mitigate the impact of climate change. Research Report. WaterIsLifeSchools 2022.
• [27] Rondinel-Oviedo D. R., Sarmiento-Pastor J. M.: Water: consumption, usage patterns, and residential infrastructure. A comparative analysis of three regions in the Lima metropolitan area. Water International 45(7-8), 2020, 824–846.
• [28] Rondinel O., Daniel R., Sarmiento-Pastor J. M.: Water: consumption, usage patterns, and residential infrastructure. A comparative analysis of three regions in the Lima metropolitan area. Water International 45(7-8), 2020, 824–846.
• [29] Rodríguez C. et al.: Water balance assessment in schools and households of rural areas of Coquimbo region, north-central Chile: Potential for greywater reuse. Water 12(10), 2020, 2915.
• [30] Reddy D. G. et al.: Aadhaar Enabled Water Distribution System. Water Resources Management 38(7), 2024, 2279–2291.
• [31] Rexline S. J., Renold M., Deeba M.: Potable water distribution monitoring system using internet of things. AIP Conference Proceedings 2842(1), 2023.
• [32] Rahmatulloh A. et al.: IoT-Enabled Water Distribution Monitoring: A Sensor-Based Analytical Model. Ingénierie des Systèmes d'Information 28(6), 2023.
• [33] Smith A., Ali M.: Understanding the impact of cultural and religious water use. Water and Environment Journal 20(4), 2006, 203–209.
• [34] Singha B. et al.: Changing patterns of household water consumption and conservation behaviour in Bangladesh: an exploration in the context of COVID-19 pandemic. International Journal of Innovation and Sustainable Development 18(1-2), 2024, 106–122.
• [35] Spang E. S. et al.: The water consumption of energy production: an international comparison. Environmental Research Letters 9(10), 2014, 105002.
• [36] Timmerman M.: Water consumption in the Middle East and North Africa (MENA) region: a virtual water approach, 2013.
• [37] Tejas T. et al.: IoT based water management system with machine learning. AIP Conference Proceedings 2742(1), 2024.
• [38] Utami R. R. et al.: Mapping domestic water use to quantify water-demand and water-related contaminant exposure in a peri-urban community, Indonesia. International Journal of Environmental Health Research 34(1), 2024, 625–638.
• [39] Vladislav D. S.: A short study on smart water distribution through sensor network. Big data and computing visions 2(3), 2022, 122–127.
• [40] Velayudhan N. K. et al.: IoT-enabled water distribution systems – a comparative technological review. IEEE Access 10, 2022, 101042–101070.
• [41] Xenochristou M., Blokker M.: Investigating the influence of weather on water consumption: a Dutch Case Study (032). WDSA/CCWI Joint Conference Proceedings 1, 2018.
• [42] Yan L.: The ethnic and cultural correlates of water consumption in a pluralistic social context – the Sydney Metropolitan Area. Diss. 2015.
• [43] https://www.epa.gov/watersense/statistics-and-facts
• [44] https://www.statista.com/statistics/278066/global-water-demand-by-region/