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Tytuł artykułu

Internet of Things (IoT) and Artificial Neural Networks Towards Water Pollution Forecasting

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Water could be some-times a source of danger on people's lives and property. Although it is one of the most important elements of life on this planet. This article define the threat of water pollution in Tigris River in Iraq. by collecting a data that generated by sensors that installed in a water pollution sensing project in Baghdad city, also this article aimed to detect and analyze the behavior of water environment. It is an effort to predict the threat of pollution by using advanced scientific methods like the technology of Internet of Things (IoT) and Machine learning in order to avoid the threat and/or minimize the possible damages. This can be used as a proactive service provided by E-governments towards their own citizens.
Rocznik
Strony
117--129
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
  • Iraqi Federal Board of Supreme Audit, Baghdad, Iraq
  • Atilim University, Ankara, Turkey
autor
  • Atilim University, Ankara, Turkey
  • Molde University College-Specialized University in Logistics, Norway
Bibliografia
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  • Ayachi, R., Boukhris, I., Mellouli, S., Amor, N., Elouedi, Z., (2015). Proactive and reactive e-government services recommendation. © Springer-Verlag Berlin Heidelberg.
  • Boehm, A. (2019). Risk-based water quality thresholds for coli phage in surface waters: Effect of temperature and contamination aging. Environmental Science: Processes & Impacts, 4(8), 134-142.
  • Dave, T., Tschofenig, H., and Barnes, M. (2015). Architectural Considerations in Smart Object Networking IETF 92 Technical Plenary - IAB RFC 7452. 6 Sept. Web. https://www.ietf.org/proceedings/92/slides/slides-92-iab-techplenary-2.pdf .
  • Corwin, D.L., & Yemoto, K. (2017). Salinity: Electrical Conductivity and Total Dissolved Solids. © Soil Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA.
  • Enderlein, S., Enderlein, E., & Williams, W. (1990). Water Quality Requirements. World Health Organization (WHO), https://www.who.int/water_sanitation_health/resourcesquality/wpcchap2.pdf .
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  • Ibrahim, T. (2014). A Road Map to a Successful Application of E-Government in Iraq. Çankaya University. http://earsiv.cankaya.edu.tr:8080/xmlui/bitstream/handle/123456789/318/%C4%B0brahim%2c%20Thaer.pdf?sequence=1&isAllowed=y
  • Ibrahim, T., Mishra, A., Bostan, A. (2019). Role of E-government in Reducing Disasters. TEM Journal, 8(4), 1150-1158, ISSN 2217-8309, DOI: 10.18421/TEM84-07.
  • Internet society. (2015). The Internet of Things: An over view. www.internetsociety.org.
  • Jing, W., Tingtng, L. (2015). Application of wireless sensor network in Yangtze River Basin water environment monitoring, in Proceedings of the 27th Chinese Control and Decision Conference (CCDC ’15), 5981-5985, IEEE, Qingdaob, China, © IEEE.
  • Kale, V. S. (2016). Consequence of temperature, pH, turbidity and dissolved oxygen water quality parameters. International Advanced Research Journal in Science, Engineering and Technology, 3(8), 186-190.
  • Keskin, T., Düenci, M., and Kaçaro, F. (2015). Prediction of water pollution sources using artificial neural networks in the study areas of Sivas. Karabük and Bart n (Turkey). Environmental Earth Sciences, 73(9), 5333-5347.
  • LAILIA, N. L., Arafah, F., Jaelani, A., & PAMUNGKAS, A. D. (2015). Development of water quality parameter retrieval algorithms for estimating total suspended solids and chlorophyll-A concentration using Landsat-8 imagery at Poteran island water. Remote Sensing and Spatial Information Sciences, 2(2), 55-62.
  • Leonidas G., and Christopher G. (2016). Understanding electronic government research and smart city: A framework and empirical evidence. 99-112 © IOS Press and the authors.
  • Lopes, N., Pinto, F., Furtado, P., Silva J. (2014). IoT architecture proposal for disabled people. In: IEEE 10th international conference on wireless and mobile computing, networking and communications (WiMob), 152-158. DOI: 10.1109/WiMOB.2014.6962164.
  • Maojing, N. (2016). River Water Quality Monitoring and Simulation based on WebGIS – Anhui Yinghe River as an Example. Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, Geoscience, 716-720. DOI: 10.1109/IMCCC.2016.119, © IEEE.
  • McCaffrey, S. (2010). Water Quality Parameters and Indicators. Waterwatch Coordinator, Namoi Catchment Management Authority, https://sswm.info/sites/default/files/reference_attachments/MCCAFFREY%20ny%20Water%20Quality%20Parameters% 20&%20Indicators.pdf.
  • Sarrayrih, M.A., & Sriram, B. (2015). Major challenges in developing a successful e-government: A review on the Sultanate of Oman. Journal of King Saud University- Computer and Information Sciences, 27(2), 230-235.
  • Moon, M.J. (2002). The Evolution of E-Government among Municipalities: Rhetoric or Reality? Public Administration Review, 62, 424-433.
  • Mustofa, K. (2013). Translating the Idea of the E-Government One-Stop-Shop in Indonesia. © IFIP International Federation for Information Processing.
  • Nghe, T., Hai, T., Ngon, C. (2020). Deep Learning Approach for Forecasting Water Quality in IoT Systems. International Journal of Advanced Computer Science and Applications, 11(8).
  • Nordfors, L., Ericson, B., Lindell, H., & Lapidus, J. (2009). eGovernment of tomorrow: Future scenarios for 2020. Vinnova – Swedish Governmental Agency for Innovation Systems.
  • Northeast Georgia Regional Development Center. (1998). Watershed Protection Plan Development Guidebook.
  • https://epd.georgia.gov/sites/epd.georgia.gov/files/related_files/site_page/devwtrplan_b.pdf.
  • Rajput, A., Nair, K. (2013). Significance of Digital Literacy in E-Governance. The SIJ Transactions on Industrial Financial & Business Management, 1(4), 136-141.
  • Ramos, P. M., Pereira, J. D., Ramos, H. M. G., & Ribeiro, A. L. (2008). A four-terminal water-quality-monitoring conductivity sensor. IEEE Transactions on Instrumentation and Measurement, 57(3), 577-583.
  • Saleh, H. (2013). Design of disaster early warning systems using artificial intelligence. Higher Commission for Scientific Research - Damascus - Syria, http://www.arsco.org/detailed/a8fbcc55-1c65-48bd-be39-67872f50decf .
  • Shalini, E., Surya, P., Thirumurugan, R., & Subbulakshmi, S. (2016). Cooperative flood detection using SMS through IoT. Int. J. Adv. Res. Elect., Electron. Instrum. Eng, 5(3), 3410-3414.
  • Signs, V. (2010). The Five Basic Water Quality Parameters, The Clean Water Team Guidance Compendium for Watershed Monitoring and Assessment State Water Resources Control Board 310.doc, http://home.iitk.ac.in/~anubha/Water2.pdf
  • The Food and Agriculture Organization (FAO). (2008). Euphrates –Tigris River Basin, Irrigation in the Middle East region in figures – AQUASTAT Survey, http://www.fao.org/nr/water/aquastat/basins/euphrates-tigris/Euphrates.tigris-CP_eng.pdf
  • Ullo, S., Sinha, G. (2020). Advances in Smart Environment Monitoring Systems Using IoT and Sensors. MDPI journal. Sensors 2020, 20, 3113 DOI: 10.3390/s20113113.
  • World Health Organization (WHO). (1996). Total dissolved solids in Drinking-water, Guidelines for drinking-water quality, 2nd ed. Vol. 2. Health criteria and other supporting information. World Health Organization, Geneva, https://www.who.int/water_sanitation_health/dwq/chemicals/tds.pdf.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e92a7763-4fe8-45fd-bba1-2bd1aa2c8978
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