Water Demand Forecasting using Deep Learning in IoT Enabled Water Distribution Network
DL-Water Demand Forecasting for Water Distribution Design
Keywords:
Internet of Things, Recurrent Neural Network, Long Short-Term Memory, Auto Regressive Integrated Moving Average, Water Distribution NetworkAbstract
Most of the water losses occur during water distribution in pipelines during transportation. In order to eradicate the losses, an “IoT based water distribution system” integrated with “Fog and Cloud Computing" proposed for water distribution and underground health monitoring of pipes. For developing an effective water distribution system based on Internet of Things (IoT), the demand of the consumer should be analysed. So, towards predicting the water demand for consumers, Deep learning methodology called Long Short-Term Memory (LSTM) is compared with traditional Time Series methodology called Auto Regressive Integrated Moving Average (ARIMA) in terms of error and accuracy. Now based on demand prediction with higher accuracy, an IoT integrated “Water Distribution Network (WDN)” is designed using hydraulic engineering. This WDN design will ensure minimal losses during transportation and quality of water to the consumers. This will lead to development of a smart system for water distribution.
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[30] [Online] https://catalog.data.gov/dataset?q=water+consumption+demand+forecasting
[2] Abdelhafidh, M.; Fourati, M.; Fourati, L.C.; Abidi, A. (2017). A remote water pipeline monitoring system IoT-based architecture for new industrial era 4.0 14th International Conference on Computer Systems and Applications, 1184-1191, 2017. https://doi.org/10.1109/AICCSA.2017.158
[3] Afshar, A.; Miri Khombi, S.M. (2015). Multi objective Optimization of Sensor Placement in Water Distribution Networks; Dual Use Benefit approach. International Journal of Optimization in Civil Engineering, 5(3), 315-331, 2015.
[4] Akram, T.; Naqvi, S.R., Haider, S.A., Kamran, M. (2017). Towards real-time crops surveillance for disease. Computers and Electrical Engineering, 59, 15-26, 2017. https://doi.org/10.1016/j.compeleceng.2017.02.020
[5] Amatulla, P. H.; Navnath, B.P.; Yogesh, B.P. (2017). IoT based water management system for smart city. International Journal of Advanced Research , Ideas and Innovations in Technology, 3(2), 319-383, 2017.
[6] Amin, M.; Amanullah, M.; Akbar, A. (2014). Time series modelling for forecasting wheat production of Pakistan Journal of Animal and Plant Sciences, 24(5), 1444-1451, 2014.
[7] Bennett, P.J.; Soga, K.; Wassell, I.et all. (2010). Wireless sensor networks for underground railway applications: case studies in Prague and London, Smart Structures and Systems, 6, 619-639, 2010. https://doi.org/10.12989/sss.2010.6.5_6.619
[8] Bonomi, F.; Milito, R.; Natarajan, P.; Zhu, J. (2014). Fog computing: A platform for internet of things and analytics, Big Data and Internet of Things: A roadmap for smart Environments, New York, NY, USA: Springer, 169-186, 2014. https://doi.org/10.1007/978-3-319-05029-4_7
[9] Candelieria, A.; Archettia, F. (2014). Identifying typical urban water demand patterns for a reliable short-term forecasting- the ice water project approach, 16th Conference on Water Distribution System analysis, Bari, Italy, 89, 1004-1012, 2014. https://doi.org/10.1016/j.proeng.2014.11.218
[10] Chena, J.; Boccelli, D. L. (2014). Demand forecasting for water distribution systems, 12th International Conference on Computing and Control for the Water Industry, Perugia, Italy, 70, 339-342, 2014. https://doi.org/10.1016/j.proeng.2014.02.038
[11] Dorini, G.; Jonkergouw, P.; Kapelan, Z.; Savic, D. (2010). SLOTS: Effective algorithm for sensor placement in water distribution systems, Journal of Water Resources Planning and Management, 136(6), 620-628, 2010. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000082
[12] Ferrari, G. (2009). Sensor networks, Springer Series on signals and communication technology,, 1860-4862, 2009. https://doi.org/10.1007/978-3-642-01341-6
[13] Gwaivangmin, B.; Iand Jiya, J. D. (2017).Water demand prediction using artificial neural network for supervisory control, Nigerian Journal of Technology (NIJOTECH), 36(1), 148-154, 2017.
[14] Hart, W. E.; Murray, R.(2010). Review of sensor placement strategies for contamination warning systems in drinking water distribution systems, Journal of Water Resources Planning and Management, 136(6), 611-619. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000081
[15] Hu, C.; Tian, D.; Yan, X. (2014). Research on placement of water quality sensor in water distribution systems, 11th World Congress on Intelligent Control and Automation , 3584-3587, 2014.
[16] Kalpana, M B. (2016). Online monitoring of water quality using Raspberry Pi3 model, International Journal of Innovative Technology and Research, 4(6), 4790-4795, 2016.
[17] Lakshmi, K. N.; Suresh, S. (2019). IoT based smart water distribution management and underground pipe health monitoring system for smart city, Proceedings of 5thIEEE International Conference For Convergence In Technology, Pune, India,, 1-7, 2019.
[18] Laspidou, C.; Papageorgiou, E.; Kokkinos, K. et all. (2015). Exploring patterns in water consumption by clustering, 13th Computer Control for Water Industry Conference, Leicester, UK, 119, 1439-1446, 2015. https://doi.org/10.1016/j.proeng.2015.08.1004
[19] Lenker, J.; Carroll, J.; Lenkor, K.; Carroll, C. (2004). United States patent application US 10/735,329. https://mdepatents.com/, 2004.
[20] Liu, Y.; Zhang, W.; Cui, X.; Zhang, G.; Wang, G. (2015). City pipe network intelligent service based on gis and internet of things 7th International Conference on Intelligent Computation Technology and Automation, 169-186, 2015. https://doi.org/10.1109/ICICTA.2014.225
[21] Narayanan, L. K.; Sankaranarayanan, S.; Rodrigues, J. J.; Lorenz, P. (2020). Multi-Agent-Based Modeling for Underground Pipe Health and Water Quality Monitoring for Supplying Quality Water. International Journal of Intelligent Information Technologies (IJIIT), 16(3), 52-79, 2020. https://doi.org/10.4018/IJIIT.2020070103
[22] Noje, D.; Tarca, R.; Dzitac, I.; Pop, N.(2019). IoT Devices Signals Processing based on Multidimensional Shepard Local Approximation Operators in Riesz MV-algebras. International Journal of Computers Communications & Control, 14(1), 56-62, 2019. https://doi.org/10.15837/ijccc.2019.1.3490
[23] Ostfeld, A.; Asce, M.; Salomons, E. (2004). Optimal layout of early warning detection stations for water distribution systems security Journal of Water Resources Planning and Management,, 130(5), 377-385, 2004. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:5(377)
[24] Pracheet, V.; Akshay, K.; Pratik, J.et all.(2015). Towards an IoT based water management system for campus 1st International Smart Cities Conference (ISC2), IEEE Xplore, 1-6, 2015.
[25] Pranita, V.K.; Joshi, M.S.(2015). An IOT based water supply monitoring and controlling system with theft identification, International Journal of Innovative Research in Science, Engineering and Technology, 5(9), 16152-16159, 2015.
[26] Rinaudo, JD. (2015. Long-term water demand forecasting. In Grafton Q., Daniell K., Nauges C., Rinaudo JD., Chan N. (eds) Understanding and Managing Urban Water in Transition. Global Issues in Water Policy, Springer, 15, 239-268, 2015. https://doi.org/10.1007/978-94-017-9801-3_11
[27] Tariq, A.L.K.; Ziyad, A.L.T.; Abdullah, A.L.O. (2013). Wireless sensor networks for leakage detection in underground pipelines: a survey paper, 5th International Symposium on Application of Ad hoc and Sensor Networks, Procedia Computer Science, 491- 498, 2013. https://doi.org/10.1016/j.procs.2013.09.067
[28] Tom, R. J.; Sankaranarayanan, S.; Rodrigues Joel, J. P. C. (2019). Smart energy management and demand reduction by consumers and utilities in an IoT fog based power distribution system, IEEE Internet of Things journal, 6(5), 7386-7394, 2019. https://doi.org/10.1109/JIOT.2019.2894326
[29] Veeramanikandan, M.; Sankaranarayanan, S. (2019). Publish/subscribe based multi-tier based computational model in internet of things for latency reduction, Journal of parallel and distributed computing , 127, 18-27, 2019. https://doi.org/10.1016/j.jpdc.2019.01.004
[30] [Online] https://catalog.data.gov/dataset?q=water+consumption+demand+forecasting
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