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IJSTR >> Volume 9 - Issue 12, December 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Water Consumption Prediction Challenges And Recent Research Directions

[Full Text]

 

AUTHOR(S)

Ahmed Abdelnasser, Sherif Hussein, Magdi Zakaria

 

KEYWORDS

Water resources management, Machine learning, Water quality, Internet of Things, Water supply and demand, Water pollution, and Land resources.

 

ABSTRACT

Recently, water-scarce resources became one of the most urgent problems that threaten the existence of the human species. In this paper, water consumption recent research directions were surveyed while the different challenges were discussed. The proper elements integration of any proposed system is a key success for the design to allow for the necessary expansion. That can enable the optimal management of water demand to reduce water consumption and improve water infrastructure utilization. A survey has been conducted to investigate the approaches and datasets involved in water resources management to shed light on the research directions in that vital field. The authors believe that it is of utmost importance to apply such integrated technologies of the Internet of Things, Machine Learning, Cloud Computing, and the emerging 5G technology to achieve the best possible water resources management performance.

 

REFERENCES

[1] Miller, S., Horvath, A., & Monteiro, P. (2018). Impacts of booming concrete production on water resources worldwide. Nature Sustainability, 1(1), 69-76. doi: 10.1038/s41893-017-0009-5Z6: https://doi.org/10.1016/j.jclepro.2017.04.040
[2] Zhang, X., Liu, J., Tang, Y., Zhao, X., Yang, H., & Gerbens-Leenes, P. et al. (2017). China’s coal-fired power plants impose pressure on water resources. Journal Of Cleaner Production, 161, 1171-1179. doi: 10.1016/j.jclepro.2017.04.040
[3] Hussein, S.E., & El-nasr, M.A. (2013). Resources Allocation in Higher Education based on System Dynamics and Genetic Algorithms. International Journal of Computer Applications, 77, 40-48.
[4] M. M. Fouad, A. I. El-Desouky, R. Al-Hajj and E. -S. M. El-Kenawy, "Dynamic Group-Based Cooperative Optimization Algorithm," in IEEE Access, vol. 8, pp. 148378-148403, 2020, doi: 10.1109/ACCESS.2020.3015892.
[5] E. -S. M. El-Kenawy, A. Ibrahim, S. Mirjalili, M. M. Eid and S. E. Hussein, "Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images," in IEEE Access, vol. 8, pp. 179317-179335, 2020, doi: 10.1109/ACCESS.2020.3028012.
[6] A. Ibrahim, S. Mohammed, H. A. Ali and S. E. Hussein, "Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm," in IEEE Access, vol. 8, pp. 122121-122134, 2020, doi: 10.1109/ACCESS.2020.3007336.
[7] E. M. El-Kenawy, M. M. Eid, M. Saber and A. Ibrahim, "MbGWO-SFS: Modified Binary Grey Wolf Optimizer Based on Stochastic Fractal Search for Feature Selection," in IEEE Access, vol. 8, pp. 107635-107649, 2020, doi: 10.1109/ACCESS.2020.3001151.
[8] A. Ibrahim, M. Noshy, H. A. Ali and M. Badawy, "PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization," in IEEE Access, vol. 8, pp. 81747-81764, 2020, doi: 10.1109/ACCESS.2020.2990828.
[9] Ahmed A, Hussein SE (2020) Leaf identification using radial basis function neural networks and SSA based support vector machine. PLOS ONE 15(8): e0237645. https://doi.org/10.1371/journal.pone.0237645
[10] Wang, H., Wang, W., Cui, Z., Zhou, X., Zhao, J., & Li, Y. (2018). A new dynamic firefly algorithm for demand estimation of water resources. Information Sciences, 438, 95-106. doi: 10.1016/j.ins.2018.01.041
[11] Al-Jawad, J., Alsaffar, H., Bertram, D., & Kalin, R. (2019). A comprehensive optimum integrated water resources management approach for multidisciplinary water resources management problems. Journal Of Environmental Management, 239, 211-224. doi: 10.1016/j.jenvman.2019.03.045
[12] Shen, C. (2018). A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists. Water Resources Research, 54(11), 8558-8593. doi: 10.1029/2018wr022643
[13] S. E. Hussein, "Picture Archiving and Communication System Analysis and Deployment," 2009 11th International Conference on Computer Modelling and Simulation, Cambridge, 2009, pp. 520-525, doi: 10.1109/UKSIM.2009.36.
[14] Ibrahim, T. Horiuchi and S. Tominaga, "Illumination-invariant representation for natural color images and its application," 2012 IEEE Southwest Symposium on Image Analysis and Interpretation, Santa Fe, NM, 2012, pp. 157-160, doi: 10.1109/SSIAI.2012.6202477.
[15] Ahmed A., Ibrahim A., Hussein S. (2019) Detection of Palm Tree Pests Using Thermal Imaging: A Review. In: Hassanien A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_12
[16] M. Salem, A. F. Ibrahim and H. A. Ali, "Automatic quick-shift method for color image segmentation," 2013 8th International Conference on Computer Engineering & Systems (ICCES), Cairo, 2013, pp. 245-251, doi: 10.1109/ICCES.2013.6707212.
[17] Horiuchi T., Ibrahim A., Kadoi H., Tominaga S. (2012) An Effective Method for Illumination-Invariant Representation of Color Images. In: Fusiello A., Murino V., Cucchiara R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33868-7_40
[18] A. Ibrahim, S. Tominaga and T. Horiuchi, "Spectral Invariant Representation for Spectral Reflectance Image," 2010 20th International Conference on Pattern Recognition, Istanbul, 2010, pp. 2776-2779, doi: 10.1109/ICPR.2010.680.
[19] Ibrahim A., Gaber T., Horiuchi T., Snasel V., Hassanien A.E. (2016) Human Thermal Face Extraction Based on SuperPixel Technique. In: Gaber T., Hassanien A., El-Bendary N., Dey N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_15
[20] Abdelhameed Ibrahim, Muhammed Salem, Hesham Arafat Ali, Block-based illumination-invariant representation for color images, Ain Shams Engineering Journal, Volume 9, Issue 4, 2018, Pages 917-926, doi: 10.1016/j.asej.2016.04.011.
[21] Ibrahim, A., Horiuchi, T., Tominaga, S., & Hassanien, A. E. (2017). Color Invariant Representation and Applications. In Hassanien, A. E., & Gaber, T. (Ed.), Handbook of Research on Machine Learning Innovations and Trends (pp. 1041-1061). IGI Global. http://doi:10.4018/978-1-5225-2229-4.ch046
[22] Ibrahim A., Horiuchi T., Tominaga S., Ella Hassanien A. (2016) Spectral Reflectance Images and Applications. In: Awad A., Hassaballah M. (eds) Image Feature Detectors and Descriptors. Studies in Computational Intelligence, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-319-28854-3_9
[23] Ibrahim, A., El-kenawy, E. S. M. (2020). Image Segmentation Methods Based on Superpixel Techniques: A Survey. Journal of Computer Science and Information Systems,15 (3 October 2020).
[24] Ibrahim, A., El-kenawy, E. S. M. (2020). Applications and Datasets for Superpixel Techniques A Survey. Journal of Computer Science and Information Systems,15 (3 October 2020).
[25] Sun, Y., Liu, N., Shang, J., & Zhang, J. (2017). Sustainable utilization of water resources in China: A system dynamics model. Journal Of Cleaner Production, 142, 613-625. doi: 10.1016/j.jclepro.2016.07.110
[26] Abdulbaki, D., Al-Hindi, M., Yassine, A., & Abou Najm, M. (2017). An optimization model for the allocation of water resources. Journal Of Cleaner Production, 164, 994-1006. doi: 10.1016/j.jclepro.2017.07.024
[27] Megdal, S., Eden, S., & Shamir, E. (2017). Water Governance, Stakeholder Engagement, and Sustainable Water Resources Management. Water, 9(3), 190. doi: 10.3390/w9030190
[28] Kumura T., Suzuki N., Takahashi M., Tominaga S., Morioka S., Ivan S., Smart water management technology with intelligent sensing and ICT for the integrated water systems, NEC Technical Journal, Vol. 9, No. 1, January, 2015, 103-106.
[29] Vinothini E., Suganya N., Automated water distribution and performance monitoring system, International Journal of Engineering and Innovative Technology (IJEIT), Vol. 3, Issue 8, February, 2014, 30-32.
[30] Whittle, A., Allen M., Preis A., Iqbal M., Sensor networks for monitoring and control of water distribution systems International Society for Structural Health Monitoring of Intelligent, The 6th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Hong Kong, 9-11 December, 2013.
[31] Shri J., Jagadeesan A., Lavanya A., Theft identification and automated water supply system using embedded technology, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 8, August, 2013, 3727-3733.
[32] Piasecki, A., Juraasz, J. and Skowron, R. (2017). Forecasting Surface Water Level Fluctuations of Lake Serwy (Northern Poland) by Artificial Neural Networks and Multiple Linear Regression. Journal of Environmental Engineering and Landscape Management, 25(4), pp.379-388.
[33] Tharwat A., Ibrahim A., Gaber T., Hassanien A.E. (2019) Personal Identification Based on Mobile-Based Keystroke Dynamics. In: Hassanien A., Tolba M., Shaalan K., Azar A. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018. Advances in Intelligent Systems and Computing, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-319-99010-1_42
[34] A. A. Nasser, M. Z. Rashad and S. E. Hussein, "A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks," in IEEE Access, vol. 8, pp. 147647-147661, 2020, doi: 10.1109/ACCESS.2020.3015655.
[35] Debasis B., Jaydip S., Internet of things: applications and challenges in technology and standardization, Wireless Personal Communications An International Journal, Vol. 58, Issue 1, May, 2011, 49–69.
[36] Hussein, S, and Arafat, H., “An Open Cloud Model for Expanding Healthcare Infrastructure” International Journal of Advanced Computer Science and Applications (IJACSA), 4(9), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040914
[37] Sedliacik, I. and Dado, J. (2017). Unintended Consequences of Interventions in Electricity Production and Consumption. New Trends and Issues Proceedings on Humanities and Social Sciences, 3(4), pp.16-22.
[38] Campillo, J., Dahlquist, E., Wallin, F. and Vassileva, I. (2016). Is real-time electricity pricing suitable for residential users without demand-side management?.Energy, 109, pp.310-325.
[39] Kasperowicz, R. (2014). Electricity consumption and economic growth: evidence from Poland. Journal of International Studies, 7(1), pp.46-57.
[40] Ghalehkhondabi, I., Ardjmand, E., Young, W. and Weckman, G., 2017. Water demand forecasting: review of soft computing methods. Environmental Monitoring and Assessment, 189(7).
[41] Brentan, B., Luvizotto Jr., E., Herrera, M., Izquierdo, J. and Pérez-García, R., 2017. Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, pp.532-541.
[42] Muhammad, A., Li, X. and Feng, J., 2019. Artificial Intelligence Approaches for Urban Water Demand Forecasting: A Review. Machine Learning and Intelligent Communications, pp.595-622.
[43] Papageorgiou, E., Poczeta, K. and Laspidou, C., 2016. Hybrid model for water demand prediction based on fuzzy cognitive maps and artificial neural networks. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[44] Shabani, S., Yousefi, P. and Naser, G., 2017. Support Vector Machines in Urban Water Demand Forecasting Using Phase Space Reconstruction. Procedia Engineering, 186, pp.537-543.
[45] H. Hassan, A. I. El-Desouky, A. Ibrahim, E. M. El-Kenawy and R. Arnous, "Enhanced QoS-Based Model for Trust Assessment in Cloud Computing Environment," in IEEE Access, vol. 8, pp. 43752-43763, 2020, doi: 10.1109/ACCESS.2020.2978452.
[46] Elhosuieny, A., Salem, M., Thabet, A., & Ibrahim, A. (2019). ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model. International Journal of Web Services Research (IJWSR), 16(4), 53-73. doi:10.4018/IJWSR.2019100104
[47] Hussein, S.E., & Badr, S.M. (2013). Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression. International Journal of Computer Applications, 80, 9-15.
[48] Jeddi, S. and Sharifian, S., 2019. A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing. Cluster Computing, 22(4), pp.1397-1412.
[49] Narayanan, L. and Sankaranarayanan, S., 2019. IoT-based water demand forecasting and distribution design for smart city. Journal of Water and Climate Change.
[50] USGS Water Data for the Nation. (2020). Retrieved 23 October 2020, from https://waterdata.usgs.gov/nwis
[51] Water Quality Data (WQX) | US EPA. (2020). Retrieved 23 October 2020, from https://www.epa.gov/waterdata/water-quality-data-wqx
[52] Managing water data. (2020). Retrieved 23 October 2020, from https://www.unenvironment.org/explore-topics/water/what-we-do/monitoring-water-quality/managing-water-data
[53] Real-time water data. (2020). Retrieved 23 October 2020, from https://realtimedata.waternsw.com.au/
[54] Water Resources Management. (2020). Retrieved 23 October 2020, from https://ieee-dataport.org/open-access/water-resources-management