Abstract
Water scarcity is the major problem presently being faced globally; it is to be managed in an efficient manner. The water management procedure is one of the techniques to condense necessary water. The objective of water management system is that water supply agency to collect, distribute quality water without any delay and scarcity. An intelligent system is necessary for efficient production, collection and distribution. The proposed intelligent system consists of genetic operations with fitness value and neural network for training. The fitness function is used to make new intelligent members from existing population of water resources for water collection and distribution. The system is applicable for prediction about water consumption, distribution using decision-making algorithms to increase optimization performance by calculation of objective function of various population types. The regression performance of proposed intelligent system is calculated and compared with other algorithms.
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References
Abernethy J, Chojnacki A, Farahi A, Schwartz E, Webb J (2018) Active remediation: the search for lead pipes in flint, Michigan. 2018 Association For Computing Machinery. https://doi.org/10.1145/3219819.3219896
Chih-Chiang W (2012) Wavelet kernel support vector machines forecasting techniques: case study on water-level predictions during typhoons. Expert Syst Appl. https://doi.org/10.1016/J.Eswa.2011.11.020
Deng Wu, Zhao Huimin, Zou Li, Li Guangyu, Yang Xinhua, Daqing Wu (2017) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput. https://doi.org/10.1007/S00500-016-2071-8
Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE. https://doi.org/10.1109/access.2019.2897580
Green Accounting and Data Improvement for Water Resources (2019) United Nations Educational, Scientific and Cultural Organization (Unesco), United Nations World Water Assessment Programme (WWAP). http://www.Unesco.Org/New/En/Natural-Sciences/Environment/Water/Wwap/
https://Www.Waterschappen.Nl/Ondanks-Regen-Nog-Altijd-Erg-Droog-In-Nederland/ (2019)
Jac Van Der Gun (2012) Groundwater and global change: trends, opportunities and challenges. United Nations World Water Assessment Programme, Published in 2012 by the United Nations Educational, Scientific and Cultural Organization 7, Place De Fontenoy, 75352 Paris 07 SP, France, ISBN 978-92-3-001049-2
Laith Mohammad QA (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl (IJCSEA)
Laith Mohammad QA (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Studies In Computational Intelligence Book Series. Springer, New York
Laith Mohammad A, Ahamad Tajudin K (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Super Comput. https://doi.org/10.1007/s11227-017-2046-2
Laith Mohammad A, Kr Ahamad Tajudin, Essam Said H (2017) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci. https://doi.org/10.1016/j.jocs.2017.07.018
Laith Mohammad A, Ahamad Tajudin K, Essam Said H (2018a) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell. https://doi.org/10.1007/S10489-018-1190-6
Laith Mohammad A, AhamadTajudin K, Essam Said H (2018) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell
Msiza IS, Nelwamondo FV, Marwala T (2008) Water demand prediction using artificial neural networks and support vector regression. J Comput 3(11)
The Global Runoff Data Centre (2020) http://Grdc.Bafg.De
Zaher MY, Deo RC, Ebtehaj I, Hosseinbonakdari (2018) Hybrid data, intelligent models and applications for water level. IGI Global. https://doi.org/10.4018/978-1-5225-4766-2.ch006
Zhao H, Yang X, Xiong J, Sun M, Li B (2017) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl Soft Comput. https://doi.org/10.1016/J.Asoc.2017.06.004
Zhao U, Zheng J, Xu J, Deng W (2019) Fault diagnosis method based on principal component analysis and broad learning system. IEEE. https://doi.org/10.1109/access.2019.2929094
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Gino Sophia, S.G., Ceronmani Sharmila, V., Suchitra, S. et al. Water management using genetic algorithm-based machine learning. Soft Comput 24, 17153–17165 (2020). https://doi.org/10.1007/s00500-020-05009-0
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DOI: https://doi.org/10.1007/s00500-020-05009-0