A systematic mapping of water quality prediction using computational intelligence techniques
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- Submitted: July 29, 2015
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Published: May 2, 2016
Abstract
Due to the renewable nature of water, this resource has been treated and managed as if it were unlimited; however, increase the indiscriminate use has brought with it a rapid deterioration in quality; so as predicting water quality has a very important role for many socio-economic sectors that depend on the use of the precious liquid. In this study, a systematic literature mapping was performed about water quality prediction using computational intelligence techniques, including those used to calibrate predictive models in order to improve accuracy. Based on research questions formulated in the systematic mapping, a gap is identified oriented to creation of an adaptive mechanism for predicting water quality that can be applied in different water uses without raised the accuracy of the predictions is affected.
References
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[30] A. M. Baltar y D. G. Fontane, “A generalized multiobjective particle swarm optimization solver for spreadsheet models: application to water quality”, Proc. Twenty Sixth Annu. Am. Geophys. Union Hydrol. Days, pp. 20-22, 2006.
[31] A. Afshar, H. Kazemi, y M. Saadatpour, “Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran”, Water Resour. Manag., vol. 25, n.° 10, pp. 2613-2632, ago. 2011.
[32] J. Zhangzan, X. Gang, C. Jiujun, y G. Fei, “Anomaly detection of water quality based on visual perception and V-detector”, Inf. Control, vol. 1, p. 026, 2011.
[33] S. Liu, H. Tai, Q. Ding, D. Li, L. Xu, y Y. Wei, “A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction”, Math. Comput. Model., vol. 58, n.° 3-4, pp. 458-465, ago. 2013.
[34] S. Liu, L. Xu, D. Li, Q. Li, Y. Jiang, H. Tai, y L. Zeng, “Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization”, Comput. Electron. Agric., vol. 95, pp. 82-91, jul. 2013.
[35] D. Ömer Faruk, “A hybrid neural network and ARIMA model for water quality time series prediction”, Eng. Appl. Artif. Intell., vol. 23, n.° 4, pp. 586-594, jun. 2010.
[36] L. A. Díaz-Robles, J. C. Ortega, J. S. Fu, G. D. Reed, J. C. Chow, J. G. Watson, y J. A. Moncada-Herrera, “A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile”, Atmos. Environ., vol. 42, n.° 35, pp. 8331-8340, nov. 2008.
[2] Consejo Económico y Social de Castilla-Mancha, “Estudio La gestión del Agua en Castilla-La Mancha”. 2004.
[3] Comunidad Autónoma de Extremadura, Agentes Forestales de Extremadura. Legislacion Básica Ebook. MAD-Eduforma, 2003.
[4] IDEAM, “Calidad del Agua Superficial en Colombia”, en Estudio Nacional del Agua, 2010, pp. 231-277.
[5] N. Rescher, Predicting the Future: An Introduction to the Theory of Forecasting. SUNY Press, 1998.
[6] E. Kumar, Artificial Intelligence. I.K. International Publishing House Pvt. Limited, 2008.
[7] A. P. Engelbrecht, Computational Intelligence: An Introduction. Wiley, 2007.
[8] B. Kitchenham y S. Charters, “Guidelines for performing Systematic Literature Reviews in Software Engineering”, Keele University and Durham University Joint Report, UK, EBSE 2007-001, 2007.
[9] K. Petersen, R. Feldt, S. Mujtaba, y M. Mattsson, “Systematic Mapping Studies in Software Engineering”, en Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, Swinton, UK, UK, 2008, pp. 68–77.
[10] J. M. Pérez, Inteligencia computacional inspirada en la vida. Servicio de Publicaciones e Intercambio Científico de la Universidad de Málaga, 2010.
[11] J. Grbović y S. Džeroski, “Knowledge discovery in a water quality database”, Proc 1st Intl Conf Knowl. Discov. Data Min. KDD95 AAAI Press Menlo Park CA 1995.
[12] S. Džeroski, D. Demšar, y J. Grbović, “Predicting Chemical Parameters of River Water Quality from Bioindicator Data”, Appl. Intell., vol. 13, n.° 1, pp. 7–17, jul. 2000.
[13] L. Breiman, Classification and regression trees. Chapman & Hall, 1984.
[14] H. Blockeel, S. Dzeroski, y J. Grbovic, Simultaneous prediction of multiple chemical parameters of river water quality with TILDE. 1999.
[15] T. G. Dietterich, “Ensemble Methods in Machine Learning”, en Multiple Classifier Systems, Springer Berlin Heidelberg, 2000, pp. 1-15.
[16] I. Partalas, G. Tsoumakas, E. V. Hatzikos, y I. Vlahavas, “Greedy regression ensemble selection: Theory and an application to water quality prediction”, Inf. Sci., vol. 178, n.° 20, pp. 3867-3879, oct. 2008.
[17] G. Tan, J. Yan, C. Gao, y S. Yang, “Prediction of water quality time series data based on least squares support vector machine”, Procedia Eng., vol. 31, pp. 1194-1199, 2012.
[18] K. Gurney, An Introduction to Neural Networks. Taylor & Francis, 2003.
[19] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
[20] E. Cox, The Fuzzy Systems Handbook: A Practitioner’s Guide to Building, Using, and Maintaining Fuzzy Systems. San Diego, CA, USA: Academic Press Professional, Inc., 1994.
[21] C. E. Romero y J. Shan, “Development of an Artificial Neural Network-based Software for Prediction of Power Plant Canal Water Discharge Temperature”, Expert Syst Appl, vol. 29, n.° 4, pp. 831–838, nov. 2005.
[22] J. Gutiérrez, W. Riss, y R. Ospina, “Bioindicación de la calidad del agua con macroinvertebrados acuáticos en la sabana de Bogotá, utilizando redes neuronales artificiales”, Caldasia, vol. 26, n.° 1, pp. 151-160, 2004.
[23] I. García, J. G. Rodríguez, F. López, y Y. M. Tenorio, “Transporte de contaminantes en aguas subterráneas mediante redes neuronales artificiales”, Inf. Tecnológica, vol. 21, n.° 5, pp. 79-86, 2010.
[24] A. I. Saint-Gerons y J. M. Adrados, “Desarrollo de una red neuronal para estimar el oxígeno disuelto en el agua a partir de instrumentación de EDAR”.
[25] A. Ogata y R. B. Banks, “A solution of the differential equation of longitudinal dispersion in porous media”, 1961.
[26] G. J. Pelletier, S. C. Chapra, y H. Tao, “QUAL2Kw – A framework for modeling water quality in streams and rivers using a genetic algorithm for calibration”, Environ. Model. Softw., vol. 21, n.° 3, pp. 419-425, mar. 2006.
[27] S. Liu, D. Butler, R. Brazier, L. Heathwaite, y S.-T. Khu, “Using genetic algorithms to calibrate a water quality model”, Sci. Total Environ., vol. 374, n.° 2-3, pp. 260-272, mar. 2007.
[28] Y. Huang y L. Liu, “Multiobjective Water Quality Model Calibration Using a Hybrid Genetic Algorithm and Neural Network–Based Approach”, J. Environ. Eng., vol. 136, n.° 10, pp. 1020-1031, 2010.
[29] K. Chau, “A Split-Step PSO Algorithm in Prediction of Water Quality Pollution”, en Advances in Neural Networks – ISNN 2005, J. Wang, X.-F. Liao, y Z. Yi, Eds. Springer Berlin Heidelberg, 2005, pp. 1034-1039.
[30] A. M. Baltar y D. G. Fontane, “A generalized multiobjective particle swarm optimization solver for spreadsheet models: application to water quality”, Proc. Twenty Sixth Annu. Am. Geophys. Union Hydrol. Days, pp. 20-22, 2006.
[31] A. Afshar, H. Kazemi, y M. Saadatpour, “Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran”, Water Resour. Manag., vol. 25, n.° 10, pp. 2613-2632, ago. 2011.
[32] J. Zhangzan, X. Gang, C. Jiujun, y G. Fei, “Anomaly detection of water quality based on visual perception and V-detector”, Inf. Control, vol. 1, p. 026, 2011.
[33] S. Liu, H. Tai, Q. Ding, D. Li, L. Xu, y Y. Wei, “A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction”, Math. Comput. Model., vol. 58, n.° 3-4, pp. 458-465, ago. 2013.
[34] S. Liu, L. Xu, D. Li, Q. Li, Y. Jiang, H. Tai, y L. Zeng, “Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization”, Comput. Electron. Agric., vol. 95, pp. 82-91, jul. 2013.
[35] D. Ömer Faruk, “A hybrid neural network and ARIMA model for water quality time series prediction”, Eng. Appl. Artif. Intell., vol. 23, n.° 4, pp. 586-594, jun. 2010.
[36] L. A. Díaz-Robles, J. C. Ortega, J. S. Fu, G. D. Reed, J. C. Chow, J. G. Watson, y J. A. Moncada-Herrera, “A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile”, Atmos. Environ., vol. 42, n.° 35, pp. 8331-8340, nov. 2008.
How to Cite
Lopez, I. D., Figueroa, A., & Corrales, J. C. (2016). A systematic mapping of water quality prediction using computational intelligence techniques. Revista Ingenierías Universidad De Medellín, 15(28), 35-51. https://doi.org/10.22395/rium.v15n28a2
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