A Decision Support System for the Prediction of Wastewater Pumping Station Failures Based on CBR Continuous Learning Model
Abstract
Nowadays the communities are facing the problem of waste and wastewater. While wastewater systems have become more complex, the need for development of sustainable solution for wastewater management emerged. Therefore, the development of a Decision Support System (DSS) for wastewater disposal management became necessary. This paper presents a new DSS for predicting the failure of wastewater pumping stations, the system architecture and its implementation. The prediction model is based on the Case Based Reasoning (CBR) classification method. The standard CBR classification technique has been upgraded with an algorithm for continuous learning. The paper describes the system structure, its connection to the wastewater system, the internal processes involved in the prediction process and the implemented algorithm for continuous learning. Furthermore, the features used in the prediction are indicated along with the achieved results and the method of results evaluation. The test and obtained results indicate that the proposed DSS is efficient and capable of providing very good results in the prediction process.
Keywords:
case based reasoning, continuous learning, decision support system, prediction, wastewater pumping stationDownloads
References
M. A. Hamouda, W. B. Anderson, P. M Huck, “Decision support systems in water and wastewater treatment process selection and design: a review”, Water Science & Technology, Vol. 60, No. 7, pp. 1757-1770, 2009 DOI: https://doi.org/10.2166/wst.2009.538
A. S. Patil, N. J Kulkarni, “Decision support system for waste water management: a review”, International Journal of Innovative Research in Advanced Engineering, Vol. 1, No. 3, pp. 24-29, 2014
A. K. Bhavsar, J. S. Shah, “Cloud based decision support system for waste-water management using supervised decision tree algorithm”, International Journal of Computer Sciences and Engineering, Vol. 5, No. 10, pp. 367-372, 2017 DOI: https://doi.org/10.26438/ijcse/v5i10.367372
Z. Zhang, X. He, A. Kusiak, “Data-driven minimization of pump operating and maintenance cost”, Engineering Applications of Artificial Intelligence, Vol. 40, pp. 37–46, 2015 DOI: https://doi.org/10.1016/j.engappai.2015.01.003
J. Z. Ren, H. W. Liang, “Multi-criteria group decision-making based sustainability measurement of wastewater treatment processes”, Environmental Impact Assessment Review, Vol. 65, pp. 91-99, 2017 DOI: https://doi.org/10.1016/j.eiar.2017.04.008
W. X. Hu, “The application of artificial neural network in wastewater treatment”, IEEE 3rd International Conference on Communication Software and Networks, Xi'an, China, May 27-29, 2011
Y. Guo, J. Hu, Y. Peng, “Research on CBR system based on data mining”, Applied Soft Computing, Vol. 11, No. 8, pp. 5006–5014, 2011 DOI: https://doi.org/10.1016/j.asoc.2011.05.057
M. M. Richter, R. Weber, “Case-Based Reasoning: A Textbook”, in: Basic CBR Elements, Springer Science & Business Media, 2013, pp. 17-34 DOI: https://doi.org/10.1007/978-3-642-40167-1_2
H. Huang, H. Qin, Z. Hao, A. Lim, “Example-based learning particle swarm optimization for continuous optimization”, Information Sciences, Vol. 182, No. 1, pp. 125-138, 2012 DOI: https://doi.org/10.1016/j.ins.2010.10.018
F. P. A. Lima, M. L. M. Lopes, A. D. P. Lotufo, C. R. Minussi, “An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems”, Expert Systems with Applications, Vol. 56, pp. 131-142, 2016 DOI: https://doi.org/10.1016/j.eswa.2016.03.010
E. F. Vazquez, “Updating weighting matrices by Cross-Entropy”, Investigaciones Regionales, Vol. 21, pp. 53- 69, 2011
A. Bonzano, P. Cunningham, B. Smyth, “Using introspective learning to improve retrieval in CBR: A case study in air traffic control”, in: Case-Based Reasoning Research and Development, Springer, 2005
C. K. Riesbeck, R. C. Schank, Inside Case-Based Reasoning, Psychology Press, 2013 DOI: https://doi.org/10.4324/9780203781821
S. Bernard, C. Chatelain, S. Adama, R. Sabourin, “The multiclass ROC front method for cost-sensitive classification”, Pattern Recognition, Vol. 52, pp. 46-60, 2015 DOI: https://doi.org/10.1016/j.patcog.2015.10.010
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Abstract Views: 591PDF Downloads: 380 Fig_1 - The architecture of wastewater pumping decision support system (WPDSS) Downloads: 0 Fig.2 - WPDSS internal processes Downloads: 0 Fig.3 - ROC curve of WPDSS performance Downloads: 0
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