An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms

Document Type : Research Paper


1 PhD of Information Technology Management, Dept. of Industrial Management, College of Management and Accounting, Allameh Tabataba’I University, Tehran, Iran

2 . Assoc. Prof., Dept. of Industrial Management, College of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran

3 Assist. Prof., Dept. of Industrial Management, College of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran


One of the pillars of any country’s development is access to safe water and sanitation, so it is important to execute water and wastewater projects in the shortest possible time. In this regard, considering the emergence of various methods of partnership, choosing the right approach has become one of the most important issues in this industry. Therefore, a proper investment method in this field has always been the concern of decision makers. Using the database of partnership projects and data mining algorithms in the water and wastewater sector, we have designed a model to predict a proper way for public-private partnership projects. In this research, CRISP data mining method was applied to the data from 176 projects. After understanding and identifying the data, they were cleaned by deleting outliers and noisy data, and missing values were replaced. Then, the process of data classification was performed using decision tree and machine learning algorithms, and necessary analysis was performed. Also, the indicators of PPP were extracted and prioritized. K-fold cross validation method is used for validation and dividing the data. Based on the modeling and considering the data preparations and data mining methods, the stacking method is suitable for predicting and determining the type of public-private partnership contract in the implementation of each project of water and wastewater industry, which has an accuracy of 86.27%. In the pre-processing section, the combined COF method for deleting outliers and entropy factors for feature selection was used. Using the model, the success rate of each project can be predicted and an appropriate PPP contractual template for any water and wastewater project can be proposed. In addition, by entering the information of each new project, the impact of the improvement of each indicator can be easily examined.


Akintoye, A., Hardcastle, C., Beck, M., Chinyio, E. & Asenova, D. 2003. Achieving best value in private finance initiative project procurement. Construction Management and Economics, 21, 461-470.
Al-Radaideh, Q. A. & Al Nagi, E. 2012. Using data mining techniques to build a classification model for predicting employees performance. International Journal of Advanced Computer Science and Applications, 3(2), 144-151.
Alegre, H. & Association, I. W. 2006. Performance indicators for water supply services, IWA publishing. London, UK.
Ali, M. & Qamar, A. M. 2013. Data analysis, quality indexing and prediction of water quality for the management of rawal watershed in Pakistan. 8th International Conference on Digital Information Management (ICDIM 2013), Islamabad, Pakistan, IEEE, 108-113.
Azhar, S. A. S., Johar, H., Baki, S. R. M. S. & Tahir, N. M. 2013. Optimization of water quality monitoring based on fuzzy algorithms. IEEE Conference on Systems, Process and Control (ICSPC, 2013), IEEE, 283-288.
Beikzadeh, M. R., Phon-Amnuaisuk, S. & Delavari, N. 2008. Data mining application in higher learning institutions. Informatics in Education-An International Journal, 7(1), 31-54.
Böhl, C. G. P. 2007. Development of a knowledge based decision support system for private sector participation in water and sanitation utilities, Oldenbourg Industrieverlag, Munichen, Germany.
Chen, W. S. & Du, Y. K. 2009. Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications, 36, 4075-4086.
Cheng, H., Lu, Y. C. & Sheu, C. 2009. An ontology-based business intelligence application in a financial knowledge management system. Expert Systems with Applications, 36, 3614-3622.
Company, N. W. A. W. 2009. National wastewater financing and investment strategy. Ministry of Energy, Tehran, Iran. (In Persian)
Cui, C., Liu, Y., Hope, A. & Wang, J. 2018. Review of studies on the public–private partnerships (PPP) for infrastructure projects. International Journal of Project Management, 36, 773-794.
Delmon, J. 2015. Creating a framework for public-private partnership programs: a practical guide for decision-makers, World Bank, Washington, DC, USA.
Freund, Y. & Schapire, R. E. 1995. A desicion-theoretic generalization of on-line learning and an application to boosting. European Conference on Computational Learning Theory, Bertinoro, Italy, Springer, 23-37.
Golabchi, M. & Nourzaei, E. A. 2015. Selecting the best PPP method in rail projects by using AHP methods. Journal of Transportation Engineering, 6(3), 523-537. (In Persian)
Hall, D., Lobina, E. & Motte, R. D. L. 2005. Public resistance to privatisation in water and energy. Development in Practice, 15, 286-301.
Hodge, G. A., Greve, C. & Boardman, A. 2010. International handbook on public–private partnerships, Edward Elgar Publishing, UK.
Jenabi, G. & Mirroshandel, S. A. 2014. Using data mining techniques for improving customer relationship management. European Online Journal of Natural and Social Sciences: Proceedings, 2, 3143-3149.
Kirimi, J. M. & Moturi, C. A. 2016. Application of data mining classification in employee performance prediction. International Journal of Computer Applications, 146, 28-35.
Lavrač, N., Bohanec, M., Pur, A., Cestnik, B., Debeljak, M. & Kobler, A. 2007. Data mining and visualization for decision support and modeling of public health-care resources. Journal of Biomedical Informatics, 40, 438-447.
Li, B., Akintoye, A., Edwards, P. J. & Hardcastle, C. 2005. Critical success factors for PPP/PFI projects in the UK construction industry. Construction Management and Economics, 23, 459-471.
Li, X., Zhu, Z. & Pan, X. 2010. Knowledge cultivating for intelligent decision making in small & middle businesses. Procedia Computer Science, 1, 2479-2488.
Marzouk, M. & Fayez, E. 2018. Public private partnership projects: concessionaire performance measurement. Journal of Al Azhar University Engineering Sector, 13, 466-480.
Mcquaid, R. W. & Scherrer, W. 2010. Changing reasons for public–private partnerships (PPPs). Public Money and Management, 30, 27-34.
Mutula, S. M. & Van Brakel, P. 2006. E-readiness of SMEs in the ICT sector in Botswana with respect to information access. The Electronic Library, 24, 402-417.
Ogwueleka, T. C. & Ogwueleka, F. N. 2010. Data mining application in predicting Cryptosporidium spp. oocysts and Giardia spp. cysts concentrations in rivers. Journal of Engineering Science and Technology, 5, 342-349.
Qiao, L., Wang, S. Q., Tiong, R. L. & Chan, T. S. 2001. Framework for critical success factors of BOT projects in China. The Journal of Structured Finance, 7, 53-61.
Qiu, Y., Li, J., Huang, X. & Shi, H. 2018. A feasible data-driven mining system to optimize wastewater treatment process design and operation. Water, 10, 1342.
Ribeiro, D., Sanfins, A. & Belo, O. 2013. Wastewater treatment plant performance prediction with support vector machines. Industrial Conference on Data Mining, 2013. Springer, 99-111.
Rokach, L. 2010. Ensemble-based classifiers. Artificial Intelligence Review, 33, 1-39.
Sachs, T., Tiong, R. & Qing Wang, S. 2007. Analysis of political risks and opportunities in public private partnerships (PPP) in China and selected Asian countries: survey results. Chinese Management Studies, 1, 126-148.
Salman, A. F., Skibniewski, M. J. & Basha, I. 2007. BOT viability model for large-scale infrastructure projects. Journal of Construction Engineering and Management, 133, 50-63.
Sun, J., Wang, R., Wang, X., Yang, H. & Ping, J. 2014. Spatial cluster analysis of bursting pipes in water supply networks. Procedia Engineering, 70, 1610-1618.
Wen, Y. Y., Huang, W. M., Wu, J., Chen, Y. & Song, J. Q. 2013. Water consumption analysis system based on data mining. Applied Mechanics and Materials, 241-244, 1093-1097.
Yu, I., Kim, K., Jung, Y. & Chin, S. 2007. Comparable performance measurement system for construction companies. Journal of Management in Engineering, 23, 131-139.
Yuan, J., Skibniewski, M. J., Li, Q. & Zheng, L. 2009. Performance objectives selection model in public-private partnership projects based on the perspective of stakeholders. Journal of Management in Engineering, 26, 89-104.
Yuan, J., Wang, C., Skibniewski, M. J. & Li, Q. 2011. Developing key performance indicators for public-private partnership projects: questionnaire survey and analysis. Journal of Management in Engineering, 28, 252-264.
Zhang, X. 2005. Critical success factors for public–private partnerships in infrastructure development. Journal of Construction Engineering and Management, 131, 3-14.