An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms
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 o...
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| Format: | Article |
| Language: | English |
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Water and Wastewater Consulting Engineers Research Development
2021-03-01
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| Series: | آب و فاضلاب |
| Subjects: | |
| Online Access: | http://www.wwjournal.ir/article_128011_9f3c8ea4714120f7b8524bb2c9d455dc.pdf |
| _version_ | 1818718767005302784 |
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| author | Malihe Eskandary MohammadTaghi Taghavifard Iman Raeesi Vanani Soroush Ghazi Noori |
| author_facet | Malihe Eskandary MohammadTaghi Taghavifard Iman Raeesi Vanani Soroush Ghazi Noori |
| author_sort | Malihe Eskandary |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T19:56:16Z |
| format | Article |
| id | doaj.art-406af40021bb40d9896de3b27a2efeaf |
| institution | Directory Open Access Journal |
| issn | 1024-5936 2383-0905 |
| language | English |
| last_indexed | 2024-12-17T19:56:16Z |
| publishDate | 2021-03-01 |
| publisher | Water and Wastewater Consulting Engineers Research Development |
| record_format | Article |
| series | آب و فاضلاب |
| spelling | doaj.art-406af40021bb40d9896de3b27a2efeaf2022-12-21T21:34:36ZengWater and Wastewater Consulting Engineers Research Developmentآب و فاضلاب1024-59362383-09052021-03-01321699010.22093/wwj.2020.211331.2970128011An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree AlgorithmsMalihe Eskandary0MohammadTaghi Taghavifard1Iman Raeesi Vanani2Soroush Ghazi Noori3PhD of Information Technology Management, Dept. of Industrial Management, College of Management and Accounting, Allameh Tabataba’I University, Tehran, Iran. Assoc. Prof., Dept. of Industrial Management, College of Management and Accounting, Allameh Tabataba’i University, Tehran, IranAssist. Prof., Dept. of Industrial Management, College of Management and Accounting, Allameh Tabataba’i University, Tehran, IranAssist. Prof., Dept. of Industrial Management, College of Management and Accounting, Allameh Tabataba’i University, Tehran, IranOne 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.http://www.wwjournal.ir/article_128011_9f3c8ea4714120f7b8524bb2c9d455dc.pdfwater and wastewater industrypublic-private partnershipdata miningforecastingoutsourcing |
| spellingShingle | Malihe Eskandary MohammadTaghi Taghavifard Iman Raeesi Vanani Soroush Ghazi Noori An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms آب و فاضلاب water and wastewater industry public-private partnership data mining forecasting outsourcing |
| title | An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms |
| title_full | An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms |
| title_fullStr | An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms |
| title_full_unstemmed | An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms |
| title_short | An Intelligent Hybrid Model for Determining Public-Private Partnership in Iranian Water and Wastewater Industry Based on Collective Tree Algorithms |
| title_sort | intelligent hybrid model for determining public private partnership in iranian water and wastewater industry based on collective tree algorithms |
| topic | water and wastewater industry public-private partnership data mining forecasting outsourcing |
| url | http://www.wwjournal.ir/article_128011_9f3c8ea4714120f7b8524bb2c9d455dc.pdf |
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