مدل ترکیبی هوشمند تعیین روش مشارکت عمومی- خصوصی صنعت آب و فاضلاب ایران بر مبنای الگوریتم‌های جمعی درختی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دکترای مدیریت فناوری اطلاعات، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران

2 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران

3 استادیار، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران

چکیده

دسترسی به آب سالم و دفع بهداشتی فاضلاب از ارکان توسعه هر کشور بوده و ضروری است که این طرح‌ها در کوتاه‌ترین زمان ممکن تکمیل شود. از سوی دیگر با توجه به ظهور انواع روش‌های مشارکت عمومی- خصوصی، انتخاب روش مناسب مشارکت، یکی از مسائل مهم بخش آب و فاضلاب بوده و این صنعت همواره نیازمند مدلی برای تصمیم‌گیری در خصوص نحوه و روش سرمایه‌گذاری در منطقه و یا طرح خاص بوده است. با توجه به وجود پایگاه داده از اطلاعات پروژه‌های مشارکتی در بخش آب و فاضلاب، می‌توان با استفاده از داده‌های به‌دست ‌آمده و الگوریتم‌های کشف الگو و تصمیم‌گیری داده‌کاوی، مدل سرمایه‌گذاری و روش مناسب اجرای پروژه را تعیین کرد. در این پژوهش با بهره‌گیری از داده‌های ۱۷۶ پروژه مشارکتی بخش آب و فاضلاب و استفاده از روش اجرای پروژه‌های داده‌کاوی یعنی فرایند کریسپ، مدل مشارکت عمومی- خصوصی صنعت آب و فاضلاب استخراج شد. پس از تشریح و درک داده، مراحل پاک‌سازی و حذف داده‌های پرت اجرا شده است. در مرحله دسته‌بندی، با تکنیک‌های درختی و یادگیری ماشین، طبقه‌بندی موفقیت و شکست پروژه‌ها و تحلیل‌های لازم انجام و شاخص‌های مشارکت عمومی- خصوصی به‌ترتیب اولویت استخراج شد. به‌منظور اعتبار‌سنجی و تقسیم داده‌ها، از روش اعتبارسنجی ضربدری استفاده شد. بر مبنای مدل‌سازی انجام شده و با در نظر گرفتن انواع روش‌های پیش‌پردازش و داده‌کاوی، روش استکینگ با دقت 27/86 درصد، به‌عنوان روش مناسب پیش‌بینی و تعیین نوع قرارداد مشارکت عمومی- خصوصی در اجرای هر پروژه‌ بخش آب و فاضلاب معرفی شد. در بخش پیش‌پردازش نیز روش ترکیبی Connectivity-based Outlier Factor برای حذف داده پرت و شاخص آنتروپی برای انتخاب ویژگی استفاده شد. با توجه به مدل پیشنهادی، علاوه بر معرفی قالب قراردادی مشارکتی مناسب برای اجرای هر گروه از پروژه‌های بخش آب و فاضلاب و معرفی قالب قراردادی مناسب در هر استان، می‌توان میزان موفقیت هر طرح را در هر یک از قالب‌های قراردادی پیش‌بینی و تأثیر بهبود هر یک از شاخص‌ها را در افزایش موفقیت پروژه بررسی کرد.

کلیدواژه‌ها


عنوان مقاله [English]

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

نویسندگان [English]

  • Malihe Eskandary 1
  • MohammadTaghi Taghavifard 2
  • Iman Raeesi Vanani 3
  • Soroush Ghazi Noori 3
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
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Water and Wastewater Industry
  • public-private partnership
  • Data mining
  • Forecasting
  • Outsourcing
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