ارائه مدل ترکیبی هوشمند تعیین روش مشارکت عمومی- خصوصی صنعت آب و فاضلاب ایران بر مبنای الگوریتمهای جمعی درختی
محورهای موضوعی : مدیریت شهری
ملیحه اسکندری
1
,
سید محمد تقی تقوی فرد
2
,
ایمان رئیسی وانانی
3
,
سروش قاضی نوری
4
1 - دانشگاه علامه طباطبائی
2 - دانشگاه علامه طباطبائی
3 - دانشگاه علامه طباطبایی
4 - علامه طباطبایی
کلید واژه: دادهکاوی پیشبینی حذف داده پرت روشهای ترکیبی شاخص سود اطلاعاتی گرادیان بوستینگ جنگل تصادفی,
چکیده مقاله :
دسترسی به آب سالم و دفع بهداشتی فاضلاب از ارکان توسعه هر کشور است و ضروری است که این طرحها در کوتاهترین زمان تکمیل گردد. با توجه به ظهور انواع روشهای مشارکتی، انتخاب روش مناسب، یکی از مسائل مهم بوده و این صنعت نیازمند مدلی جهت تصمیمگیری در خصوص نحوه و روش سرمایهگذاری در منطقه و یا طرح خاص است. با توجه به وجود پایگاه داده از پروژههای مشارکتی در بخش آب و فاضلاب، میتوان با استفاده از اطلاعات بهدستآمده و الگوریتمهای کشف الگو و تصمیمگیری دادهکاوی، مدل سرمایهگذاری و روش مناسب اجرای پروژه را پیشبینی نمود. این تحقیق با استفاده از دادههای ۱۷۶ پروژه و بهره گیری از فرآیند کریسپ در دادهکاوی انجام شده است. پس از تشریح و درک داده، مراحل پاکسازی و حذف داده پرت اجرا و در مرحله دستهبندی با تکنیک های درختی و یادگیری ماشین، طبقهبندی موفقیت و شکست پروژهها و تحلیل های لازم انجام گرفته و شاخصهای مشارکت عمومی- خصوصی به ترتیب اولویت استخراج گردید. بر مبنای یافتههای مدلسازی، روش ترکیبی COF5 جهت حذف داده پرت و شاخص آنتروپی برای انتخاب ویژگی و روش استکینگ با دقت ۸۶.۲۷٪، مدل پیشنهادی جهت پیشبینی موفقیت پروژهها هستند . با توجه به مدل پیشنهادی، میتوان علاوه بر معرفی قالب قراردادی مناسب اجرای هر گروه از پروژههای بخش آب و فاضلاب، میزان موفقیت هر طرح را در هر یک از قالب های قراردادی پیشبینی و تأثیر بهبود هر یک از شاخصها را بررسی نمود.
One of the pillars of any country’s development is the access to safe water and sanitation and so, these projects should be implemented in the shortest possible time. In this regard, considering the existence and emergence of various methods of private sector participation, choosing the right approach has become one of the most important issues in this industry. The decision makers always need a model for selection of the best public-private partnership method in specific region or project. Using the database of partnership projects, existing information, and pattern and data mining algorithms in the water and wastewater sector, we have designed a public-private partnership (PPP) model to predict and propose a proper way to execute such projects. In this research, CRISP data mining method was applied to the data from 176 projects. After describing and understanding the data, the purging and deletion steps were performed. In the process of classification with tree techniques and machine learning, the classification and success of the projects were applied to the data and necessary analysis were performed. Based on the results, the indicators of public-private participation were extracted and prioritized. Based on the research findings, the combined COF method for deleting outliers, Entropy indices for feature selection, and Stacking methods are applied to predict project success or failure with an accuracy of 86.27%. According to the proposed model, one can easily predict the success rate of each of the contractual templates in addition to introducing the appropriate contractual template for any water and wastewater project by entering information on each new project and examine the impact of the improvement of each of the indicators.
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