تحقیقات مهندسی سازه های آبیاری و زهکشی

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مدل‌سازی هزینه طراحی و اجرای سامانه های آبیاری قطره ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 استاد گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

3 استادیار گروه احیاء مناطق خشک و کوهستانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

این پژوهش با هدف برآورد هزینه پروژه های آبیاری قطره ای در مراحل اولیه طراحی با استفاده از تکنیک برنامه ریزی ژنتیک، با استفاده از داده‌های 100 پروژه آبیاری قطره ای انجام شد. ا مهمترین ویژگی ها که بیشترین تاثیر را در هزینه ها داشتند با استفاده از نرم‌افزار Eureqa و بهره گیری از برنامه ریزی ژنتیک انتخاب شدند. در مرحله آخر مدل های مختلفی در هر بخش برای برآورد هزینه ارائه شده و بر اساس آماره های دقت و پیچیدگی، بهترین مدل در هر بخش معرفی شدند. نتایج آنالیز همبستگی بین متغیرهای مستقل با متغیر وابسته (هزینه هر بخش) نشان داد که در بخش TCP متغیرPP (توان پمپ مورد نیاز)، در بخش TCF متغیر L16mm (طول کل لترال)، در بخش TCI متغیر SR (فاصله ردیف گیاهان) و در بخش TCT متغیر HP (ارتفاع پمپاژ) به ترتیب با ضریب همبستگی (R) برابر با 0/77، 0/64، 0/36 و 0/43 بیشترین مقدار را بین بقیه متغیرها داشته و همگی در سطح اطمینان یک درصد معنی‌دار شدند. همچنین نتایج مدل‌سازی هزینه سامانه آبیاری قطره ای نشان داد که در بخش TCP مدلی با معیار ارزیابی R برابر 0/45، MAE (میانگین قدر مطلق خطا) برابر با 27236333 ریال و پیچیدگی 22 بهترین مدل بوده و این معیارهای ارزیابی برای بهترین مدل در بخش TCF برابر 0/85، 21198257 ریال و 13، در بخش TCI برابر0/77، 45483996 ریال و 11، و در بخش TCT این معیارها به ترتیب 0/74، 77220845 ریال و 15 به دست آمد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Early Stage Cost Modeling of Drip Irrigation Systems

نویسندگان [English]

  • Masoud Pourgholam-Amiji 1
  • AbdolMajid Liaghat 2
  • Khaled Ahmadaali 3

1 Ph.D. Candidate, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

2 Professor, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran

3 Assistant Professor, Department of Arid and Mountainous Regions Reclamation, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

چکیده [English]

Introduction
Allocation of budget to pressurized irrigation projects is supported by the government to manage, saving water resources and increase agricultural production and is considered as the ideal project of the country. Cost-effectiveness and awareness of the cost of an irrigation system is important and necessary for the government. Therefore, estimating the initial and final cost of the project, especially irrigation systems, is one of the project management tools that allow project managers to make more accurate decisions at different stages. Finding a model to identify the important factors affecting the final cost of an irrigation system, as well as formulating it for use throughout the country and regions with different characteristics, is what the present study seeks.

Methodology
The aim of this study was to estimate the cost of drip irrigation projects in the early stages of design using genetic programming technique, using data from 100 drip irrigation projects, in four sections including; cost of pumping station and central control system (TCP), cost of on-farm equipment (TCF), cost of installation and performing on-farm and pumping station (TCI) and total cost (TCT). First, a database containing 39 important and influential variables on the cost of the mentioned sections was prepared and the prices of the projects were updated for the base year of 2019. In the next step, the most important features that had the highest impact on the costs were selected using Eureqa Formulize software and using genetic programming. In the last stage, different models were presented in each section to estimate the cost and the best model in each section was introduced based on the statistics of accuracy and complexity.

Results and Discussion
The results of correlation analysis between independent variables and dependent variable (cost of each section) show that in TCP section PP variable (pump power required), in TCF section L16mm variable (total lateral length), in TCI section SR variable (plant row spacing) and in the TCT section, the HP variable (pumping height) with the correlation coefficient (R) equal to 0.766, 0.638, 0.355 and 0.429, respectively, had the highest value among the other variables and all were significant at the level of one percent confidence. Also, the results of cost modeling for drip irrigation system showed that in the TCP section, a model with an evaluation criterion of R equal to 0.449, MAE (average absolute error value) equal to 27236333 Rials and complexity of 22 was the best model. These evaluation criteria for the best model in the TCF section were equal to 0.848, 21198257 Rials and 13, in the TCI section equal to 0.770, 45483996 Rials and 11, and in the TCT section these criteria were 0.743, 77220845 Rials and 15, respectively.

Conclusions
In this study, the cost estimation of different parts of the drip irrigation system was modeled using genetic programming algorithm, and the obtained results showed that the presented models had excellent accuracies in each part. The results of this study can be a very useful tool for researchers, managers, students, consultants, contractors and those who are concerned in the water industry. By conducting similar research, it is possible to make an economic estimate with a high accuracy before the implementation stage.

Acknowledgement
This article is extracted from the Ph.D. dissertation, the first author of the article. For this purpose, the authors of the article would like to thank the Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran for their excellent cooperation, providing the necessary facilities for this research and the preparation of relevant articles.
Keywords: Pressurized Irrigation, Cost Estimation, Genetic Algorithm, Feature Selection, Eureqa Formulize.

کلیدواژه‌ها [English]

  • Pressurized Irrigation
  • Cost Estimation
  • Genetic Algorithm
  • Feature Selection
  • Eureqa Formulize
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تحقیقات مهندسی سازه های آبیاری و زهکشی
دوره 22، شماره 82 - شماره پیاپی 82
اردیبهشت 1400
صفحه 1-22
فایل ها
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  • تاریخ دریافت: 30 دی 1399
  • تاریخ بازنگری: 09 فروردین 1400
  • تاریخ پذیرش: 16 فروردین 1400
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