مقایسۀ کارایی مدل‌های شبکۀ عصبی مصنوعی، منطق فازی و جنگل تصادفی در برآورد پارامتر قابلیت انتقال آبخوان دشت ملکان

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکتری هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز، تبریز، ایران

2 دانشیار هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز، تبریز، ایران

3 استاد هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز، تبریز، ایران

چکیده

آبخوان دشت ملکان به عنوان یکی از آبخوان‏های حوضۀ دریاچۀ ارومیه، به مدیریت صحیح کمی و کیفی نیاز دارد. روش‏های مختلفی از جمله انجام آزمون پمپاژ، روش‏های آزمایشگاهی، استفاده از ردیاب‏ها و روش‏های ژئوفیزیکی برای ارزیابی پارامترهای هیدروژئولوژیکی و مدیریت مناسب آبخوان‏ها وجود دارد. هر چند تعبیر و تفسیر داده‏های به‌دست‌آمده از آزمون پمپاژ، بهترین روش تخمین پارامترهای هیدروژئولوژیکی آبخوان است، اما این روش‏ پرهزینه، وقت‏گیر و نتایج آن مختص به مناطق محدودی خواهد بود. با توجه به اینکه مدل‏های هوش مصنوعی توانایی‏هایی در برآورد پارامترهای هیدروژئولوژیکی نشان داده‏اند، در تحقیق حاضر کارایی مدل‏های شبکه‏های عصبی مصنوعی، منطق فازی و جنگل تصادفی در برآورد پارامتر قابلیت انتقال آبخوان دشت ملکان بررسی شده‏ است. پارامترهای ژئوفیزیکی و هیدروژئولوژیکی مرتبط با قابلیت انتقال، از جمله مقاومت عرضی، هدایت الکتریکی، ضخامت آبخوان و هدایت هیدرولیکی به عنوان مهم‏ترین ورودی در این مدل‏ها در نظر گرفته شده ‏است. بر اساس نتایج به‌دست‌آمده از بین مدل‏های شبکۀ عصبی و فازی و جنگل تصادفی، مدل جنگل تصادفی ‌دقت و توانایی بیشتری در شبیه‏سازی داشته‏ است. نتایج به‌دست‌آمده از شبیه‏سازی ( 96/0 = AUC، 001/0 = MSE و 986/0= R2) و تعیین مهم‏ترین پارامترهای تأثیرگذار در پیش‏بینی قابلیت انتقال، گویای برتری این مدل نسبت به مدل‏های شبکه‏های عصبی مصنوعی و منطق فازی در بحث پیش‏بینی است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparing Performans of Fuzzy Logic, Artificial Neural Network and Random Forest Models in Transmissivity Estimation of Malekan Plain Aquifer

نویسندگان [English]

  • hossein norouzi 1
  • Ata Allah Nadiri 2
  • Asghar Asghari Moghaddam 3
  • Maryam Norouzi 1
1 tabriz university
2 Assistant Professor of Natural Faculty, University of Tabriz
3 Department of Earth Sciences, Faculty of Natural Scineces
چکیده [English]

Estimating aquifer hydrogeological parameters is essential for the studies or management of groundwater resources. There are several different methods such as pumping test, simulation or modeling of groundwater, geophisical modeling to estimate these parameters. Although analysis and evaluation of pumping test data is the best way to achieve this purpose, it is costly, time consuming and the gained results are from limited points. Malekan plain aquifer is one of the marginal plains of Urmia Lake which suffered more ground water declination and Salinization in last decades and it needs qualitative and quantitative management. In this study, artificial neural networks, fuzzy logic and random forest models have been used to estimate the transmission of aquifers and the performance each of these models has been investigated. Inputs of presented models included related geophysical and hydrogeological variables to transmissivity such as transverse resistivity (Rt), electric conductivity (EC), alluvium thickness (B), and hydraulic conductivity (k). Based on the results of all models, random forest model has higher accuracy and ability to predict transmissivity parameter. According to this model, electrical conductivity (EC), aquifer environment (A) and hydraulic gradient (H) parameters were identified as the most important parameters to predict the transmissivity, respectively.

کلیدواژه‌ها [English]

  • Artificial Intelligence
  • Groundwater
  • Malekan Plain
  • Random forest
  • Transmissivity
[1]. Chow VT. On the determination of transmissibility and storage coefficient from pumping test data. Transactions. American Geophysical Union. 1952; 33(3): 397-404.
[2]. Cooper H, Jacob C E. A generalized graphical method for evaluation formation constants and summarizing well field history. Transactions, American Geophysical Union. 1946; 27(4): 526-534.
[3]. Neuman SP. Theory of flow in unconfined aquifers considering delayed response of water table. Journal of Water Resources Research. 1972; 8(4): 1031-1045.
[4]. Theis CV. The relationship between the lowering of piezometric surface and the rate and duration of discharge of a well using groundwater storage. Transactions, American Geophysical Union. 1935; 16(2): 519-524.
[5]. Samani N, Gohari-Moghadam M, Safavi AA. A simple neural network model for the determination of aquifer parameters. Journal of Hydrology. 2007; 340(1-2): 1-11.
[6]. Nadiri AA, Chitsazan N, Frank TC, Moghaddam A. Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation. Journal of Hydrologic Engineering. 2014; 19(3): 520- 532.
[7]. Chen CH, Lin Z. A committee machine with empirical formulas for permeability prediction. Journal of Computers and Geosciences. 2006; 32: 485–496.
[8]. Chitsazan N, Nadiri AA, Tsai F. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical bayesian model averaging. Journal of Hydrology. 2015; 528: 52-62.
[9]. Kadkhodaie A, Amini A. A fuzzy logic approach to estimation hydraulic flow units from well log data: case study from the Ahvaz oilfield in south Iran. Journal of Petroleum Geology. 2009; 32(1): 67-78 67.
[10]. Kadkhodaie A, Rezaee MR, Rahimpour-Bonab H. A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf. Journal of Petroleum Science and Engineering. 2009a; 65: 23-32.
 
[11]. Nadiri AA, Asghari Moghaddam A, Tsai F, Fijani E. Hydrogeochemical analysis for Tasuj plain aquifer, Iran. Journal of Earth System Science. 2013; 122(4): 1091-1105.
[12]. Pulido CI, Gutiérrez JC. Improved irrigation water demand forecasting using a soft computing hybrid model. Journal of Biosystems Engineering. 2009; 102(2): 202-218.
[13]. Rodriguez V, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sánchez J.P. An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS Journal of Photogram Remote Sens. 2012d; 67: 9 -104.
[14]. Breiman L. Random Forests. Machine Learning. 2001; 45(1): 5–32.
[15]. Yoo W, Ference BA, Cote ML, Schwartz A. A Comparison of Logistic Regression, Logic Regression, Classification Tree, and Random Forests to Identify Effective Gene-Gene and Gene-Environmental Interactions. International Journal of Applied Science and Technology. 2012; 2(7): 268-274.
[16]. Norouzi H, Asghari Mogaddam A, Nadiri AA. Determining vulnerable areas of Malekan Plain Aquifer for Nitrate, Using Random Forest method. Journal of Environmental Studies. 2015; 41(4): 923-94. [In Persian]
[17]. Hopfield JJ. Neural network and physical systems with emergent collective computational abilities. Proc. Nat, Academy of scientists. 1982; 79: 2554-2558.
[18]. Demuth H, Beale M. Neural Network Toolbox User, s Guide, By the Math Works. Inc Version. 2000; 4: 840pp.
[19]. ASCE. Task Committee on Application of Artificial Neural Networks in Hydrology, Part I and II. Journal of Hydrology. 2000; 5(2): 115-137.
[20]. Chiu S. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems. 1994; 2(4): 267–278.
[21]. Nikravesh M, Aminzadeh F. Soft Computing and Intelligent Data Analysis in Oil Exploration. Part1: Introduction: Fundamentals of Soft Computing. Elsevier, Berkeley, USA. 2003; pp.744.
[22]. Quinlan JR. Induction of decision trees. Journal of Machine Learning. 1986; 1(1): 81-106.
[23]. Schapire R. The strength of weak learnability. Journal of Machine learning, 1990; 5:197-227.
[24]. Kotsiantis S, Pintelas P. Combining bagging and boosting. International Journal of Computational Intelligence. 2004; 1(4): 324–33.
[26]. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees, Chapman & Hall/CRC, New York. 1984; pp.744.
[26]. Quinlan JR. C4.5 programs for machine learning. San Mateo, CA: Morgan Kaurmann. 1993; 303 pp.
[27]. Breiman L. Bagging predictors. Machine Learning. 1996; 24(2): 123–40.
[28]. Bellman R. Dynamic programming. Mineola, NY: Dover Publications. 2003; 366 pp.
[29]. Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Res. 2003; 3: 1157–82.
[30]. Dixon BA. Case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N. Journal of Hydrogeology. 2010; 17(6): 1507–20.
[31]. Critto A, Carlon C, Marcomini A. Characterization of contaminated soil and groundwater surrounding an illegal landfill by principal component analysis and kriging. Journal of Environmental Pollution. 2003; 122(2): 235–44.
[32]. Harb N, Haddad K, Farkh S. Calculation of transverse resistance to correct aquifer resistivity of groundwater saturated zones, implications for estimating its hydrogeological properties. Lebanese science journal. 2010; 11(1): 105-115.
[33]. Valcarce RM, Rodríguez WM. Resolution power of well log geophysics in karst aquifers. Journal of Environmental Hydrology. 2004; 12: 1-7.
[34]. Lehmann P, Davis. Evaporation and capillary coupling across vertical textural contrasts in porous media. Journal of Phys, Rev. 2009; 80(4): 44-57
[35]. Chehata N, Guo L, Mallet C. Airborne lidar feature selection for urban classification using random forests. International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences. 2009; 39: 207-12.