نوع مقاله : علمی - پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه زنجان

2 دانشکده آب و خاک، دانشگاه زابل

چکیده

امروزه با توجه به خشکسالی های اخیر و گرمایش زمین مدل سازی و پیش بینی پارامترهای اقلیمی امری اجتناب ناپذیر به نظر م یرسد که در میان روش های موجود استفاده از الگوریتم های داده کاوی در پییش بینی عناصر اقلیمی کاربرد وسیعی پیدا کرده است، لذا در این پژوهش دو الگوریتم داده کاویCART و CHAID درپیش بینی دمای هوای ایستگاه سینوپتیک اراک مورد ارزیابی قرار گرفتند. داده های هواشناسی شامل متوسط ساعات آفتابی، دمای نقطه شبنم، درصد رطوبت نسیبی، سرعت باد، کمبود فشار بخار اشباع طی دوره چهل وشش ساله (2335-1993) مورد استفاده قرار گرفته شد. پس از معرفی  داده های هواشناسی  به صورت متوسط ماهانه به عنوان متغییرهای ورودی و متوسط دمای ماهانه ماه بعد به عنوان متغییرهای خروجی ی بیه دو الگیوریتم میذکور، 2 الگوریتم ها توسط دو شاخص آمیاری ریریه همبسیتگی ( R (و متوسیط مطلق خطا (MAE) مورد ارزیابی قرار گرفتند . بر اساس دو شاخص آماری مدل درختی CHAID با مقدار 915.0 = R و 77.2 = MAE عملکرد بهتری در پیش بینی دمای متوسط ماه بعد نسبت به الگیوریتم CART داشته است.

کلیدواژه‌ها

عنوان مقاله [English]

Assessment among two data mining algorithms CART and CHAID in forecast air temperature of the synoptic station of Arak

نویسندگان [English]

  • Mehdi Panahi 1
  • Seyyed Hassan Mirhashemi 2

1 Department of Water Engineering, Faculty of Agriculture, University of Zanjan, Zanjan

2 Faculty of Water and Soil, University of Zabol, Zabol

چکیده [English]

Due to recent droughts and global warming, in the modeling and prediction of climatic parameters it seems inevitable that the approach of using data mining algorithms to predict climatic elements has been widely used .Therefore, in this study the use of the CARD and CHAID data mining algorithms to predict the air temperature at Arak synoptic stations was evaluated. The data provided are the average monthly data from the Arak weather station, including: “sunshine hours”, “dew point”, “relative humidity”, “average wind speed” and “saturation vapor pressure deficit” over a forty-six-year period from 1960 to 2005. The output variable used was “average temperature months later” on a monthly basis. After introducing the weather data as a monthly mean to the algorithm as an input variable of air temperature months later. Then to the CART and CHAID algorithms were evaluated using correlation coefficient (R2) and mean absolute error (MAE). According to the two statistical indices, the CHIAD Tree model has a better function by R2 =0.915 and MAE= 2.77 in forecasting the monthly average temperature for the months later.

کلیدواژه‌ها [English]

  • Assessment
  • Tree Model
  • CART
  • CHAID
  • Average Temperature Months
  • Synoptic Station of Arak

Babazadeh H, Shamsnia SA, Bostani F, Norozi eghdam A, Khodakaramidehkordi D. Evaluation of drought, wet and prediction of Shiraz climatic parameters precipitation and temperature by using stochastic methods. Journal of Geography and Urban Planning; 2012;16 (41):23-47. [In Persian]

Sfandiari F, Hosseini SA, Azadimobaraki M, Hejazizadeh Z. Predict the average monthly temperature in Sanandaj station using the model (MLP) MLP neural network.Iran Geographic; 2010;8(27):45- 65. [In Persian]

Bootsma A. Long-term (100 years) climate trends for agriculture at selected locations in Canada. Climatic Change, 1994; 26: 65–88.

Plummer N, Salinger M J, Nicholls N, Suppiah R, Hennessy K J, Leighton R M, Trewin B, Page C M, Lough J M. Changes in climate extremes over the Australian region and New Zealand during the twentieth century. Climatic Change; 1999; 42: 183–202.

Heino R, Brazdil R, Forland E, Tuomenvirta H, Alexandersson H, Beniston M, Pfister C, Rebetez M, Rosenhagen G, Rösner S, Wibig J. Progress in the study of climatic extremes in northern and central Europe. Climatic Change; 1999; 42: 151–181.

Khalili A. Report of the National Water Master Plan, the synthesis report, the weather, the possible effect of climate change on water resources. Ministry of Energy; 2001.[In Persian]

Shahabfar A, Mohammadnia gharaii S, Javedani Khalifa N, The time variations of frost days in Mashhad. Proceedings of the Third Regional Conference and the National Conference on Climate Change; 2004: 74-81.

Wang B, Zhang M, Wei J, Wang S, Li S, Ma Q, Li X, Pan S. Changes in extreme events of temperature and precipitation over Xinjiang, northwest China, during 1960–2009. Quaternary International; 2013;17(298):141-151.

Balling JRRC, Idso SB. Effects of greenhouse warming on maximum summer temperatures. Agric for meteoro; 1990; l53:143–147.

Giudici P. Applied data Mining: statistical methods for business and industry. Wily, London; 2003: 364.

Mahmoudi A, Rostami H, Canopy M, Moradi A. A review of the science of data mining and its applications in the offshore industry. Fifth National Conference on offshore industries OIC. Sharif University; 2014. [In Persian]

Vanderberg H, Sogard P, Motoroni S. Mine Set TM 3.0 Enterprise Edition Tutorial for Windows , Silicon Graphics Inc. 1999; Doc. No. 007- 4006-001.

Meshkani A, Nazmi A. Introduction to Data Mining, Islamic Azad University. Neyshabur; 2010:456.[In Persian]

Cabena PH, Stadler R, Verhees J, Zanasi. Discovering data mining: From concept to implementation, IMB, New Jersey; 1998:195.

Pal M, Deswal S. M5 model tree based modeling of reference evapotranspiration. Hydrol Process; 2009; 23:1437-1443.

Shirsath PB, Singh AK. A comparative study of daily pan evaporation estimation using ANN, Regression and climate based models. J. Water resource management; 2010; 24:1571-1581.

Diamantopoulou MJ, Georgiou PE, Papamichial DM. Perforeance evaluation of artificial neural network in estimating reference evapotranspiration with minimal meteorological data. Global nest Journal; 2010;13 (1):18-27.