ارزیابی مدل درختی M5 و شبکة عصبی مصنوعی برای برآورد متوسط روزانة دمای هوا بر اساس داده‌های دمای سطح زمین سنجنده مودیس

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد گروه مهندسی آبیاری و زهکشی پردیس ابوریحان دانشگاه تهران

2 دانشیار گروه مهندسی آبیاری و زهکشی پردیس ابوریحان دانشگاه تهران

3 استادیار پژوهشکدة حفاظت خاک و آبخیزداری

چکیده

استفاده از داده‏های تصاویر ماهواره‏ای روشی‏ مؤثر برای پهنه‌بندی دمای هواست. در این تحقیق مدل‏های شبکة عصبی و مدل درختی M5 برای تبدیل دمای سطح زمین در دو زمان روز و شب محصولات ماهوارة ترا سنجندة مودیس برای استان خوزستان مقایسه شد. در کل 365 تصویر از محصولات دمای سطح زمین در سال 2007، که منطقة مورد مطالعه را پوشش می‏داد، استفاده شد. داده‏های متوسط دمای هوای روزانه از 29 ایستگاه هواشناسی سینوپتیک و کلیماتولوژی سال 2007 جمع‌آوری و به منزلة داده‏های واقعی استفاده شدند. داده‏های ورودی مدل‏ها شامل دمای سطح زمین در دو زمان روز و شب و تابش بیرون زمینی بودند. نتایج نشان داد ضریب تعیین هر دو مدل بیش از 96/0 است. با این حال مدل شبکة عصبی با دقت بیشتری دمای هوا را برآورد می‏کند. جذر مربع میانگین خطا و ضریب تعیین مدل شبکة عصبی به‌ترتیب برابر 7/1 درجة سانتی‌گراد و 97/0 برآورد شد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

An Evaluation of M5 Model Tree vs. Artificial Neural Network for Estimating Mean Air Temperature as Based on Land Surface Temperature Data by MODIS-Terra Sensor

نویسندگان [English]

  • Saeed Emamifar 1
  • Ali Rahimikhoob 2
  • Ali Akbar Noroozi 3
1 Former Graduate Student of Irrigation and Drainage Engineering Department, Aburaihan Campus, University of Tehran, Tehran, Iran
2 Associate Professor of Irrigation and Drainage Engineering Department, Aburaihan Campus, University of Tehran, Tehran, Iran
3 Assistant Professor of Soil Conservation & Watershed Management Research Center, Tehran, Iran
چکیده [English]

The use of satellite data in an estimation of air temperature (Ta) near the earth’s surface has turned into an effective way for a large area of high spatial and temporal resolution. Throughout the present study, Artificial Neural Network (ANN) as well as M5 model tree were employed to estimate Ta in Khuzestan Province (South West of Iran), using satellite remotely sensed land surface temperature (Ts) data acquired through the MODIS-Terra sensor. The input variables for the models consisted of the daytime and nighttime MODIS Ts as well as extraterrestrial solar radiation. A total of 365 images of MOD11A1 Ts product for the year 2007, covering the area under study were collected from the Land Processes Distributed Active Archive Center (LP DAAC). The results indicated that coefficient of determination (R2) for both models exceeded 0.96. However, ANN model estimations of air temperature were more accurate than RMSE with the respective R2 values of 1.7 and 0.97 oC.

کلیدواژه‌ها [English]

  • Air Temperature
  • Artificial Neural Network
  • Land surface temperature
  • MODIS Sensor
  • M5 model tree

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