ارزیابی یادگیری ماشین و سنجش از دور در تخمین تبخیر و تعرق مرجع

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

2 'گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

3 گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

10.22059/jwim.2025.383457.1182

چکیده

تبخیر و تعرق مرجع (ETo) یکی از اساسی‌ترین متغیرها در تعیین نیاز آبی محصول و برنامه‌ریزی و طراحی سامانه‌های آبیاری است. مدل‌های یادگیری ماشین برای رفع محدودیت‌های مدل‌های تجربی و برآورد تبخیر تعرق (ET) توسعه داده‌ شده‌اند. در سال‌های اخیر، محاسبه  ET با استفاده از فناوری‌های سنجش از دور توسعه و بهبود یافته است. در این مطالعه برای تخمین تبخیر و تعرق مرجع در  دشت اردبیل کارایی سه مدل جنگل تصادفی(RF)، رگرسیون خطی چندگانه (MLR) و ماشین بردار پشتیبان (SVM) بررسی شد. اطلاعات هواشناسی برای داده‌های مدل از سال 1385 تا 1401 از طریق سنجش از دور و ایستگاه‌های سینوپتیک دریافت شد. ETo به‌عنوان پارامتر هدف، با استفاده از روش فائو پنمن-مانتیث در محدوده پنج ایستگاه سینوپتیک محاسبه شد. در مرحله ساخت و آموزش مدل‌ها، سری‌زمانی پارامترهای ورودی و هدف در محل چهار ایستگاه سینوپتیک و با تشکیل یک سری زمانی تصادفی و ترکیبی از کل داده‌ها انجام و در مرحله ارزیابی نهایی مدل، تنها از داده‌های ایستگاه پنجم استفاده شد. آماره­های ارزیابی مورداستفاده، ، NSE و RMSE بودند. نتایج حاصل برای شاخص‌های آماری، برای مدل RF به‌ترتیب برابر با 7/0، 558/0 و 76/10، برای SVM برابر 71/0، 1- و 6/13 و برای MLR 71/0، 688/0- و 21 بود که با مقایسه نتایج، مدل RF نسبت به سایر مدل‌ها از دقت بالاتری برخوردار بود. مطالعه حاضر نشان داد که مدل‌ جنگل تصادفی می‌تواند یک مدل مطمئن و نسبتاً دقیق برای پیش‌بینی ETo با استفاده از داده‌های RS برای مناطق فاقد آمار باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Assessment of Machine Learning and Remote Sensing in Quantifying Reference Evapotranspiration

نویسندگان [English]

  • javanshir azizi mobaser 1
  • ali rasoulzadeh 2
  • amin akbari majd 3
1 Department of Water Engineering, Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili, Ardabil, Iran.
2 Department of Water Engineering, Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili, Ardabil, Iran.
3 Department of Water Engineering, Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili, Ardabil, Iran.
چکیده [English]

Estimating crop water requirements and designing irrigation systems effectively depends heavily on determining evapotranspiration. Machine learning models have been developed to estimate evapotranspiration (ET) and circumvent the limitations of empirical models. In recent years, ET estimate has been improved and refined through the use of remote sensing technology. Assessment of remote sensing and machine learning for determining reference evapotranspiration This study examined the effectiveness of three models for estimating reference evapotranspiration in the Ardabil plain: random forest (RF), multiple linear regression (MLR), and support vector machine (SVM). From 2006 to 2023, synoptic stations and remote sensing provided meteorological data for the model. The FAO Penman-Monteith method was used to calculate ETo, the target parameter, within a five-synoptic station range. The time series of input and target parameters were recorded at the four synoptic stations during the model's construction and training phases. A random time series and a combination of all the data were then used in the model's final evaluation phase, which only used the data from the fifth station. R2, NSE, and RMSE were the evaluation statistics that were employed. The RF model's statistical index results were 0.7, 0.558, and 10.76, the SVM's were 0.71, -1, and 13.6, and the MLR's were 0.71, -0.688, and 21. Comparing the outcomes, it was found that the RF model was more accurate than the others. The current study demonstrated that, for areas lacking statistics, the random forest model can be a dependable and reasonably accurate model for predicting ETo using RS data.

کلیدواژه‌ها [English]

  • Machine Learning
  • MLR
  • Reference Evapotranspiration
  • RF
  • SVM
  • Ungagged catchment

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مدیریت آب و آبیاری
دوره 15، شماره 1
خرداد 1404
صفحه 180-205

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