استفاده از روش یادگیری ماشین به‌منظور برآورد تبخیر و تعرق(مطالعه موردی: استان سمنان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه بیابان زدایی، دانشکده کویر شناسی، دانشگاه سمنان، سمنان، ایران

2 گروه بیابان زدایی، دانشکده کویرشناسی، دانشگاه سمنان، سمنان، ایران

چکیده

تبخیر و تعرق (ET) در مناطق خشک و نیمه‌خشک از اهمیت زیادی برخوردار بوده و برآورد دقیق آن در برنامه‌ریزی و مدیریت شیوه‌های آبیاری حائز اهمیت  است. استان سمنان دارای تعداد محدودی ایستگاه سینوپتیک و تبخیرسنجی است که کمبود داده‌های تبخیر و تعرق سبب شده که برآورد مکانی آن با مشکل مواجه گردد. در این مطالعه از محصول تبخیر و تعرق حاصل از داده‌های بازتحلیل ERA5-Land به همراه متغیرهای کمکی ارتفاع و دما برای برآورد تبخیر و تعرق در منطقه موردمطالعه استفاده شد. همچنین به‌منظور برقراری ارتباط بین متغیرهای کمکی و داده‌های تبخیر و تعرق از روش یادگیری ماشین جنگل تصادفی (RF) استفاده شد و نقشه تبخیر و تعرق در منطقه موردمطالعه با استفاده از مدل RF تهیه شد. دقت مدل RF در برآورد تبخیر و تعرق با استفاده از چهار معیار آماری شامل ضریب همبستگی (r)، مقدار اریبی (BIAS)، میانگین ریشه مربعات خطا (RMSE) و شاخص KGE مورد ارزیابی قرار گرفت. نتایج مرحله اعتبارسنجی، کارایی بالای مدل RF را نشان داد (R² = 0.95،  4.1-BIAS= ،  RMSE = 98.6 و KGE = 0.92). همچنین مشخص شد عملکرد مدل RF در برآورد تبخیر و تعرق با استفاده از متغیرهای ورودی به وابستگی خطای مدل (BIAS) به توپوگرافی بستگی دارد و متغیر ارتفاع عامل مهمی در برآورد تبخیر و تعرق محسوب می‌گردد. نتایج این مطالعه نشان داد که استفاده از داده‌کاوی و پردازش در محیط برنامه‌نویسی R، در مناطقی با تعداد محدود ایستگاه‌ هواشناسی، برآورد دقیق میزان تبخیر و تعرق در مناطق خشک و نیمه‌خشک را ممکن می‌سازد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Using Machine Learning Method to Estimate Evapotranspiration (Case Study: Semnan Province)

نویسندگان [English]

  • Hesam Heravi 1
  • Ali-Asghar Zolfaghari 2
1 Department of Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran.
2 Department of Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran
چکیده [English]

Evapotranspiration (ET) plays a crucial role in arid and semi-arid regions, and its precise estimation of ET is essential for effective irrigation planning and management. Semnan province faces challenges due to a scarcity of synoptic and evaporation stations, making spatial estimation of ET difficult. This study utilized the evapotranspiration product from the ERA5-Land reanalysis dataset, in conjunction with auxiliary variables such as elevation and temperature, to estimate ET in the study area. The Random Forest (RF) model was employed to establish the relationship between auxiliary variables and ET data, resulting in the creation of an ET map using the RF model. The accuracy of the RF model in estimating ET was assessed against observational data using four statistical criteria: R², BIAS, RMSE, and KGE. The validation results demonstrated the high efficiency of the RF model (R² = 0.95, BIAS = -4.1, RMSE = 98.6, and KGE = 0.92). It was observed that the RF model's performance in ET estimation is influenced by the relationship between model error (BIAS) and topography, with elevation playing a significant role in ET estimation accuracy. This study underscores the effectiveness of utilizing data mining and processing techniques within the R programming environment to accurately estimate ET in regions with limited weather stations, particularly in arid and semi-arid areas. By leveraging these methods, it becomes possible to enhance the estimation of evapotranspiration in such challenging environments.

کلیدواژه‌ها [English]

  • Keywords: Elevation
  • ERA5-Land
  • Random Forest Model (RF)
  • Temperature

Using Machine Learning Method to Estimate Evapotranspiration

 (Case Study: Semnan Province)

 

EXTENDED ABSTRACT

 

Background and purpose:

Evapotranspiration (ET) plays a crucial role in the hydrological cycle and water management within the agricultural sector, aiding in the estimation of necessary water resources and understanding the impacts of climate change. Additionally, precise estimation of evapotranspiration is vital for effective irrigation planning and management strategies. The objective of this research is to develop a precise annual ET map of Semnan province using the RF model. Furthermore, the study aims to explore the relationship between predicted data and observational data to assess the significance of auxiliary variables in evapotranspiration estimation.

Materials and methods:

Semnan province, spanning 96,816 km2, faces challenges in estimating evapotranspiration (ET) due to a limited number of synoptic and evaporation stations. To address this issue, an alternative data-driven model with minimal data requirements was employed. During the statistical period of 1993-2020, 6 synoptic stations and 5 evaporation stations were utilized for evapotranspiration estimation. Initially, the study utilized the evapotranspiration product from the ERA5-Land reanalysis dataset, along with auxiliary variables such as Digital Elevation Model (DEM) and temperature, to estimate evapotranspiration in the study area. Additionally, the Random Forest (RF) method was employed to establish the connection between auxiliary variables and evapotranspiration data, leading to the creation of an evapotranspiration map using the RF model. Subsequently, the model's accuracy in estimating ET was evaluated by comparing it to observational data using four statistical criteria: Pearson correlation coefficient (R²), root mean square error (RMSE), BIAS, and Kling–Gupta efficiency (KGE).

Findings:

The results of the validation phase demonstrated the high efficiency of the RF model, with values of R² = 0.95, BIAS = -4.1, RMSE = 98.6, and KGE = 0.92. Analysis of the ET data generated by the RF model revealed lower ET levels in the northern regions of the province. In the central to northern areas, average ET levels were observed, correlating with the region's average elevation. Conversely, the highest ET levels were recorded in most of the eastern and southern desert regions of the province. Furthermore, independent analysis of the monitored stations highlighted the RF model's reliance on topography. The elevation variable emerged as a crucial factor in systematically correcting model errors to enhance ET estimation accuracy. These findings underscored the effectiveness of auxiliary variables in a non-linear model like the RF model for accurately estimating complex and spatially variable characteristics such as ET. Notably, the model error (BIAS) of the ERA5-Land reanalysis dataset was rectified as a significant factor in determining model accuracy through the RF algorithm. Unlike the RF model, ERA5-Land employs spatial interpolation, enhancing predictions by incorporating covariates that directly impact ET, such as topographic variables.

Conclusion:     

The results indicated that using RF model with different number of input variables to prepare ET map and processing in R programming environment provides proper accuracy in ET estimation for arid and semi-arid regions. 

 

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تحقیقات آب و خاک ایران
دوره 55، شماره 5
مرداد 1403
صفحه 781-797

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