ارزیابی مدل جنگل تصادفی برای محاسبه تبخیر و تعرق مرجع با استفاده از داده‌های هواشناسی محدود در منطقه مطالعاتی دشت اردبیل

نوع مقاله : مقاله پژوهشی

نویسندگان

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

چکیده

به دلیل افزایش فشار جهانی بر در دسترس بودن منابع آب، توجه به تلفات آب بیش‌تر از قبل مشخص می‌گردد، تبخیر و تعرق (ET) به عنوان بخش مهمی از تلفات بوده و تخمین آن برای بررسی تغییرات آب و هوا، جلوگیری از آبیاری ناکارآمد و استفاده مناسب از منابع آب، حیاتی است. علی‌رغم مدل‌های تجربی فراوان برای پیش‌بینی ET، هنوز هیچ اجماع جهانی در مورد استفاده از یک مدل تجربی مشخص وجود ندارد. مدل‌های محاسبات نرم به دلیل نیاز به داده‌های کم‌تر، برای جلوگیری از محدودیت‌ مدل‌های تجربی و برای برآورد دقیق‌تر ET توسعه داده‌ شده‌اند. در این تحقیق برای تخمین تبخیر و تعرق مرجع با داده‌های هواشناسی در حدفاصل سال‌های 1385 تا 1401 و در دشت اردبیل کارایی دو مدل جنگل تصادفی (RF) و رگرسیون خطی چندگانه (MLR) ارزیابی شد. برای ساخت مدل، از ترکیبب داده‌های 4 ایستگاه هواشناسی استفاده و از ایستگاه پنجم برای ارزیابی نهایی مدل‌ها استفاده شد. آماره‌های ارزیابی شامل R2، NSE و RMSE بود. نتایج به‌دست آمده برای مدل RF به ترتیب برابر بود با 74/0، 743/0 و 20/8 میلی‌متر که در مقایسه با نتایج مدل MLR از دقت بالاتری برخوردار بود. مطالعه حاضر نشان داد که مدل‌های جنگل تصادفی می‌تواند یک مدل مطمئن با در نظر گرفتن دقت و ثبات، برای پیش‌بینی ETo و با استفاده از مجموعه داده‌های محدود باشد. به طور کلی، با استفاده از نتایج این تحقیق می‌توان گفت که مدل‌های RF، تبخیر و تعرق مرجع را در مناطقی با داده‌های محدود با دقت قابل قبولی شبیه‌سازی می‌کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of Random Forest model to calculate potential Evapotranspiration using limited meteorological data (study area: Ardabil Plain)

نویسندگان [English]

  • javanshir azizi mobaser
  • ali rasoulzadeh
  • Amin Akbari Majd
Department of Water Engineering, Faculty of Agriculture and Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili, Ardabil , Iran
چکیده [English]

As the global demand for water resources increases, the reduction in water loss, including Evapotranspiration, becomes more obvious. Although many models have been developed to predict evapotranspiration, no universally accepted model for all climate regions has been established. Several soft computational models have been created to circumvent the constraints of empirical models and accurately predict ET. Soft computing models typically require less data and are applicable across various climatic zones. This study aimed to analyze how well two Random Forest models and Multiple Linear Regression could predict ETo in the Ardabil plain region. Meteorological data from the Iranian Meteorological Organization were used to calculate the reference evapotranspiration from 2014 to 2016. In constructing the model, data from 4 meteorological stations were combined to generate a random time series, while the fifth station was reserved for evaluating the models. The assessment metrics used comprised RMSE, R2, and NSE. The RF model achieved higher accuracy with R2, NSE, and RMSE values of 0.74, 0.743, and 8.20 mm, respectively, compared to the MLR model. The results demonstrate that random forest models are reliable tools for forecasting ETo with minimal climate data. In general, using the results of this study and other similar research, we conclude that RF and MLR models simulate potential evapotranspiration with acceptable accuracy but are sensitive to the number of input parameters.

کلیدواژه‌ها [English]

  • machine learning
  • multiple linear regression
  • random forest
  • reference evapotranspiration

EXTENDED ABSTRACT

Introduction

Water is necessary for environmental protection and meeting various human needs. Water is used for agricultural, domestic, energy generation, recreational, and industrial purposes (directly) and irrigation of green areas (indirectly). Furthermore, proper ET assessment is critical for controlling agricultural water requirements, monitoring and efficiently using water supplies, and forecasting droughts or similar situations. Despite using multiple empirical models for ET prediction, no consensus exists on the suitability of applying all the proposed models across diverse climate zones. Various soft computing models have been developed to overcome the restrictions associated with empirical models and appropriately estimate ET.

Method and Materials

The measurements were conducted in the Ardabil Plain region and its surrounding basin. In this research, model inputs, which included meteorological data, were sourced from the Iranian Meteorological Organization’s website between 2006 and 2023. The output and target parameters, which included reference evapotranspiration data, were computed every 10 days (first, second, and third decade of each month) using the FAO Penman-Monteith method (FAO56). This research uses two models, multiple linear regression and random forest, to estimate reference evapotranspiration in the Ardabil plain. In this research, the primary approach was to assess the precision of the random forest model in estimating reference evapotranspiration using observational data, which was initially compared in the correlation analysis of 14 meteorological parameters with reference evapotranspiration data. By examining the correlations between the primary parameters, the parameters that displayed a very low correlation with the reference evapotranspiration were removed from the input data list. Finally, the models and the remaining meteorological data with a mixed-random time series were used to compute the reference evapotranspiration. Evaluation statistics such as the Nash-Sutcliffe efficiency coefficient (NSE), R2, and mean square error were used in this study.

Results

From 2006 to 2023, meteorological data were first calculated at five meteorological stations: Nair, Sarein, Ardabil, Ardabil Airport, and Nemin. Using data from four meteorological stations—Nair, Sarein, Ardabil, and Ardabil Airport—a model was developed to understand and establish a relationship between input parameters and reference evaporation and transpiration during the statistical period. Finally, the models were evaluated using data from the Namin station, which had the shortest statistical period and the least complete data among the stations. The final input characteristics were the minimum, maximum, and average temperatures, evaporation, maximum wind direction, daily daylight hours, and soil temperature. A random mix of data from four stations that created a time series with a period of 1600 units was used to train or construct the models in the first stage. The obtained Namin station was then used to evaluate the final model.

The RF model was more accurate in calculating the reference evapotranspiration from the same data, according to the obtained R2, NSE, and RMSE values. The present study also demonstrated that, when considering accuracy and stability, hybrid learning models, such as random forest and random tree models, can be reliable machine learning models for predicting ETo using limited climate datasets for different climatic zones of the Ardabil Plain.

Conclusions

Although using all seven parameters produced the best results, the results showed that the parameters of soil temperature, maximum temperature, average temperature, minimum temperature, wind speed direction, hours of radiation, and evaporation made up the best-suggested combination. Temperature parameters are more important when estimating reference evapotranspiration using the Penman-Monteith technique, according to the constant coefficients obtained from the MLR model at the model's input. Additionally, the RF model outperformed the MLR model regarding prediction accuracy among the models included in the study.

Author Contributions

A.A.M.: Writing – original draft, Formal analysis, Conceptualization, Data curation, Methodology, Validation, Writing – review & editing. J.A.M.: Writing – review & editing. A.R.: Writing – review & editing.

Data Availability Statement

Not applicable

Acknowledgments

The authors would like to thank all participants of the present study.

 

Ethical considerations

The Ethics Committee of the Mohaghegh Ardabili University approved the study. The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.

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دوره 56، شماره 2
اردیبهشت 1404
صفحه 545-569

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