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استفاده از روش یادگیری ماشین به‌منظور برآورد تبخیر و تعرق(مطالعه موردی: استان سمنان) | ||
| تحقیقات آب و خاک ایران | ||
| دوره 55، شماره 5، مرداد 1403، صفحه 781-797 اصل مقاله (3.65 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2024.371452.669652 | ||
| نویسندگان | ||
| حسام هروی1؛ علی اصغر ذوالفقاری* 2 | ||
| 1گروه بیابان زدایی، دانشکده کویر شناسی، دانشگاه سمنان، سمنان، ایران | ||
| 2گروه 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 | ||
| واژه‌های کلیدی: ارتفاع؛ دما؛ ERA5-Land؛ مدل جنگل تصادفی(RF) | ||
| عنوان مقاله [English] | ||
| Using Machine Learning Method to Estimate Evapotranspiration (Case Study: Semnan Province) | ||
| نویسندگان [English] | ||
| Hesam Heravi1؛ Ali-Asghar Zolfaghari2 | ||
| 1Department of Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran. | ||
| 2Department 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 | ||
| مراجع | ||
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