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مدل‌سازی هدررفت خاک ناشی از فرسایش خندقی با استفاده از مدل‌های جنگل تصادفی و ماشین‌بردار پشتیبان و ارزیابی کارایی آنها | ||
| نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
| دوره 78، شماره 3، مهر 1404، صفحه 363-383 اصل مقاله (1.53 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jrwm.2025.390477.1804 | ||
| نویسندگان | ||
| سید مسعود سلیمان پور* 1؛ امید رحمتی2؛ صمد شادفر3؛ مریم عنایتی1 | ||
| 1بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع‌طبیعی استان فارس، سازمان تحقیقات، آموزش و ترویج کشاورزی، شیراز، ایران | ||
| 2بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع‌طبیعی استان کردستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، سنندج، ایران | ||
| 3پژوهشکده حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران ایران | ||
| چکیده | ||
| اندازه‌گیری میدانی میزان هدررفت خاک ناشی از فرسایش خندقی، بسیار زمان‌بر و هزینه‌بر بوده بنابراین اندازه‌گیری مستقیم فرسایش خندقی در سطوح وسیع فرآیندی زمان‌بر، هزینه‌بردار و طاقت‌فرسا است. به این منظور، پژوهش حاضر، نسبت به انجام این مهم از طریق مدل‌سازی هدررفت خاک ناشی از فرسایش خندقی با استفاده از مدل‌های یادگیری ماشینی جنگل تصادفی و ماشین‌بردار پشتیبان و ارزیابی کارایی آنها در حوزه آبخیز ماهورمیلاتی واقع در جنوب‌غرب استان فارس اقدام کرد. در طی چهار سال (1399 لغایت 1402)، اندازه‌گیری‌های میدانی پارامترهای ابعادی 70 خندق انجام شد. در فرآیند مدل‌سازی، 15 عامل محیطی، به‌عنوان متغیرهای مستقل و میزان هدررفت خاک خندق‌ها به‌عنوان متغیر وابسته در نظر گرفته شدند و مدل‌سازی با رویکرد اعتبارسنجی متقاطع انجام شد. دقت مدل‌ها با استفاده از معیارهای کمی خطای جذر میانگین مربعات (RMSE)، ضریب تعیین (R2)، ریشه مربعات خطا (RSR) و همبستگی تطابق (d) مورد بررسی قرار گرفت. میزان هدررفت خاک خندق‌ها در دوره مورد مطالعه 15300/94 تن بود. نتایج ارزیابی دقت پیش‌بینی مدل‌ها نشان داد مدل جنگل تصادفی از نظر معیارهای ارزیابی، نسبت به مدل ماشین‌بردار پشتیبان از عملکرد بهتری برخوردار بود و به‌عنوان مدل برتر برای پیش‌بینی میزان هدررفت خاک ناشی از فرسایش خندقی معرفی شد. یافته‌ها نشان داد "مدل‌سازی" می‌تواند در صرفه‌جویی وقت و هزینه، خدمات ارزنده‌ای به مدیریت حفاظت آب و خاک ارائه دهد. به این منظور پیشنهاد می‌شود استفاده از مدل‌های مبتنی بر هوش مصنوعی و ساختار یادگیری ماشینی در پژوهش‌های آینده مورد توجه بیشتری قرار گیرد. | ||
| کلیدواژه‌ها | ||
| فرسایش خندقی؛ مدل سازی؛ هدررفت خاک؛ هوش مصنوعی؛ یادگیری ماشینی | ||
| عنوان مقاله [English] | ||
| Modeling soil loss due to gully erosion using random forest and support vector machine models and evaluating their efficiency | ||
| نویسندگان [English] | ||
| Seyed Masoud Soleimanpour1؛ Omid Rahmati2؛ Samad Shadfar3؛ Maryam Enayati1 | ||
| 1Soil Conservation and Watershed Management Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran | ||
| 2Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Sanandaj, Iran | ||
| 3Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran | ||
| چکیده [English] | ||
| Field measurements of soil loss due to gully erosion are very time-consuming and costly, so direct measurement of gully erosion at large scales is a time-consuming, costly, and labor-intensive process. For this purpose, the present study attempted to accomplish this by modeling soil loss due to gully erosion using random forest and support vector machine learning models and evaluating their efficiency in the Mahurmilati watershed located in the southwest of Fars province. Field measurements of dimensional parameters of 70 gullies were conducted over four years (2021 to 2024). In the modeling process, 15 environmental factors were considered as independent variables and the rate of soil loss in ditches as the dependent variable, and modeling was performed with a cross-validation approach. The accuracy of the models was evaluated using quantitative criteria such as root mean square error (RMSE), coefficient of determination (R2), root mean square error (RSR), and correlation coefficient (d). The rate of soil loss in gullies during the study period was 15300.94 tons. The results of the model prediction accuracy evaluation showed that the random forest model has better performance than the support vector machine model in terms of evaluation criteria and was introduced as the superior model for predicting the rate of soil loss due to gully erosion. The findings showed that "modeling" can provide valuable services to water and soil conservation management in saving time and money. For this purpose, it is suggested that the use of artificial intelligence-based models and machine learning structures be given more attention in future research. | ||
| کلیدواژه‌ها [English] | ||
| Artificial intelligence, gully erosion, machine learning, modeling, soil loss | ||
| مراجع | ||
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سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب