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مدل‌سازی پایداری خاکدانه‌های خیس بر اساس جنگل تصادفی بهینه‌شده با الگوریتم ژنتیک | ||
| تحقیقات آب و خاک ایران | ||
| دوره 55، شماره 7، مهر 1403، صفحه 1095-1111 اصل مقاله (1.99 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2024.376443.669712 | ||
| نویسندگان | ||
| ساناز منور سابق1؛ داود زارع حقی1؛ سعید صمدیان فرد* 2؛ حسین رضائی3 | ||
| 1گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران | ||
| 2گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران | ||
| 3گروه علوم و مهندسی خاک، دانشگده کشاورزی، دانشگاه تبریز، تبریز، ایران | ||
| چکیده | ||
| مطالعه وضعیت پایداری خاکدانه‌های خیس (WAS)، به‌عنوان شاخصی رایج از ساختمان خاک و نیز ارزیابی کیفیت آن، برای مدیریت بهینه منابع خاک و آب، حائز اهمیت است. در پژوهش حاضر، برای مدل‌سازی پایداری خاکدانه‌های خیس از مدل‌های یادگیری ماشین جنگل تصادفی (RF) و جنگل تصادفی بهینه‌شده با الگوریتم ژنتیک (GA-RF) استفاده شد. بدین منظور، ویژگی‌های بافت، ماده آلی و آهک 55 نمونه خاک از جنگل‌های ارسباران تعیین و سپس با ترکیب‌های ورودی مختلف بر اساس مقادیر همبستگی با پارامتر WAS، مدل‌سازی با استفاده از هفت سناریو انجام شد. به‌منظور تعیین توانایی مدل‌های اجرا شده، سه شاخص عملکرد ضریب همبستگی (CC)، جذر میانگین مربعات خطای نرمال شده (NRMSE) و ضریب ویلموت (WI) مورد استفاده قرار گرفت. نتایج نشان داد که مدل RF5 در بین مدل‌های جنگل تصادفی با 038/0NRMSE =، 736/0CC = ، 789/0WI = و مدل GA-RF5 در بین مدل‌های جنگل تصادفی بهینه‌شده با الگوریتم ژنتیک با 031/0NRMSE = ، 800/0CC = ، 842/0WI = با ورودی درصد شن و سیلت و رس، بهترین عملکرد را داشتند. علاوه‌براین نتایج RF1 ) 047/0NRMSE = ، 589/0CC = ، 721/0WI = ( و GA-RF1 ) 036/0NRMSE = ، 662/0CC = ، 797/0WI = ( نشان داد که درصد رس بالاترین درجه همبستگی را با پایداری خاکدانه‌ها دارد. همچنین، با اضافه شدن کربنات کلسیم معادل در سناریو 7، بهبود عملکرد و تأثیر مثبت این ویژگی در پیش‌بینی پایداری خاکدانه‌های خیس مشاهده گردید. بنابراین، مدل جنگل تصادفی بهینه‌شده با الگوریتم ژنتیک برای تعیین دقیق و مناسب پایداری خاکدانه‌های خیس در مطالعات مربوط به خصوصیات خاک توصیه می‌گردد. | ||
| کلیدواژه‌ها | ||
| الگوریتم ژنتیک؛ جنگل تصادفی؛ پایداری خاکدانه‌های خیس | ||
| عنوان مقاله [English] | ||
| Wet aggregate stability modeling based on random forest optimized with genetic algorithm | ||
| نویسندگان [English] | ||
| Sanaz Monavvar Sabegh1؛ Davoud Zarehaghi1؛ Saeed Samadianfard2؛ Hossein Rezaei3 | ||
| 1Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran | ||
| 2Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran | ||
| 3Soil Science and Engineering Department, Agriculture Faculty, University of Tabriz, Tabriz, Iran | ||
| چکیده [English] | ||
| In order to effectively manage soil and water resources, it is imperative to investigate wet aggregate stability (WAS) as a fundamental indicator for assessing soil structure and quality. In this study, machine learning techniques, specifically random forest (RF) and random forest optimized with genetic algorithm (GA-RF), were employed. The analysis focused on determining the texture, organic matter content, and lime characteristics of 55 soil samples collected from the Arsbaran forests. Utilizing various input combinations based on correlations with WAS, modeling was performed across seven distinct scenarios. Furthermore, three performance metrics including correlation coefficient (CC), normalized root mean square error (NRMSE), and Wilmot coefficient (WI) were utilized to evaluate the effectiveness of the models. The findings indicated that the RF5 model exhibited superior performance among the random forest models, achieving NRMSE = 0.038, CC = 0.736, and WI = 0.789. Similarly, the GA-RF5 model, optimized through a genetic algorithm approach, demonstrated exceptional performance with NRMSE = 0.031, CC = 0.800, and WI = 0.842 when considering input percentages of sand, silt, and clay. Moreover, results from RF1 (NRMSE = 0.047, CC = 0.589, WI = 0.721) and GA-RF1 (NRMSE = 0.036, CC = 0.662, WI = 0.797) emphasized that clay content exhibited the strongest correlation with stability. Additionally, the incorporation of calcium carbonate equivalent in scenario 7 significantly enhanced model performance and positively influenced the prediction of wet aggregate stability. In summary, the hybrid model combining random forest with a genetic algorithm is recommended for precise and reliable determination of wet aggregate stability in studies focusing on soil properties. | ||
| کلیدواژه‌ها [English] | ||
| Genetic algorithm, random forest, wet aggregate stability | ||
| مراجع | ||
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