مقایسه و تلفیق الگوریتم‌های یادگیری ماشین و شیء پایه برای غربالگری عوامل زمینه‌ساز و تهیه نقشه طبقه‌بندی زمین‌لغزش

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت حوزه‌های آبخیز، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

2 نویسنده مسئول، استاد گروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران.

3 استاد بخش علوم خاک، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

4 دانشیار گروه مدیریت مناطق بیابانی، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران.

چکیده

چکیده
سابقه و هدف: زمین‌لغزش‌ به‌عنوان یکی از مخرب‌ترین مخاطرات طبیعی پس از زلزله، خسارات جبران‌ناپذیری به محیط‌زیست و زیرساخت‌ها وارد می‌کند. ایران به‌دلیل شرایط زمین‌شناختی و اقلیمی خاص خود، هرساله شاهد وقوع زمین‌لغزش‌های متعددی است که نیازمند مطالعات دقیق و اقدامات پیشگیرانه است. در مطالعه حاضر یک روش تلفیقی با استفاده از قابلیت و توانایی الگوریتم‌های یادگیری ماشین برای انتخاب ویژگی‌های مؤثر در شناسایی زمین‌لغزش و مقایسه الگوریتم‌های شیء پایه جهت تهیه نقشه طبقه‌بندی زمین‌لغزش با استفاده از تصاویر ماهواره‌ای گائوفن-1 توسعه داده شد. درنهایت نمودار وابستگی جزئی زمین‌لغزش‌ها با هر یک از متغیرهای مستقل استخراج‌شده ترسم گردید.
مواد و روش‌ها: به‌منظور شناسایی زمین‌لغزش‌های حوزه آبخیز محمدآباد گلستان از دو تصویر ماهواره گائوفن-1 مربوط به اسفند 1401 و خرداد 1402 استفاده شد. به علت متفاوت بودن فصول دو تصویر، تمام پردازش‌های دو تصویر به‌صورت مجزا صورت گرفت. در اولین مرحله 218 زمین‌لغزش با استفاده از بازدید میدانی شناسایی گردید که 70 درصد آن برای آموزش مدل و 30 درصد باقیمانده برای ارزیابی نتایج استفاده شد. طبقه‌بندی تصاویر ماهواره‌ای و استخراج کلاس‌های لغزش و فاقد لغزش بااستفاده‌از طبقه‌بندی شیء پایه انجام شد؛ که شامل دو مرحله قطعه‌بندی و طبقه‌بندی است. بعد از انجام مرحله قطعه‌بندی تصویر با الگوریتم قطعه‌بندی چندمقیاسه، انتخاب ویژگی‌ با استفاده از سه الگوریتم شبکه عصبی، درخت تصمیم و جنگل تصادفی انجام شد و عوامل مؤثر بر شناسایی زمین‌لغزش با استفاده از تصاویر ماهواره‌ای از هر کدام از قطعات استخراج گردید سپس ویژگی‌های انتخاب‌شده بر اساس ویژگی هم‌خطی بررسی شدند. در مرحله بعد، این ویژگی‌ها در طبقه‌بندی و شناسایی زمین‌لغزش با چهار الگوریتم شیء پایه ماشین بردار پشتیبان، نزدیک‌ترین همسایه، درخت تصمیم و جنگل تصادفی مورداستفاده قرار گرفتند سپس عملکرد الگوریتم‌ها با شاخص‌های صحت کلی، ضریب کاپا، ضریب سورنسن، نرخ مثبت صحیح و نرخ مثبت کاذب مقایسه شد و مناسب‌ترین الگوریتم برای شناسایی زمین‌لغزش با استفاده از تصویر ماهواره‌ای انتخاب شد.
یافته‌ها: نتایج انتخاب ویژگی نشان داد از بین سه روش‌ مورد بررسی در این مطالعه، الگوریتم جنگل تصادفی با دقت بالاتری ویژگی‌های مؤثر در شناسایی زمین‌لغزش با استفاده از تصاویر ماهواره‌ای را تعیین کرد. بررسی طبقه‌بندی زمین‌لغزش‌ها با چهار الگوریتم شیءگرا ماشین بردار پشتیبان، درخت تصمیم، جنگل تصادفی و نزدیک‌ترین همسایه نشان داد، الگوریتم ماشین بردار پشتیبان با صحت بالای 92 درصد و ضریب کاپا بالای 85/0 به نسبت سایر الگوریتم‌ها شناسایی زمین‌لغزش‌های منطقه مورد مطالعه را به‌خوبی انجام داده است.
نتیجه‌گیری: مطالعه حاضر نشان داد که استفاده از قابلیت و توانایی الگوریتم‌های یادگیری ماشین و تلفیق آن‌ با الگوریتم‌های شیء پایه می‌تواند به‌عنوان روش قابل‌اعتماد برای شناسایی سریع و ارزان زمین‌لغزش‌ها با استفاده از تصاویر ماهواره‌ای مورداستفاده قرار گیرد. شناسایی زمین‌لغزش‌ها اولین گام مطالعه این مخاطره طبیعی است به‌طوری‌که نتایج آن می‌تواند در بهبود برنامه‌ریزی، مدیریت و کاهش خسارات ناشی از زمین‌لغزش‌ به مدیران امر و کارشناسان اجرایی کشور کمک کنند. به‌منظور انجام مطالعات آتی پیشنهاد می‌گردد که تصاویر با قدرت تفکیک مکانی بالا تهیه شود تا با جزئیات بالا بتوان به شناسایی زمین‌لغزش‌ها اقدام نمود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Comparison and integration of machine learning and object-based algorithms for screening underlying factors and preparing a landslide classification map

نویسندگان [English]

  • Marzieh Nikjoui 1
  • Ali Najafinejad 2
  • Hamid Reza Pourghasemi 3
  • Chooghi Bairam Komaki 4
1 Ph.D. Student in Watershed Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2 . Corresponding Author, Professor, Dept. of Watershed Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan
3 Professor, Dept. of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran.
4 Associate Prof., Dept. of Arid Regions Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
چکیده [English]

Abstract
Background and objectives: Landslides, among the most destructive natural hazards following earthquakes, cause irreversible damage to both the environment and infrastructure. Due to the unique geological and climatic conditions, Iran experiences numerous landslides annually, necessitating the needs for careful studies and preventive measures. This study presents an integrated approach that leverages the capabilities of machine learning algorithms to identify effective features for landslide detection. Furthermore, it compares object-based algorithms to generate a landslide classification map utilizing Gaofen-1 satellite images. Eventually, the study includes partial dependence plot illustrating the relationship between landslides and various independent conditional factors.
Materials and methods: To identify landslides in the Mohammadabad watershed of Golestan, two Gaofen-1 satellite images from March 2023 and June 2024 were employed. Due to seasonal variations between the images, all processing was conducted separately. In the first phase, 218 landslides were identified through field visits, with 70% of these used for model training and the remaining 30% reserved for evaluating the results. The classification of satellite images and the extraction of landslide and non-landslide classes were performed using basic object-oriented classification approach involving two stages: segmentation and classification. Following image segmentation with a multi-scale segmentation algorithm, feature selection was conducted using three algorithms: neural networks, decision trees, and random forests. Factors influencing landslide identification were extracted from each segment based on the satellite images, and collinearity among features was assessed. Subsequently, these features were employed in the classification and identification of landslides using four object-based algorithms: support vector machine, nearest neighbor, decision tree, and random forest. The performance of these algorithms was compared using overall accuracy indices, Kappa coefficient, Sorensen coefficient, true positive rate, and false positive rate. Ultimately, the most effective algorithm for landslide detection using satellite imagery was determined.
Findings: The results of the feature selection analysis indicated that out of the three methods examined in this study, the random forest algorithm identified the most effective features for landslide detection using satellite images. The classification of landslides utilizing four object-oriented algorithms, support vector machine, decision tree, random forest, and nearest neighbor, revealed that the support vector machine algorithm achieved the highest accuracy of 92% and a Kappa coefficient of 0.85. This performance showed its superior capability in identifying landslides within the studied area compared to the other algorithms.
Conclusion: This study showed that the integrating of machine learning algorithms with object-based methods provides a reliable and cost-effective approach for the rapid identification of landslides using satellite images. Identifying landslides is an important step in studying this natural hazard, as the findings can offer valuable insights to managers and practitioners to enhance planning and management strategies to mitigate landslide-induced damages. For future study, utilizing high-resolution images is recommended to enable more detailed landslide identification.

کلیدواژه‌ها [English]

  • landslide
  • basic object
  • data mining
  • random forest
  • support vector system

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مجله پژوهش‌های حفاظت آب و خاک
دوره 32، شماره 1
فروردین 1404
صفحه 31-55

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