نشریه محیط زیست طبیعی

پهنه‌بندی حساسیت سیلاب با استفاده از روش‌های یادگیری ماشین بهبودیافته توسط الگوریتم ژنتیک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی نقشه برداری، دانشکده مهندسی نقشه برداری و اطلاعات مکانی، دانشگاه تهران، تهران. ایران.

2 گروه مهندسی عمران آب و سازه‌های هیدرولیکی، دانشکده فنی مهندسی، دانشگاه تهران، تهران، ایران.

3 گروه مهندسی عمران، دانشکده مهندسی، دانشگاه بیرجند، بیرجند، ایران.

4 گروه مهندسی و مدیریت منابع آب، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس، تهران، ایران.

10.22059/jne.2022.350170.2485

چکیده

با توجه به بالا رفتن خطر وقوع سیلاب خصوصاً در سطح شهرها و به ­وجود آمدن خطرات جانی، مالی و محیط ­زیستی ناشی از افزایش آن، پهنه‌بندی مناطق سیل‌خیز از اهمیت بالایی برخوردار است. بنابراین در این مطالعه سعی شد مناطق حساس به سیلاب در دشت بیرجند با استفاده از معیارهای مؤثر پهنه‌بندی شود. در این راستا از روش‌های­ داده‌محور ماشین­ بردار پشتیبان (SVM) و جنگل تصادفی (RF) در ترکیب با الگوریتم ژنتیک جهت پهنه‌بندی مناطق حساس به سیل استفاده ­شد. بنابراین برای پیاده‌سازی و اعتبارسنجی مدل‌های ذکر شده، 42 موقعیت سیل‌خیز در منطقة مورد مطالعه استخراج شد. علاوه ­بر­ این، 19 معیار هیدروژئولوژیکی، توپوگرافی، زمین‌شناسی و محیطی مؤثر بر حساسیت سیلاب در منطقة مورد مطالعه استخراج شدند تا برای پیش‌بینی نقشة حساسیت سیل مورد استفاده قرارگیرند. سطح زیر منحنی (AUC) و انواع شاخص ­های آماری دیگر از جمله ضریب تشخیص (R2) و ریشة میانگین خطای مربعات (RMSE) برای ارزیابی عملکرد مدل‌ها استفاده شد. مقادیر R2، RMSE و AUC حاصل از روش SVM-GA به­ترتیب 0/9032، 0/2751 و 0/931 و روش RF-GA به ترتیب 0/9823، 0/2321 و 0/914 به ­دست ­آمد که نشان‌دهندة سازگاری و دقت بالای مدل RF نسبت به مدل SVM است. هم‌چنین نتایج نشان ­داد که حساسیت سیل به‌دلیل ارتفاع و زاویة شیب کمتر در مناطق مرکزی منطقة مطالعاتی بیشتر از سایر مناطق است. نتایج این مطالعه می‌تواند به‌منظور مدیریت مناطق آسیب‌پذیر و کاهش خسارت‌های سیل مورد استفاده قرار گیرد.

کلیدواژه‌ها

عنوان مقاله [English]

Flood susceptibility zoning using machine learning improved by genetic algorithm

نویسندگان [English]

  • Peyman Karami 1
  • Seyed Ahmad Eslamnezhad 1
  • Mobin Eftekhari 2
  • Mohammad Akbari 3
  • Melika Rastgoo 4

1 Department of Surveying Engineering, Faculty of Surveying Engineering and Spatial Information, University of Tehran, Tehran, Iran.

2 Department of Water Engineering and Hydraulic Structures, Faculty of Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

3 Department of of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.

4 Department of Engineering and Water Resources Management, Faculty of Civil and Environmental Engineering, Tarbiat Madras University, Tehran, Iran.

چکیده [English]

Due to the increase in the risk of floods, especially in the cities, and the emergence of human, financial, and environmental risks due to its increase, the flood zoning areas are of great importance. Therefore, in this study, flood susceptible areas in Birjand plain were tried to be zoned with the help of effective criteria. In this regard, the data-driven methods of support vector machine (SVM) and random forest (RF) were used in combination with genetic algorithm to zoning flood susceptible areas. Therefore, in order to implement and validate the mentioned models, 42 flood prone locations in the study area were extracted. In addition, 19 hydrogeological, topographical, geological and environmental criteria affecting flood susceptibility in the study area were extracted to be used to predict flood susceptibility map. Area under the curve (AUC) and a variety of other statistical indicators including coefficient of determination (R2) and Root mean square error (RMSE) were used to evaluate the performances of the models. The values of R2, RMSE and AUC obtained from the SVM-GA method were 0.9032, 0.2751 and 0.931, respectively, and the RF-GA method were 0.9823, 0.2321 and 0.914, respectively, which indicate the compatibility and The RF model is more accurate than the SVM model. The results also showed that the susceptibility of flooding in the central areas of the study area, due to lower altitude and slope angle, is higher than other areas.

کلیدواژه‌ها [English]

  • Optimization
  • Flood
  • Random forest
  • Support vector machine
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نشریه محیط زیست طبیعی
دوره 76، شماره 1
اردیبهشت 1402
صفحه 43-60