پهنه‌بندی احتمال وقوع سیل با استفاده از بررسی مقایسه‌ای دو مدل شناخته شده جنگل تصادفی و ماشین بردار پشتیبان در شمال ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی آب، دانشکده کشاورزی، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران.

2 دانشیار، گروه مهندسی آب، دانشکده کشاورزی، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران.

3 استادیار، گروه مهندسی آب، دانشکده کشاورزی، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران

چکیده

هدف از پژوهش پیش رو، پهنه بندی احتمال وقوع سیل در حوزه آبخیز سالیان تپه، واقع در استان گلستان میباشد. بدین منظور، از دو مدل معروف و شناخته شده‌ی داده‌کاوی یعنی مدل جنگل تصادفی(RF) و مدل ماشین بردار پشتیبان (SVM) به عنوان بنچ‌مارک و به لحاظ الگوریتم محاسباتی توانمند در زمینه ارزیابی فرایند وقوع سیلاب استفاده شد. شواهد سیلاب با استفاده از بازدیدهای میدانی،گزارش‌ها و اطلاعات سازمانی موجود ثبت و در سامانه اطلاعات جغرافیاییGIS)) در قالب نقشه تهیه شد. همچنین، با توجه به مرور منابع گسترده، سیزده عامل زمینه‌ساز شامل فاصله از آبراهه، واحدهای سنگ‌شناسی، درصد شیب، بافت خاک، جهت شیب، کاربری اراضی، انحنای طولی و عرضی دامنه، شاخص رطوبت، شاخص توان فرسایشی آبراهه و طبقات ارتفاعی به‌عنوان عوامل موثر بر وقوع سیل در منطقه مورد مطالعه انتخاب و لایه‌های مذکور در سامانه اطلاعات جغرافیایی تهیه شدند. در این مطالعه بعد از آماده‌سازی لایه‌ها، برای آنالیز این داده‌ها و بررسی هم‌خطی آنها از نرم‌افزار SPSS استفاده شد. به‌منظور ارزیابی نتایج مدلها، از مقدار مساحت زیر منحنی تشخیص عملکرد نسبی ROC)) استفاده شد. سه سری متفاوت از نقاط وقوع خطر سیل (S1, S2, S3 ( شامل 70 درصد برای آموزش مدل و 30 درصد برای اعتبار سنجی به صورت تصادفی آماده شد تا دقت و صداقت مدل مورد ارزیابی قرار بگیرد. نتایج نشان داد نقشه میانگین حاصل از مدل جنگل تصادفی در مرحله اعتبارسنجی با مساحت زیر منحنی 96 درصد و صداقت 001/0 کارایی بهتری نسبت به مدل ماشین بردار پشتیبان در پهنه‌بندی سیلاب در حوضه مورد مطالعه دارد .

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Flood probability zonation using a comparative study of two well-known random forest and support vector machine models in northern Iran

نویسندگان [English]

  • Mohammad Reza Tahmasebi 1
  • Saeid Shabanlou 2
  • Ahmad Rajabi 3
  • Fariborz Yosefvand 3
1 Ph.D. Candidate, Department of Water Engineering, College of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
2 Associate Professor, Department of Water Engineering, College of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
3 Assistant Professor, Department of Water Engineering, College of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
چکیده [English]

The current study is aimed to zoning flood probability map in the Saliantapeh catchment is located in the Golestan Province. To this aim, two well-known data mining models namely Random Forest (RF) and Support Vector Machine (SVM) were applied due to their robust computational algorithm. Flood inventories were gathered through several field surveys using, local information and available organizational resources and corresponding map was created in the geographic information system. Reviewing several worldwide studies, 13 predisposing variables including proximity to stream, soil texture, lithological units, land use/cover, slope percent, elevation/DEM, slope aspect, plan curvature, profile curvature, stream power index and topographic wetness index were chosen and the corresponding maps were generated in the geographic information system. In this study, after preparing the predictor maps, SPSS software was used to analyze this data and testing Multi-collinearity. In order to evaluate models’ results the area under the receiver operating were used. Three different sample data sets (s1, s2, s3) including 70% for training and 30% for validation were randomly gathered to evaluate the robustness of the applied models. Results showed that the RF model with the area under curve value of 0.96 and robustness of 0,001 in validation step had better performance on flood probability zonation over the study area.

کلیدواژه‌ها [English]

  • Flood
  • Golestan Province
  • Random forest
  • Robustness
  • Support vector machine

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مدیریت آب و آبیاری
دوره 11، شماره 2
شهریور 1400
صفحه 223-235

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