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تجزیه و تحلیل خطر سیل‌گیری با استفاده از روش یادگیری ماشین جنگل تصادفی (مطالعۀ موردی: شهر مشهد) | ||
| اکوهیدرولوژی | ||
| مقاله 1، دوره 10، شماره 1، فروردین 1402، صفحه 1-15 اصل مقاله (2.2 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2022.346677.1667 | ||
| نویسندگان | ||
| نرگس عرب1؛ عبدالرسول سلمان ماهینی* 2؛ علیرضا میکائیلی تبریزی3؛ توما ویته4 | ||
| 1دکتری آمایش محیط زیست، دانشکدۀ شیلات و محیط زیست، دانشگاه گرگان | ||
| 2استاد، دانشکدۀ شیلات و محیط زیست، دانشگاه گرگان | ||
| 3دانشیار، دانشکدۀ شیلات و محیط زیست، دانشگاه گرگان | ||
| 4دانشیار، دانشکدۀ جغرافیا، دانشگاه رن 2 | ||
| چکیده | ||
| سیل از رایج‌ترین بلایای طبیعی است و خسارتهای مالی و جانی فراوانی به جای میگذارد. اگرچه میزان بارندگی در بسیاری از مناطق ایران کم است، در بسیاری از مناطق، بیشترین میزان بارندگی سالانه تنها در یک روز یا مدت کوتاهی رخ میدهد که منجر به سیل میشود. آب روان در جریان سیل به دلیل ساختار زمین‌شناسی و همچنین، تخریب اکوسیستم میتواند بسیار آلوده باشد و اغلب گل‌و‌لای زیادی به همراه دارد که بر خسارتهای سیل میافزاید. برای کاهش خسارتهای احتمالی سیل، برنامهریزان و تصمیمگیرندگان باید از زمان و مکان وقوع سیل آگاه باشند. این امر مستلزم استفاده از روشهای جدید پیش‌بینی سیل و جلوگیری از خسارتهای آن است. در این مطالعه، از روش یادگیری ماشین درخت تصادفی یا Random Forest (RF) برای پیش‌بینی مکان وقوع سیل در شهر مشهد استفاده شد و عملکرد آن مورد بررسی قرار گرفت. همچنین تأثیر هر یک از عوامل ارتفاع و شیب متوسط حوضه، جهت شیب، شاخص رطوبت توپوگرافی، شاخص خشکسالی، فاصله از آبراههها، زمین‌شناسی، کاربری اراضی، تراکم آبراههها، آبراههها و میزان بارش حداکثر متوسط سالانه در این پیش‌بینی مورد بررسی قرار گرفت. نتایج ارزیابی خروجی مدل RF نشان داد مقدار AUC95 درصد است. به طور کلی، نتایج نشان داد مدل RF دارای دقت زیادی در تعیین مناطق حساس به وقوع سیل در حوضۀ شهر مشهد است. | ||
| کلیدواژه‌ها | ||
| ارزیابی خطر سیل‌گیری؛ جنگل تصادفی؛ یادگیری ماشین؛ شهر مشهد؛ پهنهبندی سیل | ||
| عنوان مقاله [English] | ||
| Flood risk analysis using random forest machine learning method (Case study: Mashhad city) | ||
| نویسندگان [English] | ||
| Narges Arab1؛ Abdulrassoul Salman Mahiny2؛ Alireza Mikaeili Tabrizi3؛ Thomas Houet4 | ||
| 1Ph.D in Environment assessment and land use planning, Faculty of Fisheries and Environment, Gorgan University, Gorgan ,Iran | ||
| 2Professor, Faculty of Fisheries and Environment, Gorgan University, Gorgan ,Iran | ||
| 3Associate Professor, Faculty of Fisheries and Environment, Gorgan University, Gorgan ,Iran | ||
| 4Associate Professor, Faculty of Geography, University of Rennes 2, France | ||
| چکیده [English] | ||
| Abstract Flood is one of the most common natural disasters that causes significant financial and human losses. Although rainfall is low in many parts of Iran, in some areas, the highest amount of annual rainfall occurs in just one day or a short period, leading to floods. Due to geological structure and ecosystem destruction, the surface water during floods can be highly polluted and often carries a lot of sediment, which increases flood damage. To reduce potential flood damage, planners and decision-makers must be aware of the time and location of floods. This requires the use of new methods for predicting floods and preventing their damage. In this study, the Random Forest (RF) machine learning method was used to predict the location of floods in Mashhad city, and its performance was evaluated. The impact of each factor including average basin elevation and slope, slope direction, topographic moisture index, drought index, distance from waterways, geology, land use, waterway density, waterways, and maximum average annual rainfall was also examined in this prediction. The evaluation results of the RF model output showed an AUC value of 95%. Overall, the results showed that the RF model has high accuracy in identifying flood-prone areas in the Mashhad city basin. | ||
| کلیدواژه‌ها [English] | ||
| Flood risk assessment, Random Forest, machine learning, Mashhad city, flood zoning | ||
| مراجع | ||
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