نشریه علمی پژوهشی مهندسی آبیاری و آب ایران

نشریه علمی پژوهشی مهندسی آبیاری و آب ایران

ارزیابی کارایی شبکه عصبی حافظه طولانی کوتاه‌مدت در پیش‌بینی سیلاب

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشکده معماری دانشگاه یزد
2 دانشگاه خوارزمی
10.22125/iwe.2025.554010.1902
چکیده
پدیده سیل یکی از خطرناک‌ترین بلایای طبیعی است که می‌تواند خسارات جبران‌ناپذیری به جوامع انسانی وارد کند. پیش‌بینی دقیق سیل به منظور کاهش خسارت‌ها و برنامه‌ریزی بهتر ضروری است. این مطالعه کاربرد بالقوه شبکه حافظه طولانی کوتاه‌مدت (LSTM) از نمونه معماری شبکه‌های عصبی یادگیری عمیق، را برای پیش‌بینی و نقشه‌برداری مکانی صریح از احتمال سیل ناگهانی شرح می‌دهد. این مدل‌ها بر اساس داده‌های هیدرولوژیکی و هواشناسی آموزش داده شده و کارایی آنها با استفاده از معیارهای مختلف ارزیابی شده است. به نظر می‌رسید عملکرد این مدل نتایج قویتری نسبت به مدل‌های آماری کلاسیک ارائه دهد اما با توجه به نقص و تعدد فراوان داده های ناسازگار در ایران مرکزی که معمولا به پراکندگی مکانی ایستگاه‌ها و خطاهای اندازه‌گیری و عدم وجود متادیتا برمیگردد و دسترسی به آنها نیز تا حدی دشوار است، پیش بینی ارائه شده نشان داد که تخمین این مدل در دبی های اوج با چالش مواجه بوده و تمایل دارد دبی های های اوج را با توجه به مقادیر وسیع صفر، بسیار کم تخمین بزند. این موضوع نشان می‌دهد که هرچند مدل های شبکه عصبی ابزار قدرتمندی برای مدلسازی هیدرولوژیکی هستند ولی بسیار وابسته به داده های دقیق و منظم و همچنین نیازمند به آمارهای با سری‌های زمانی طولانی مدت هستند اما اگر همین مدل با سری داده‌های مکانی، از جمله موقعیت ایستگاه‌ها و توپوگرافی مسیر رودخانه و حتی دیگر ویژگی های مکانی که منجر به ایجاد و تقویت سیلاب می‌شود، ترکیب گردد قطعا نتایج رضایت بخش‌تری ارائه خواهد کرد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Evaluation of the efficiency of long-short-term memory neural network in flood prediction

نویسندگان English

سمیه شهری 1
amir safari 2
1 Faculty of Architecture, Yazd University
2 kharazmi university
چکیده English

Flooding is one of the most dangerous natural disasters that can cause irreparable damage to human societies. Accurate flood prediction is essential for reducing damages and better planning. This study describes the potential application of long short-term memory (LSTM) network, a deep learning neural network architecture, for explicit spatial prediction and mapping of flash flood probability. These models were trained based on hydrological and meteorological data and their performance was evaluated using various criteria. The performance of this model seemed to provide stronger results than classical statistical models, but due to the lack and abundance of inconsistent data in central Iran, which is usually due to the spatial dispersion of stations and measurement errors and the lack of metadata, and their access to them is also somewhat difficult, the presented forecast showed that the estimation of this model faced challenges in peak discharges and tended to greatly underestimate peak discharges due to large values ​​of zero. This shows that although neural network models are a powerful tool for hydrological modeling, they are highly dependent on accurate and regular data and also require long-term time series statistics. However, if the same model is combined with spatial data series, including the location of stations and the topography of the river course and even other spatial features that lead to the creation and amplification of floods, it will definitely provide more satisfactory results.

کلیدواژه‌ها English

Flood
prediction
artificial intelligence
deep learning
long-short-term memory network
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