مدل سازی توان سیل خیزی در آبخیز زرینه رود با استفاده از مدل های هوش مصنوعی

اعلمی, مهدی; اردستانی, مجتبی; ملک محمدی, بهرام

doi:10.22092/wmrj.2023.360973.1513

مدل سازی توان سیل خیزی در آبخیز زرینه رود با استفاده از مدل های هوش مصنوعی

نوع مقاله : پژوهشی

نویسندگان

¹ دانشجوی دکتری محیط‌زیست گرایش منابع آب، پردیس بین الملل کیش دانشگاه تهران

² استاد دانشکده ی مهندسی محیط‌زیست، دانشگاه تهران

³ دانشیار دانشکده ی مهندسی محیط‌زیست، دانشگاه تهران

10.22092/wmrj.2023.360973.1513

چکیده

مقدمه و هدف
در میان خطرها و بلاهای طبیعی، بدون تردید سیل به‌عنوان ناگوارترین خطر در جهان شناخته‌شده است. یکی از راه‌کارهای اساسی برای کاهش خسارت‌های ناشی از سیل تهیه‌ی نقشه‌ی حساسیت سیل است. پیش بینی مکانی احتمال رخداد سیل با استفاده از مدل هایی که براساس داده های مکانی و تاریخی به وجود آمده‌اند، در نهایت منجر به تهیه‌ی نقشه های حساسیت پذیری سیلاب می شود، از راه‌کارهای مناسب برای برنامه ریزان مدیریت زمین‌ها در مناطق مختلف برای پیشگیری از رخداد این پدیده است. در این پژوهش، به‌منظور تعیین مناطق مستعد رخداد سیل از مدل ترکیبی (هیبریدی) استنتاج عصبی و فازی تطبیقی و دستورالعمل بهینه سازی فراکاوشی رقابت امپراتوری (ANFIS-ICA) و مدل ترکیبی استنتاج عصبی و فازی تطبیقی و دستورالعمل بهینه سازی فراکاوشی ازدحام ذرات (ANFIS-PSO) استفاده شد.
مواد و روش ها
آبخیز زرینه رود در شمال‌غربی استان کردستان و میان طول جغرافیایی ″30′48°45 و ″20′48°46 شرقی و عرض جغرافیایی ″20′42°35 و ″15′23°36 شمالی است. مساحت این آبخیز 4485/2 کیلومترمربع است. اقلیم منطقه معتدل مرطوب است و میانگین بارندگی سالانه‌ی آن 480 میلی‌متر است. موقعیت رخدادهای سیل به‌طور تصادفی به دو گروه آموزش (70%) و اعتبارسنجی (30%) تقسیم شد. عامل‌های محیطی مختلف (بلندی، جهت، شیب، انحنای سطح، کاربری زمین، سنگ شناسی، بارندگی، شاخص توان جریان، فاصله از آبراهه، شاخص رطوبت پستی‌بلندی) به‌عنوان متغیر مستقل در مدل‌سازی انتخاب شدند و لایه های رقومی آنها تهیه شد. در این پژوهش از مدل ANFIS-ICA و مدل ANFIS-PSO استفاده شد. نتایج پیش بینی مدل‌ها بر اساس معیار (AUC) و آماره‌ی مهارت صحیح (TSS) ارزیابی شد.
نتایج و بحث
بر پایه‌ی یافته های این پژوهش در مرحله‌ی اعتبارسنجی، مدل ANFIS-PSO با (AUC) 98/0 و آماره‌ی مهارت صحیح (TSS) 89/0 بیشترین دقت را داشت. همچنین عامل فاصله از آبراهه به‌عنوان مهم‌ترین عامل محیطی شناسایی شد. افزون بر این، شیب زمین و TWI به‌ترتیب در جایگاه‌های دوم و سوم اهمیت بودند.
نتیجه گیری و پیشنهادها
بر اساس نتایج این پژوهش، رویکرد ترکیبی (هیبریداسیون) که ترکیب شدن مدل های یادگیری ماشینی و دستورالعمل های بهینه سازی فراکاوشی است، موجب افزایش قدرت یادگیری و همچنین توان پیش بینی مدل شد. همچنین عامل فاصله از آبراهه و شیب زمین مهم‌ترین عامل‌های مؤثر در سیل گیری هستند. بر اساس نتایج و تحلیل های انجام‌شده می توان نتیجه گیری کرد که مدل های یادگیری ماشینی قابلیت زیادی در پیش بینی توان سیل گیری دارند. در این پژوهش نقشه های توان سیل تهیه‌شده می تواند برای مدیران و کارشناسان بسیار کاربردی بوده و در برنامه ریزی اقدام‌های مهارکردن سیل استفاده‌ی عملی داشته باشد. توجه کردن به امکانات و اقدام‌های مهارکردن سیل در موقعیت هایی که توان سیل‌گیری زیادی دارند، موجب افزایش مدیریت سیل از نظر اقتصادی و فنی می شود.

کلیدواژه‌ها

موضوعات

مدیریت سیلاب و خشکسالی

عنوان مقاله [English]

Flood Potential Modeling in Zarineh Rood Watershed Using Artificial Intelligence Models

نویسندگان [English]

Mehdi Aalami ¹
Mojtaba Ardestani ²
Bahram Malekmohammadi ³

¹ Ph.D. Student in Environmental Engineering, Water Resources, Kish International Campus, University of Tehran

² Professor, Faculty of Environment, University of Tehran

³ Associate Professor, Faculty of Environment, University of Tehran

چکیده [English]

Introduction and Goal
Among natural disasters, flood is undoubtedly the most catastrophic hazard in the world. One of the basic strategies for reducing the damage caused by floods is to prepare a flood sensitivity map. Spatial prediction of the flooding probability using models created from spatial and historical data, which ultimately leads to the preparation of flood sensitivity maps is an appropriate solution for land management planners in different areas to prevent the occurrence of this phenomenon. In this research, in order to determine flood-prone areas, the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of imperial competition (ANFIS-ICA) and the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of particle swarm (ANFIS-PSO) are used.
Materials and Methods
The Zarine River watershed has an area of 4485 km² and is located in the northwest of Kurdistan province between the longitude of 45°48ʹ30ʺand 46°48ʹ20ʺ east and the latitude of 35°42ʹ20ʺ and 36°23ʹ15ʺ north. The climate of the region is humid and the average annual rainfall is 480 mm. Locations of flood events were randomly divided into two groups: training (70%) and validation (30%). Various environmental factors (height, direction, slope, surface curvature, land use, lithology, rainfall, flow power index, distance from river and topographic wetness index) were selected as independent variables in the modeling and their digital layers were prepared. The ANFIS-ICA and ANFIS-PSO models were used in this research and their prediction results were evaluated based on the criterion (AUC) and the true skill statistic (TSS).
Results and Discussion
On the basis of these findings, in the validation stage, the model (ANFIS-PSO) with an AUC of 0.98 and a true skill statistic (TSS) of 0.89 had the highest accuracy. The results also showed that the factor of distance from the stream was identified as the most important environmental factor. In addition, ground slope and TWI were ranked second and third in importance, respectively.
Conclusion and Suggestions
Based on the results, the hybridization approach, which combines machine learning models and meta-exploratory optimization algorithms, improves the learning power as well as the predictive power of the model. The results of this research showed that the distance from the stream and the slope of the land are the most important factors affecting flooding. Based on the results and analysis, it can be concluded that machine learning models have a high capability for predicting flood potential. The flood potential maps prepared in this research can be very useful for managers and experts and can be used in planning flood prevention measures. Directing flood control facilities and measures in situations with a high flood potential will improve flood management from an economic and technical point of view.

کلیدواژه‌ها [English]

Flood
land management
modeling
natural hazards
risk

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