رویکرد پیش‌بینی جدید با استفاده از ترکیب یادگیری ماشین برای پیش بینی مناطق حساس به وقوع سیل (مطالعه موردی: حوضۀ آبریز کارون)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد گروه محیط زیست، دانشگاه محیط زیست کرج، کرج، ایران

2 استاد گروه محیط زیست، دانشگاه آزاد اسلامی تبریز، تبریز، ایران

چکیده

سابقه و هدف: ایران به‌دلیل‌‌ تنوع محیطی بالا، رتبة بالایی در بحران‌های ناشی از سوانح طبیعی دارد. با رشد سریع شهرها و تغییرات اقلیمی، سیل به عنوان یکی از این سوانح طبیعی خسارات اجتماعی– اقتصادی، بهداشتی و آسیب‌های محیط زیستی شدیدی را در بسیاری از مناطق به وجود آورده است. لذا، پیش‌بینی فضایی سیل به‌قدری حیاتی است که عدم شناسایی مناطق مستعد سیل در یک حوضة آبریز ممکن است آثار مخرب آن را افزایش دهد. در سال‌های اخیر، با پیشرفت ابزارهای سنجش از دور، اطلاعات جغرافیایی، یادگیری ماشین و مدل‌های آماری، ایجاد نقشه‌های پیش‌‌بینی سیل با دقت بالا کاملاً امکان‌پذیر شده است. به همین منظور، در این پژوهش، با استفاده از تصاویر ماهوارةSentinel و استفاده از رویکرد نوین مدل همادی با شش مدل یادگیری ماشین به پیش‌بینی مکان‌های مستعد سیل در حوضة آبریز کارون پرداخته شد.
 
مواد و روش‌‌ها: در این پژوهش از رادار دیافراگم مصنوعی (SAR) به‌دست‌آمده از تصاویر Sentinel-1 برای شناسایی مناطقی که تحت تأثیر سیل قرار گرفته‌اند، استفاده شد. ابتدا تاریخ‌های بارندگی شدید و وقوع سیل در منطقة مورد مطالعه از منابع اطلاعاتی مختلف شناسایی شدند. سپس تصاویر Sentinel-1 مربوط به قبل و بعد از رویداد سیل از طریق پایگاه دادة Copernicus تهیه شد. پردازش این داده‌ها با استفاده از پلتفرم SNAP انجام شد. شناسایی مناطق تحت تأثیر سیل با بهره‌گیری از روش حد آستانه صورت گرفت. برای این منظور از شاخص تفاوت نرمال‌شدة آب (NDWI) تولیدشده از تصاویر Sentinel-2 و همچنین طبقات پوشش زمین که بدنه‌های آبی دائمی را نشان می‌دهند، استفاده شد تا آستانه‌ای که مناطق سیل‌زده را شناسایی می‌کند، تعیین شود. سپس لایة پلیگونی سیل به لایة نقطه‌ای تبدیل و در مجموع ۷۰ نقطه وقوع سیل ایجاد شد. با توجه به مرور مطالعات پیشین و ویژگی‌های محلی، هفت عامل اصلی که به‌طور چشمگیری بر وقوع سیلاب در منطقه تأثیر دارند، شناسایی شدند. این عوامل شامل شاخص نرمال‌شدة تفاوت پوشش گیاهی (NDVI)، شاخص رطوبت توپوگرافی (TWI)، شیب، جهت جریان، تجمع جریان، فاصله از رودخانه و بارندگی ماهانه هستند. مدل رقومی ارتفاع (DEM) منطقه نیز از پایگاه دادة SRTM تهیه شده و تفکیک فضایی همة عوامل با لایة DEM یکسان تنظیم شد. سپس، با استفاده از الگوریتم‌های مختلف یادگیری ماشین، مدلی ترکیبی توسعه داده شد که نتایج دقیق‌تری در پیش‌بینی مناطق مستعد سیل ارائه می‌دهد. مدل‌های منفرد شامل مدل خطی تعمیم‌یافته (GLM)، رگرسیون درختی پیشرفته (BRT)، مدل ماشین بردار پشتیبان (SVM)، مدل جنگل تصادفی (RF)، مدل رگرسیون سازشی چندمتغیره (MARS) و مدل بیشینة بی‌نظمی (MAXENT) هستند.
 
نتایج و بحث: نتایج این مطالعه نشان می‌دهد که شمال شرق شهرستان الیگودرز، بخش‌هایی از دورود و ازنا در استان لرستان، خادم‌میرزا، شهرکرد و کیار در استان چهارمحال بختیاری، دنا و بویراحمد در استان کهکیلویه و بویراحمد، شهرستان سمیرم در استان اصفهان، و مناطق جنوبی حاشیة رودخانه کارون در استان خوزستان بیشترین پتانسیل وقوع سیل را در این حوضه دارند. ارزیابی عملکرد مدل‌ها نشان می‌دهد که مدل‌های جنگل تصادفی (RF) و بیشینة بی‌نظمی (MaxEnt) بالاترین دقت را در بین مدل‌های منفرد داشته‌اند. این مدل‌ها با ترکیب اطلاعات محیطی و داده‌های وقوع سیل، قادر به ارائة نقشه‌های حساسیت به سیل با دقت بالا هستند. از این نقشه‌ها می‌توان به‌عنوان ابزار مدیریتی مهمی برای کاهش اثرات مخرب سیل و جلوگیری از توسعة مناطق آسیب‌پذیر استفاده کرد.
نتیجه‌گیری: به‌طور کلی، این پژوهش نشان می‌دهد که استفاده از رویکرد همادی با ترکیب مدل‌های یادگیری ماشین می‌تواند نتایج قابل اطمینان‌تری در پیش‌بینی مناطق مستعد سیل فراهم کند. نتایج این پژوهش برای مدیران و برنامه‌ریزان کارآمد است و می‌تواند از توسعه در مناطق آسیب‌پذیر جلوگیری کند و در نتیجه به کاهش زیان‌های اقتصادی و جانی در آینده کمک کند.

کلیدواژه‌ها

عنوان مقاله [English]

A New Forecasting Approach Using the Combination of Machine Learning to Predict Flood Susceptibility (Case Study: Karun Catchment)

نویسندگان [English]

  • Bhareh Gharedaghy 1
  • Amir Ghasemzadeh 2
1 MSC, Department of Environment, University of Environment, Karaj, Iran
2 Professor, Department of Environment, Islamic Azad University, Tabriz, Iran
چکیده [English]

Introduction: Due to its environmental diversity, Iran ranks high in terms of crises caused by natural disasters. Flooding, as one of these disasters, is  causing severe social, economic, health, and environmental damage in many areas due to rapid urban growth and climate change. Therefore spatial forecasting of floods is crucial, as failure to identify flood risk areas in a catchment can exacerbate the destructive effects of floods. Recent advances in remote sensing, geographic information systems, machine learning, and statistical modelling have made it possible to produce highly accurate flood prediction maps. This study aims to predict flood risk areas in the Karun watershed using Sentinel satellite images and a novel ensemble approach with six machine learning models.
Materials and Methods: In this study, Synthetic Aperture Radar (SAR) data from Sentinel-1 images were used to identify areas affected by flooding.  First, the dates of heavy rainfall and flooding events in the study area were identified from various sources of information. Subsequently, Sentinel-1 images were obtained from the Copernicus database, representing the area before and after the flood events. The aforementioned data were processed using the SNAP platform. The identification of flood-affected areas was achieved through the application of the thresholding technique. For this purpose, the Normalized Difference Water Index (NDWI) generated from Sentinel-2 images and land cover classes indicating permanent water bodies were employed to determine the threshold for identifying flood-affected areas. The flood polygon layer was converted to a point layer, resulting in a total of 70 flood occurrence points. A review of previous studies and local characteristics identified seven main factors that significantly affect flood occurrence in the region. These factors include the Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), slope, flow direction, flow accumulation, distance from the river, and monthly rainfall. Additionally, the Digital Elevation Model (DEM) of the region was obtained from the SRTM database, and the spatial resolution of all factors was aligned with the DEM layer. Subsequently, various machine learning algorithms were employed to develop a combined model that provides more accurate predictions of flood-prone areas. The individual models include the Generalized Linear Model (GLM), Boosted Regression Tree (BRT), Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), and Maximum Entropy (MAXENT).
                                
Results and Discussion: The results of this study indicate that the northeast of Aligudarz city, parts of Durud and Azna in Lorestan province, Khademmirza, Shahrekord, and Kiyar in Chaharmahal Bakhtiari province, Dana and Boyer Ahmad in Kohgiluyeh and Boyer Ahmad province, Semirom city in Isfahan province, and the southern border areas of Karun River in Khuzestan province have the highest flood potential in this basin. The performance evaluation of the models revealed that the Random Forest (RF) and Maximum Entropy (MaxEnt) models exhibited the highest accuracy among the individual models. These models, by combining environmental information and flood occurrence data, can produce highly accurate flood susceptibility maps. These maps can serve as crucial management tools to mitigate the adverse effects of floods and prevent development in vulnerable areas.
 
Conclusion: Overall, this study demonstrates that the use of an ensemble approach which combines machine learning models can provide more reliable results in the prediction of flood risk areas. The findings of this research are beneficial for managers and planners, as they can prevent development in vulnerable areas and consequently help reduce financial losses and human damages in the future.
 

کلیدواژه‌ها [English]

  • Flood
  • Karun Watershed
  • Sentinel Satellite Images
  • Machine Learning Model
  • Ensemble Model

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