ارزیابی مدل‌های هوش مصنوعی مبتنی بر درخت به منظور پیش‌بینی خطر سیل در بستر GIS

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد، گروه مهندسی نقشه برداری، دانشکده مهندسی نقشه ‎برداری و اطلاعات مکانی، دانشگاه تهران، تهران، ایران.

2 دانش‌آموخته کارشناسی ارشد مهندسی عمران آب و سازه‌های هیدرولیکی و عضو باشگاه پژوهشگران جوان و نخبگان، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.

3 دانش ‎آموخته کارشناسی ارشد مهندسی سنجش از دور، دانشگاه تحصیلات تکمیلی صنعتی فناوری پیشرفته کرمان، ایران.

4 دانشیار گروه مهندسی عمران، دانشگاه بیرجند، بیرجند، ایران.

5 دانش ‎آموخته کارشناسی ارشد، گروه آب و سازه‌های هیدرولیکی، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران.

چکیده

سیل یکی از مخرب‌ترین انواع بلایای طبیعی است که هر ساله باعث از دست رفتن جان و مال انسان‌ها در سراسر جهان می‌شود. هدف از تحقیق حاضر ارزیابی و مقایسه قابلیت سه مدل یادگیری ماشین یعنی درخت بیز ساده (‏NBTree)‏، درخت تصمیم متناوب (ADTree) و جنگل تصادفی (‏RF) ‏برای پیش‌بینی خطر وقوع سیل در شهرستان مانه و سملقان می‌باشد. نوآوری تحقیق حاضر ارائه مدل‌های ترکیبی مبتنی بر درخت می‌باشد که کمتر در تحقیقات پیشین مورد استفاده قرار گرفته‌اند. برای تهیه نقشه مرجع سیل در منطقه موردمطالعه، 300 موقعیت مستعد سیل شناسایی شدند و از طریق انتخاب تصادفی با نسبت 70 به 30 به مجموعه داده‌های آموزشی و اعتبارسنجی تقسیم شدند. پایگاه‌ داده مکانی سیل با استفاده از 15 معیار هیدروژئولوژیکی و محیطی مؤثر بر سیل ایجاد شد. در نهایت، نقشه‌های پیش‌بینی خطر سیل با استفاده از مدل‌های NBTree، ADTree و RF تهیه شدند. به منظور اعتبار‌سنجی مدل‌های پیش‌بینی خطر سیل، معیار سطح زیر منحنی (‏AUC)‏ و معیارهای آماری نرخ پیش‌بینی مثبت، نرخ پیش‌بینی منفی، حساسیت، ویژگی و دقت مورد استفاده قرار گرفتند. نتایج نشان داد که مدل RF دقت بالاتری نسبت به مدل‌های NBTree و ADTree در پیش‌بینی خطر سیل منطقه موردمطالعه دارد. ​همچنین، نتایج نشان داد که احتمال وقوع خطر سیل در مناطق مرکزی منطقه موردمطالعه به دلیل ارتفاع و شیب کمتر، بیشتر از سایر مناطق است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of Tree-Based Artificial Intelligence Models to Predict Flood Risk using GIS

نویسندگان [English]

  • Seyed Ahmad Eslaminezhad 1
  • Mobin Eftekhari 2
  • Saeid Mahmoodizadeh 3
  • Mohammad Akbari 4
  • Ali Haji Elyasi 5
1 M.Sc. Graduate, Department of Surveying Engineering, Faculty of Surveying Engineering and Spatial Information, University of Tehran, Tehran, Iran.
2 M.Sc. Graduate, Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
3 M.Sc. Graduate, Department of Remote Sensing Engineering, Faculty of Surveying, University of Industrial and Technological Advanced Studies, Kerman, Iran.
4 Associate Professor at Department of Civil Engineering, University of Birjand, Birjand, Iran.
5 M.Sc. Graduate, Department of Water and Hydraulic Structure, K. N. Toosi University of Technology, Tehran, Iran.
چکیده [English]

Floods are one of the most devastating types of natural disasters that every year causes the loss of human lives and properties around the world. The purpose of this study is to evaluate and compare the capability of three machine learning models namely Naïve Bayes Tree (NBTree), Alternating Decision Tree (ADTree), and Random Forest (RF) to predict flood risk in Maneh and Samalqan county. The novelty of the present study is the presentation of tree-based hybrid models that have been less used in previous research. To prepare a flood reference map in the study area, 300 flood-prone locations were identified and were divided into training and validation data sets through random selection with a ratio of 70 to 30. The spatial database of the flood was created using 15 hydrogeological and environmental criteria affecting the flood. Finally, three flood risk prediction maps were generated using NBTree, ADTree, and RF models. To validate the flood risk predicting models, the Area Under the Curve (AUC) factor and the statistical criteria of Positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy were used. The results showed that the RF model had higher accuracy than the NBTree and ADTree models in predicting flood risk in the study area. The results also showed that the risk of flooding in the central areas of the study area is higher than other areas due to lower altitude and slope.

کلیدواژه‌ها [English]

  • Flood Prediction
  • Naïve Bayes Tree
  • Alternating decision tree
  • Random forest
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