ناحیه‌بندی مناطق مستعد سیلاب توسط الگوریتم ماشین بردار پشتیبان بهینه‌سازی شده

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی منابع آب، گروه علوم و مهندسی آب، دانشگاه آزاد اسلامی واحد کرمان، کرمان، ایران.

2 استادیار گروه علوم و مهندسی آب، دانشگاه آزاد اسلامی واحد کرمان، کرمان، ایران.

چکیده

در این مطالعه تولید نقشه‌های مناطق مستعد سیل با بهینه‌سازی مدل ماشین بردار پشتیبان (SVM) با استفاده از الگوریتم‌های بهینه‌سازی فراابتکاری ازدحام ذرات (PSO)، ژنتیک (GA) و کلونی مورچگان (ACO) در حوضه آبریز زاهدان صورت گرفته است. برای تعیین بهترین فاکتورهای تأثیرگذار بر سیل، از بین 19 فاکتور مورداستفاده در مطالعات قبلی و اهمیت نسبی متغیرهای ورودی از دو آزمایش روش نسبت کسب اطلاعات (IGR) و آزمایش چندخطی بودن پارامترها استفاده شد.  فاکتور میزان محتوای آب در خاک که جزو عوامل مؤثر با ضریب IGR=0.767 است، برای اولین بار در این مطالعه به‌صورت مستقیم در مدل‌سازی مورداستفاده قرار گرفت. نقشه مشخصه سیل از روی پردازش داده‌های ماهواره Sentinel-1 تهیه و اعتبارسنجی شد. از مجموعه داده‌های تولید شده برای تولید نقشه‌های مناطق مستعد سیل با استفاده از مدل SVM بهینه‌سازی شده با الگوریتم‌های مذکور استفاده شد که برای اولین بار، در این تحقیق از الگوریتم ACO برای بهینه‌سازی مدل SVM در تولید نقشه‌های مناطق مستعد سیل استفاده شد. دقت پیش‌بینی مدل‌ها با استفاده از معیارهای آماری بررسی گردید. هر سه الگوریتم مورداستفاده در این مطالعه، عملکرد SVM را به‌‏طور متوسط 4 درصد بهبود بخشیدند درحالی‌که مدل PSO-SVM بهترین عملکرد را با RMSE=0.158، MSE=0.025، AUC=0.953 و StD=0.405 در میان الگوریتم‌های دیگر به نمایش گذاشت؛ همچنین مشخص شد تقریباً نیمی از منطقه شهری زاهدان در مناطق خطر متوسط تا بسیار زیاد قرار دارند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Flood Susceptibility Mapping Using Optimized SVM Algorithm

نویسندگان [English]

  • S.Meisami Mirkazemi 1
  • Navid Jalal Kamali 2
  • Mohsen Irandoost 2
1 Ph.D. Candidate of Water Recourses Engineering, Department of Water Sciences and Engineering, Islamic Azad University, Kerman Branch, Iran.
2 Assistant Professor, Department of Water Sciences and Engineering, Islamic Azad University, Kerman Branch, Iran.
چکیده [English]

This study describes optimization of Support Vector Machine (SVM) using meta-optimization algorithms including the Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Ant Colony optimization for flood susceptibility mapping at Zahedan Basin. To determine the best factors among the 19 factors used in previous studies and the relative importance of input variables, two experiments of Information Acquisition Ratio (IGR) and multilinearity of parameters were used soil moisture content factor which is identified as a highly effective factor (IGR=0.767),  is used directly in this study for the first time. The flood inventory map was prepared from Sentinel-1 satellite data processing and validated. The generated data set was used to map the flood prone areas using SVM model optimized with the mentioned algorithms. For the first time, in this research, ACO algorithm was used to optimize SVM model. The prediction accuracy of the models was evaluated using statistical criterion. All three algorithms used in this study improved SVM performance by 4% in average, while the PSO-SVM model had the best performance among the others with AUC=0.953, MSE=0.025, RMSE=0.158 and StD=0.405.

کلیدواژه‌ها [English]

  • Spatial Flood Prone Areas
  • Support Vector Machine
  • Particle Swarm Optimization
  • Genetic Algorithm
  • ant colony optimization
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