Khảo sát ảnh hưởng của các biến thể bộ phân loại vector hỗ trợ khác nhau trong việc dự đoán nguy cơ lũ lụt của một con sông ở Himalaya

I. Mirza1, P. Sheik Abdul Khader1
1Department of Computer Applications, BS Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

Tóm tắt

Việc dự đoán lũ lụt có tầm quan trọng sống còn vì nó giúp chuẩn bị tốt hơn và phát triển các chiến lược giảm thiểu cho thảm họa sắp xảy ra, do đó giảm thiểu thiệt hại kinh tế cũng như thiệt hại về con người. Nhiều công nghệ khác nhau đã được áp dụng với các phương pháp khác nhau để hoàn thành nhiệm vụ dự đoán rủi ro lũ lụt. Trong số tất cả các công nghệ này, Học máy (Machine Learning) đã được thiết lập là phương pháp mạnh mẽ và hiệu quả nhất. Trong nghiên cứu này, bốn mô hình Bộ phân loại Vector Hỗ trợ (Support Vector Classifier) đã được phát triển với bốn loại nhân khác nhau nhằm tìm ra biến thể bộ phân loại Vector Hỗ trợ có hiệu suất tốt nhất trong việc dự đoán nguy cơ lũ lụt ở một trong những con sông ở Himalaya, Jhelum. Thêm vào đó, ba mô hình Học máy khác, bao gồm Bộ phân loại Cây Quyết định (Decision Tree Classifier), Bộ phân loại Quy trình Gauss (Gaussian Process Classifier) và Hồi quy Logistic (Logistic Regression) cũng đã được phát triển và đánh giá so với biến thể bộ phân loại Vector Hỗ trợ tối ưu. Bộ phân loại Vector Hỗ trợ với nhân Radial Basis Function đạt điểm ROC/AUC là 0.855, độ nhạy (Recall) 0.945, độ chính xác (Precision) 0.73, và điểm F1 là 0.79. Những điểm số này chứng minh rằng biến thể này vượt trội hơn so với ba biến thể còn lại cũng như ba mô hình Học máy đã nêu.

Từ khóa

#dự đoán lũ lụt #học máy #bộ phân loại vector hỗ trợ #nguy cơ lũ lụt #Himalaya #mô hình tối ưu

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