15. SỬ DỤNG PHƯƠNG PHÁP TỶ SỐ TẦN SUẤT VÀ CÁC PHƯƠNG PHÁP HỌC MÁY ĐỂ THÀNH LẬP BẢN ĐỒ NHẠY CẢM TRƯỢT LỞ. KHU VỰC THỬ NGHIỆM: XÃ PHÌN NGAN, TỈNH LÀO CAI

Hiển Đỗ Minh, Hoàng Nguyễn Văn, Dũng Mai Lê, Hương Ngô Thị, Dũng Lương Hữu, Phong Nguyễn Bình

Giới thiệu

Đánh giá tính nhạy cảm với tai biến trượt lở khu vực xã Phìn Ngan, tỉnh Lào Cai, Việt Nam đã được thực hiện bằng cách áp dụng ba mô hình học máy là hồi quy logic (LR), mạng Bayes (BN), máy véc tơ hỗ trợ (SVM) và phương pháp thống kê tỷ số tần suất (FR) mà FR được sử dụng để tính toán các giá trị trọng số của mỗi lớp tham số trong các bản đồ tác nhân. Các bản đồ có trọng số này sau đó được kết hợp với bản đồ trượt lở để đánh giá mối quan hệ không gian của chúng. Tiếp theo, các mô hình học máy sẽ được áp dụng để tính toán mức độ quan trọng của từng bản đồ tác nhân gây trượt lở. Hiệu suất của các mô hình học máy đã được đánh giá bằng cách sử dụng đường cong đặc tính hoạt động thu được (ROC) và diện tích dưới đường cong (AUC). Phân tích và so sánh kết quả cho thấy cả 3 mô hình đều cho kết quả tốt khi đánh giá tính nhạy cảm với trượt lở đất (AUC = 87,2 - 97,5 %). Tuy nhiên, mô hình BN (AUC = 97,5 %) có hiệu suất tốt nhất so với các mô hình trượt lở khác, tiếp theo là mô hình LR (AUC = 94,6 %) và mô hình SVM (AUC = 87,2 %). Kết quả chỉ ra rằng các mô hình đã cho kết quả đầu ra với khả năng dự báo tốt. Chúng cũng rất hữu ích trong việc hỗ trợ lập kế hoạch sử dụng đất, phòng ngừa và giảm thiểu rủi ro do sạt lở đất trong khu vực nghiên cứu.

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Các tác giả

Hiển Đỗ Minh
hien_dm@yahoo.com (Liên hệ chính)
Hoàng Nguyễn Văn
Dũng Mai Lê
Hương Ngô Thị
Dũng Lương Hữu
Phong Nguyễn Bình
Đỗ Minh, H., Nguyễn Văn, H., Mai Lê, D., Ngô Thị, H., Lương Hữu, D., & Nguyễn Bình, P. (2022). 15. SỬ DỤNG PHƯƠNG PHÁP TỶ SỐ TẦN SUẤT VÀ CÁC PHƯƠNG PHÁP HỌC MÁY ĐỂ THÀNH LẬP BẢN ĐỒ NHẠY CẢM TRƯỢT LỞ. KHU VỰC THỬ NGHIỆM: XÃ PHÌN NGAN, TỈNH LÀO CAI. Tạp Chí Khoa học Tài Nguyên Và Môi trường, (42), 150–166. Truy vấn từ https://tapchikhtnmt.hunre.edu.vn/index.php/tapchikhtnmt/article/view/439
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