Đánh giá nguy cơ lũ đô thị trong một thành phố có mật độ đô thị cao bằng cách sử dụng phân tích đa yếu tố và các thuật toán học máy

Springer Science and Business Media LLC - Tập 149 - Trang 639-659 - 2022
Farhana Parvin1, Sk Ajim Ali1, Beata Calka2, Elzbieta Bielecka2, Nguyen Thi Thuy Linh3, Quoc Bao Pham4
1Department of Geography, Faculty of Science, Aligarh Muslim University (AMU), Aligarh, India
2Institute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, Warsaw, Poland
3Institute of Applied Technology, Thu Dau Mot University, Binh Duong province, Vietnam
4Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Sosnowiec, Poland

Tóm tắt

Lũ lụt được coi là một trong những thảm hoạ thiên nhiên gây thiệt hại nghiêm trọng nhất, cướp đi sinh mạng của nhiều người trên toàn thế giới. Nghiên cứu hiện tại nhằm dự đoán nguy cơ lũ lụt cho Warsaw, Ba Lan, bằng cách sử dụng ba mô hình học máy, bao gồm hồi quy logistic bayesian (BLR), mạng nơ-ron nhân tạo (ANN) và mạng nơ-ron sâu (DLNN). Hiệu suất của ba phương pháp này đã được đánh giá nhằm chọn ra phương pháp tốt nhất cho việc lập bản đồ nguy cơ lũ lụt trong thành phố có mật độ đô thị đông đúc. Do đó, ban đầu, mười ba yếu tố dự đoán lũ lụt đã được đánh giá bằng tỷ lệ thu được thông tin (IGR), và tám yếu tố dự đoán quan trọng nhất đã được xem xét từ quá trình huấn luyện và kiểm tra mô hình. Hiệu suất của các mô hình áp dụng và độ chính xác của kết quả được đánh giá thông qua diện tích dưới đường cong (AUC) và các chỉ số thống kê. Sử dụng tập dữ liệu kiểm tra, kết quả cho thấy DLNN (AUC = 0.877) là mô hình có hiệu suất cao hơn so với ANN (AUC = 0.851) và BLR (AUC = 0.697). Tuy nhiên, mô hình BLR có khả năng dự đoán thấp nhất. Các kết quả của nghiên cứu hiện tại có thể được sử dụng một cách hiệu quả cho các chiến lược quản lý lũ lụt đô thị.

Từ khóa

#lũ #nguy cơ lũ lụt #hồi quy logistic bayesian #mạng nơ-ron nhân tạo #mạng nơ-ron sâu #quản lý lũ lụt đô thị

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