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Đánh giá phương pháp trung bình mô hình Bayes trong quản lý nước ngầm dưới sự không chắc chắn về cấu trúc mô hình
Tóm tắt
Nghiên cứu này giới thiệu phương pháp trung bình mô hình Bayes (BMA) để xử lý sự không chắc chắn về cấu trúc mô hình trong các quyết định quản lý nước ngầm. Một chính sách tối ưu đáng tin cậy nên xem xét sự không chắc chắn của các tham số mô hình cũng như sự không chắc chắn trong cấu trúc mô hình không chính xác. Do lượng dữ liệu đầu nước ngầm và dữ liệu dẫn thủy lực hạn chế, một số mô hình mô phỏng được phát triển dựa trên các giá trị điều kiện biên đầu nước khác nhau và các mô hình bán phương sai của dẫn thủy lực. Thay vì chọn mô hình mô phỏng tốt nhất, một phương pháp BMA dựa trên cửa sổ biến thiên được giới thiệu vào mô hình quản lý để tận dụng tất cả các mô hình mô phỏng nhằm dự đoán nồng độ chloride. Với các mô hình bán phương sai khác nhau, các phân phối dẫn thủy lực có tương quan theo không gian được ước lượng bằng phương pháp tham số hóa tổng quát (GP) kết hợp các vùng Voronoi và các ước lượng kriging thông thường (OK). Trọng số mô hình của BMA được ước lượng bằng tiêu chí thông tin Bayes (BIC) và cửa sổ biến thiên trong ước lượng cực đại khả năng. Các mô hình mô phỏng sau đó được trọng số để dự đoán nồng độ chloride trong các ràng buộc của mô hình quản lý. Phương pháp này được thực hiện để quản lý sự xâm nhập nước mặn trong tầng chứa nước cát “1.500-foot” tại khu vực Baton Rouge, Louisiana. Mô hình quản lý nhằm mục tiêu đạt được các hoạt động phối hợp tối ưu của hệ thống hàng rào thủy lực và hệ thống khai thác nước mặn để giảm thiểu sự xâm nhập nước mặn. Một thuật toán di truyền (GA) được sử dụng để có được các chính sách bơm và khai thác tối ưu. Sử dụng các dự đoán của BMA, cần có các tỷ lệ bơm cao hơn và tỷ lệ khai thác lớn hơn để che phủ nhiều vi phạm ràng buộc hơn, điều này sẽ không xảy ra nếu chỉ sử dụng một mô hình tốt nhất.
Từ khóa
#quản lý nước ngầm #phương pháp trung bình mô hình Bayes #không chắc chắn về cấu trúc mô hình #mô phỏng #xâm nhập nước mặn #thuật toán di truyềnTài liệu tham khảo
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