Nghiên cứu So sánh các Hàm Chuyển Cấp Dựa trên Thống Kê, Số Học và Học Máy về Đường Cong Giữ Nước với Dữ liệu Phân Bố Kích Thước Hạt

S. Amanabadi1,2, M. Vazirinia2, H. Vereecken3, K. Asefpour Vakilian4, M. H. Mohammadi1
1Department of Soil Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2Department of Soil Science, Faculty of Agriculture and Natural Resources, Science and Research Branch, Islamic Azad University, Tehran, Iran
3Agrosphere Institute, IBG-3, Forschungszentrum Jülich GmbH, Jülich, Germany
4Department of Agrotechnology, College of Abouraihan, University of Tehran, Tehran, Iran

Tóm tắt

Đường cong giữ nước (WRC) mô tả mối quan hệ phi tuyến giữa hàm lượng nước trong đất (SWC) và tiềm năng ma trận. Do việc đo trực tiếp SWC và tiềm năng ma trận gặp nhiều khó khăn và tốn thời gian, các phương pháp gián tiếp bao gồm các hàm chuyển cấp (PTFs) dựa trên thống kê, số học và nhận dạng mẫu đã được phát triển trong vài thập kỷ qua để liên hệ các thuộc tính cơ bản của đất với WRC. Mặc dù có nhiều nghiên cứu báo cáo hiệu suất của các mô hình này trong tài liệu, nhưng có vẻ như một cuộc điều tra toàn diện so sánh các mô hình hiện có và giới thiệu một phương pháp đáng tin cậy cho các nhà thủy văn đất có thể là hữu ích. Do đó, trong nghiên cứu này, hiệu suất của các mô hình hồi quy tuyến tính bội (MLR), các mô hình số đã được điều chỉnh và các phương pháp học máy bao gồm mạng nơ-ron nhân tạo (ANN) và hệ thống suy diễn mờ thích nghi (ANFIS) được so sánh bằng cách sử dụng 98 mã UNSODA với các kết cấu đất khác nhau để ước lượng WRC. Kết quả cho thấy bất chấp kết cấu đất, ANN (RMSE = 0.029) dự đoán WRC chính xác hơn ANFIS (RMSE = 0.035), mô hình được điều chỉnh (RMSE = 0.060) và MLR (RMSE = 0.071), tương ứng. Xét về kết cấu đất, hiệu suất của ANFIS là tốt nhất trong các loại đất trung bình và mịn, trong khi mô hình số được điều chỉnh có hiệu suất chấp nhận được trong các loại đất cát. Dự đoán WRC bằng cách sử dụng các đặc tính đất dễ dàng có sẵn, đặc biệt khi thiếu dữ liệu cho thấy các phương pháp học máy mới phát triển có khả năng dự đoán WRC với độ chính xác đáng kể cho việc quản lý dòng nước và vận chuyển chất hòa tan bền vững.

Từ khóa

#Đường cong giữ nước #hồi quy tuyến tính bội #học máy #hàm chuyển cấp #mạng nơ-ron nhân tạo #hệ thống suy diễn mờ thích nghi

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