Khả năng ứng dụng của máy vector hỗ trợ và hệ thống suy diễn mờ thích ứng trong mô hình hóa sự bốc hơi nước của cây khoai tây

Springer Science and Business Media LLC - Tập 31 - Trang 575-588 - 2012
Hossein Tabari1, Christopher Martinez2, Azadeh Ezani3, P. Hosseinzadeh Talaee4
1Department of Water Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran
2Department of Agricultural and Biological Engineering, University of Florida, Gainesville, USA
3Department of Water Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
4Young Researchers Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran

Tóm tắt

Việc ước lượng sự bốc hơi nước cây trồng (ETC) cho một số cây như khoai tây là rất quan trọng cho việc lập kế hoạch tưới tiêu, lập lịch tưới và quản lý hệ thống tưới. Mục tiêu chính của nghiên cứu này là kiểm tra độ chính xác của hệ thống suy diễn mờ thích ứng (ANFIS) và máy vector hỗ trợ (SVM) trong việc ước lượng ETC của khoai tây khi không có số liệu từ lysimeter hoặc dữ liệu thời tiết đầy đủ để áp dụng phương pháp FAO. Các ước lượng từ các mô hình ANFIS và SVM được so sánh với các phương trình thực nghiệm của Blaney–Criddle, Makkink, Turc, Priestley–Taylor, Hargreaves và Ritchie. Hiệu suất của các mô hình SVM và ANFIS khác nhau được đánh giá bằng cách so sánh các giá trị tương ứng của sai số bình quân căn bậc hai (RMSE), sai số tuyệt đối trung bình (MAE) và hệ số tương quan (r). Các kết luận rút ra khẳng định rằng các mô hình SVM và ANFIS có thể cung cấp các ước lượng ETC chính xác hơn so với các phương trình thực nghiệm. Tổng quát, giá trị RMSE tối thiểu (0.042 mm/ngày) và MAE (0.031 mm/ngày) cũng như giá trị r tối đa (0.98) được thu obtained từ mô hình SVM với nhiệt độ không khí trung bình, độ ẩm tương đối, bức xạ mặt trời, số giờ nắng và tốc độ gió là đầu vào.

Từ khóa

#bốc hơi nước cây trồng #khoai tây #hệ thống suy diễn mờ thích ứng #máy vector hỗ trợ #ước lượng ETC #lập kế hoạch tưới tiêu

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