Phương pháp học sâu tuần tự tối ưu hóa cấu trúc cho dự báo độ ẩm đất bề mặt, nghiên cứu trường hợp tỉnh Quebec, Canada

Mohammad Zeynoddin1, Hossein Bonakdari1
1Department of Soils and Agri‐Food Engineering, Laval University, Québec, Canada

Tóm tắt

Độ ẩm đất bề mặt (MSS) là yếu tố chính điều khiển các tương tác môi trường trong bất kỳ lưu vực nào. Dòng năng lượng giữa đất và khí quyển, nhiệt độ đất, và sự khuếch tán nhiệt trong đất là những ví dụ về các tương tác không thể hiện rõ. Do đó, ngành nông nghiệp và nhiều ngành công nghiệp phụ thuộc vào nó bị ảnh hưởng bởi yếu tố này. Chính vì vậy, việc nghiên cứu các phương pháp lựa chọn đầu vào và tiền xử lý tối ưu hóa mới cho việc xử lý, diễn giải, mô hình hóa và dự đoán MSS là cần thiết để đảm bảo nông nghiệp bền vững. Để đạt được điều này, các sản phẩm vệ tinh đã được nghiên cứu cho tỉnh Quebec, Canada. Hai phương pháp học sâu (DL) tổng thể được đề xuất trong nghiên cứu này. Phương pháp đầu tiên và hiệu quả nhất là trích xuất các tham số mô hình có ý nghĩa bằng phương pháp phân tích cấu trúc chuỗi thời gian, và phương pháp thứ hai là sử dụng sự kết hợp của các thuật toán tối ưu hóa và phương pháp DL. Cấu trúc của chuỗi thời gian được trích xuất từ dữ liệu vệ tinh đã được đánh giá qua một số bài kiểm tra và một mẫu chu kỳ mạnh mẽ đã được phát hiện. Do đó, Holt-Winter cộng tính (SHW), chuẩn hóa theo mùa (Sstd) và phân tích quang phổ (SA) được chọn làm phương pháp tiền xử lý cho phân tích cấu trúc. Mô hình bộ nhớ ngắn hạn dài (LSTM) đã được sử dụng cho dự đoán ngắn hạn của các tập dữ liệu MSS chưa qua tiền xử lý và đã qua tiền xử lý. Cùng với phương pháp dựa trên phân tích cấu trúc, các thuật toán di truyền và người dạy-người học (GA và TLA) đã được kết hợp với LSTM để đánh giá hiệu suất của các mô hình kết hợp cho dự đoán MSS lần đầu tiên. Dựa trên phân tích cấu trúc của dữ liệu, các trạng thái ẩn hạn chế (ht) được chọn cho mô hình {1, 2, 7, 9, 52}: việc đào tạo mạng và dự đoán được thực hiện theo các trạng thái ẩn này. Vì các đặc điểm dài hạn của chuỗi thời gian như xu hướng và mức độ không đáng kể trong mô hình ngắn hạn, LSTM (Sstd, 9), hệ số tương quan (R)=0.970, sai số bình quân căn bậc hai (RMSE)=1.339 đã vượt trội hơn so với các mô hình khác, tiếp theo là LSTM (SHW, 1), R=0.922, RMSE=1.958. Ngược lại, cho dự đoán dài hạn, khi các thuộc tính này ảnh hưởng đến cấu trúc, LSTM (SHW, 2), R=0.922, RMSE=0.1961 đã thành công hơn trong việc dự đoán các mẫu và biến động, theo sau là LSTM (Sstd, 52), R=0.920, RMSE=2.064, phức tạp hơn mô hình phát triển cho mô hình ngắn hạn. GA-LSTM (ht=32, R=0.930, RMSE=1.852) và TLA-LSTM (ht=37, R=0.934, RMSE=1.781) cũng đã cải thiện kết quả dự đoán dài hạn. Sự kết hợp của hai phương pháp tối ưu hóa này có hai lợi ích. Thứ nhất, do tính ngẫu nhiên của các thuật toán tối ưu hóa và phương pháp DL, không gian tìm kiếm cho tham số tối ưu hóa (ht) đã được mở rộng rất nhiều và nhiều khả năng đã được kiểm tra. Thứ hai, LSTM có thể thực hiện dự đoán dài hạn của MSS mà không cần tiền xử lý, điều này không thể thực hiện bởi phân tích cấu trúc. Mặt khác, các phương pháp này tốn kém tính toán và sự kết hợp của các tham số điều khiển của chúng với các tham số điều khiển khác của LSTM đã tạo ra vô số khả năng. Tuy nhiên, do TLA không có tham số và ít phức tạp hơn GA, nó là một phương pháp hiệu quả hơn về tính toán, và do đó là một lựa chọn tốt hơn so với GA.

Từ khóa


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