Phương pháp trí tuệ tính toán kết hợp để dự đoán nhiệt độ điểm sương

Springer Science and Business Media LLC - Tập 75 - Trang 1-12 - 2016
Mohsen Amirmojahedi1, Kasra Mohammadi2, Shahaboddin Shamshirband3, Amir Seyed Danesh4, Ali Mostafaeipour5, Amirrudin Kamsin3
1Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
2Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, USA
3Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
4Payame Noor University, Rasht branch, Iran
5Industrial Engineering Department, Yazd University, Yazd, Iran

Tóm tắt

Gần đây, việc sử dụng các mô hình kết hợp đã thu hút sự chú ý đáng kể khi chúng tận dụng được những đặc điểm riêng của từng kỹ thuật để nâng cao độ chính xác và độ tin cậy của các dự đoán. Trong nghiên cứu này, một phương pháp kết hợp mới kết hợp máy học cực đoan (ELM) với thuật toán biến đổi wavelet (WT) được đề xuất để dự đoán nhiệt độ điểm sương hàng ngày. Để kiểm tra tính hợp lệ của phương pháp này, tập dữ liệu thời tiết hàng ngày cho cảng Bandar Abass nằm ở phía nam bờ biển Iran được sử dụng. Giá trị của phương pháp ELM-WT được xác minh so với ELM, máy vector hỗ trợ và mạng nơ-ron nhân tạo dựa trên một số chỉ số thống kê nổi tiếng. Kết quả đạt được cho thấy phương pháp ELM-WT kết hợp thể hiện sự vượt trội tuyệt đối so với các kỹ thuật mạnh khác được áp dụng. Trong bốn tập hợp tham số đã xem xét với 1, 2 và 3 đầu vào, độ chính xác cao hơn có thể đạt được bằng cách sử dụng tổ hợp của nhiệt độ môi trường trung bình (T_avg) và độ ẩm tương đối (R_h). Đối với mô hình ELM-WT tốt nhất sử dụng T_avg và R_h làm đầu vào, các chỉ số thống kê của sai số phần trăm tuyệt đối trung bình, sai số thành phần tuyệt đối trung bình, sai số bình phương trung bình và hệ số xác định lần lượt là 6,1664 %, 0,5495, 0,7621 và 0,9953 °C. Dựa trên sai số phần trăm tương đối (RPE), đối với mô hình ELM-WT tốt nhất, 91 % các dự đoán nằm trong phạm vi chấp nhận được của RPE từ -10 đến +10 %. Tóm lại, kết quả của nghiên cứu này một cách thuyết phục cho thấy rằng việc kết hợp ELM với WT sẽ là một lựa chọn hấp dẫn để cung cấp các dự đoán chính xác và cải tiến đáng kể độ chính xác của ELM.

Từ khóa

#học máy #nhiệt độ điểm sương #biến đổi wavelet #mô hình kết hợp #trí tuệ tính toán

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