Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Phương pháp kết hợp máy học và địa lý thống kê để cải thiện độ chính xác dự đoán không gian của các nguyên tố có thể độc hại trong đất
Tóm tắt
Quản lý môi trường hiệu quả và khắc phục ô nhiễm yêu cầu phân bố không gian chính xác và dự đoán các nguyên tố có thể độc hại (PTEs) trong đất. Tuy nhiên, không có phương pháp đơn lẻ nào được phát triển để dự đoán PTE trong đất một cách chính xác. Nghiên cứu này đánh giá phương pháp địa lý thống kê tiên tiến của dự đoán hồi quy kriging Bayes kinh nghiệm (EBKRP), thuật toán máy học của rừng ngẫu nhiên (RF), và mô hình kết hợp (RF-EBKRP) để dự đoán và lập bản đồ hàm lượng PTE trong đất xanh. Như được xác định bởi RF, carbon hữu cơ trong đất, chất hữu cơ, tổng (nitơ, phốt pho và kali), các đặc điểm địa hình, và các loại chức năng đô thị đã được sử dụng làm các biến đồng liên quan quan trọng để cải thiện độ chính xác dự đoán của PTE trong đất. Hiệu suất dự đoán của mô hình được đánh giá bằng cách sử dụng sai số bình quân gốc (RMSE), sai số tỷ lệ trung bình tuyệt đối (MAPE), và hệ số xác định (R2). Kết quả cho thấy RF hoạt động tốt hơn nhiều so với EBKRP trong việc dự đoán PTE trong đất, với các sai số dự đoán thấp hơn và R2 cao hơn. Giá trị RMSE, MAPE, và R2 cho mô hình RF lần lượt là 0.25–85.32 mg/kg, 3.86–25.40%, và 0.77–0.90, trong khi các giá trị cho phương pháp EBKRP là 0.51–99.03 mg/kg, 5.42–32.13%, và 0.40–0.66. Hơn nữa, phương pháp RF-EBKRP tạo ra các dự đoán không gian và phân bố PTE chính xác hơn so với các mô hình riêng lẻ, với sự cải thiện R2 là 122.5% cho EBKRP và 15.58% cho RF. Hiệu suất tốt hơn của RF-EBKRP là do sự kết hợp của nhiều biến đồng liên quan và khả năng xử lý các mối quan hệ phi tuyến phức tạp giữa PTE trong đất và các biến đồng liên quan. Cuối cùng, phương pháp RF-EBKRP kết hợp là một phương pháp hứa hẹn để cải thiện bản đồ phân bố không gian của PTE trong đất.
Từ khóa
#PTE #không gian #hồi quy kriging Bayes kinh nghiệm #rừng ngẫu nhiên #mô hình kết hợp #dự đoán không gian.Tài liệu tham khảo
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