Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural Network.

• Tác giả: Hong-Xu Bao, Jia-Ji Chen, Ke-Hua Chen, Fang Huang, Shuai Liu, Jia-Qiang Lv, Nan-Qi Ren, Yun-Peng Song, Ai-Jie Wang, Hong-Cheng Wang, Wan-Xin Yin, Yi-Wei Zhao
• Ngôn ngữ: eng
• Ký hiệu phân loại: 794.147 King
• Thông tin xuất bản: United States : Environmental science & technology , 2025
• Bộ sưu tập: NCBI
• ID: 65337

Tóm tắt

Deciphering and mitigating dynamic greenhouse gas (GHG) emissions under environmental fluctuation in urban drainage systems (UDGSs) is challenging due to the absence of a high-prediction model that accurately quantifies the contributions of biological production pathways. Here we infused biological production pathways into the graph neural network (GNN) model architecture, developing ecological knowledge-infused GNN (EcoGNN-GHG) models to evaluate methane (CH