一种预测河流溶解N2O浓度的机器学习方法
By combining river information, water and sediment physicochemical parameters, and microbial composition and function data into a machine learning model, the problem of accuracy in predicting river N2O concentration was solved, achieving high-precision N2O concentration prediction and optimization of emission reduction strategies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUNAN UNIV
- Filing Date
- 2025-04-30
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to accurately predict river N2O concentrations at different scales, and traditional methods fail to reflect spatiotemporal variability in local environments. Machine learning models lack sediment parameters and cannot fully capture the complex processes of N2O generation and emission.
Using machine learning methods, combined with river information, physicochemical parameters of water and sediment, and microbial composition and function data, classification and regression models were constructed to predict N2O concentration using the optimal dataset. The correlation between microorganisms and environmental factors was analyzed to reveal the N2O generation mechanism.
It enables high-precision prediction of N2O concentration at different scales, identifies key driving factors, optimizes management strategies, and provides a more comprehensive emission reduction strategy.
Smart Images
Figure CN120688657B_ABST