Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems.

ABSTRACT: This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting. Up-to-date literature shows a lack of research on selecting practical ML algorithms for short-term forecasting in real-time industrial applications. This research uses a quantitative and qualitative mixed method combining two rounds of literature reviews, a case study, and a comparative analysis. Ten widely used ML algorithms are selected to conduct a comparative study of gas warning systems in a case study mine. We propose a new assessment visualization tool: a 2D space-based quadrant diagram can be used to visually map prediction error assessment and predictive performance assessment for tested algorithms. Overall, this visualization tool indicates that LR, RF

SUBMITTER: Wu RMX 

PROVIDER: S-EPMC11415518 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature