Quantifying the heterogeneous impacts of the urban built environment on traffic carbon emissions: New insights from machine learning techniques

The configuration of the urban built environment is critical for promoting sustainability and achieving carbon neutrality. However, existing studies mostly use linear and spatial econometric models to investigate the relationship between urban built environments and traffic carbon dioxide (CO₂) emissions, in-depth studies exploring...

Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area

Safety analysis according to the spatial distribution characteristics of maritime traffic accidents is critical to maritime traffic safety management. An accident analysis framework based on the geographic information system (GIS) is proposed to characterize the spatial distribution of maritime traffic accidents occurring in the Fujian sea area...

Data-driven Methods to Predict the Burst Strength of Corroded Line Pipelines Subjected to Internal Pressure

A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines, especially those served for a long time. Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion. However, it is time-consuming for finite-element method and there is a limited...

Longitudinal tear detection method of conveyor belt based on audio-visual fusion

Conveyor belt tear detection is a very important part of coal mine safety production. In this paper, a new method of detecting conveyor belt damage named audio-visual fusion (AVF) detection method is proposed. The AVF method uses both a visible light CCD and a microphone array to collect images and sounds of the conveyor belt in different...

Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning

The atmospheric corrosion of carbon steel was monitored by a Fe/Cu type galvanic corrosion sensor for 34 days. Using a random forest (RF)-based machine learning approach, the impacts of relative humidity, temperature and rainfall were identified to be higher than those of airborne particles, sulfur dioxide, nitrogen dioxide, carbon monoxide...

Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China

Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time...

Hierarchical radar data analysis for activity and personnel recognition

Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in...