Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Intelligent transportation systems based on automated data collection frameworks are widely
used by the major transit companies around the globe. This paper describes the current …

This paper considers the generation of prediction intervals (PIs) by neural networks for
quantifying uncertainty in regression tasks. It is axiomatic that high-quality PIs should be as …

Modern machine learning (ML) techniques, in conjunction with simulation-based methods,
present remarkable potential for various scientific and engineering applications. Within the …

Short term traffic flow forecasting has received sustained attention for its ability to provide the
anticipatory traffic condition required for proactive traffic control and management. Recently …

Interval prediction of electric load has aroused widespread concern by the power industry
because of variability and uncertainty. To quantify the potential uncertainty associated with …

Spatiotemporal problems are ubiquitous and of vital importance in many research fields.
Despite the potential already demonstrated by deep learning methods in modeling …

Fault prediction has arisen as a basic monitoring strategy that predicts an abnormal event
occurring in near future based on the current symptoms observed in a process. Such a …

The accurate prediction of travel times is desirable but frequently prone to error. This is
mainly attributable to both the underlying traffic processes and the data that are used to infer …

Obtaining accurate point prediction of industrial processes' key variables is challenging due
to the outliers and noise that are common in industrial data. Hence the prediction intervals …