Data-driven models have been widely developed and achieved impressive results in
streamflow prediction. However, the existing data-driven models mostly focus on the …

Industrial big data technology has become one of the important driving forces to intelligent
manufacturing in the steel industry. In this study, the characteristics of data in steel …

As an important branch of time series forecasting, runoff forecasting provides a reliable
decision-making basis for the rational use of water resources, economic development and …

Data-driven models have been successfully applied in hydrological fields such as flood
forecasting. However, limitations to the solutions to scientific problems still exist in this field …

Building energy consumption prediction is critical for building energy management and
energy policy formulation, and its inherent uncertainty can significantly affect the utilization of …

Spatiotemporal deep learning (DL) has emerged as a promising paradigm for hydrological
simulation compared with lumped models using basin-averaged inputs. However, existing …

Point prediction has been used to predict air pollutant concentrations in recent years.
However, it is still a challenge to characterize the time series data of pollutant concentrations …

Runoff forecasting is the key to the rational use and protection of water resources by
mankind. The large-scale application of machine learning and neural networks in …

Uncertainty prediction of vessel trajectory is essential to enhance maritime situational
awareness and traffic safety. Traditional approaches for trajectory prediction face challenges …

Due to the underlying uncertainty in groundwater level (GWL) modelling, point prediction of
GWLs does not provide sufficient information. Moreover, the insufficiency of data on subjects …