Water | Free Full-Text | Deep Learning Framework with Time Series Analysis Methods for
Runoff Prediction Next Article in Journal Understanding Public Acceptance of a Multifunctional …

Time series data from environmental monitoring stations are often analysed with machine
learning methods on an individual basis, however recent advances in the machine learning …

Deep neural networks are powerful models capable of learning useful representations from
large complex datasets for the purpose of prediction. Such models offer great potential in …

Two predominant methodologies in forecasting temporal processes include traditional time
series models and machine learning methods. This paper investigates the impact of time …

To capture the complexity of hydrological systems across regions, multidimensional domain
knowledge (eg climate, soils, geology and topography) can be incorporated into deep …

The increase in the use of Renewable Energy Sources (RES) provides many advantages
such as reducing the environmental problems and sustainability. In this study, a long-term …

In this article, we aim to improve the prediction from experts' aggregation by using the
underlying properties of the models that provide the experts involved in the aggregation …

Supervised time-series classification garners widespread interest because of its applicability
throughout a broad application domain including finance, astronomy, biosensors, and many …

Despite the unabated growth of algorithmic decision-making in organizations, there is a
growing consensus that numerous situations will continue to require humans in the loop …

Within demand forecasting, and specifically within the field of e-commerce, the provided
data often contains erratic behaviours which are difficult to explain. This induces …