Can fourier neural operators replicate the intrinsic predictability of spatiotemporal chaos?: for the Kuramoto-Sivashinsky system

The use of deep learning in global weather forecasting has shown significant promise in improving both forecasting accuracy and speed. Traditional numerical weather prediction models have gradually improved forecasting skills but at the cost of increased computational complexity. In contrast, new deep learning models, trained directly on...

Photovoltaic Yield Nowcasting: For Residential Solar Systems in the Netherlands Using a Machine Learning Approach

An increasing number of photovoltaic (PV) systems are being installed worldwide and residential sector is responsible for a large part of this growth. Small scale PV systems do not have complex measuring devices and their breakdowns are not spotted immediately by the system owners. This might lead to prolonged time without generating power and...

Estimating real-time predictive hydrological uncertainty

Flood early warning systems provide a potentially highly effective flood risk reduction measure. The effectiveness of early warning, however, is affected by forecasting uncertainty: the impossibility of knowing, in advance, the exact future state of hydrological systems. Early warning systems benefit from estimation of predictive uncertainties,...

The Variability of Traffic in Congestion Forecasting

Congestion on the Dutch motorway network is an actual problem, which originated over the past decades. Over the past few years, the extent of congestion is decreasing, though still very significant. Road authorities therefore show interest in traffic congestion forecasting. In this way they can inform road users or undertake other strategic...

To what extent does electronic database information reflect the process of innovation?

This research aims to investigate whether adding electronic database information with the technology life cycle reflects the process of innovation. The chances of successfully market a news product are dramatically low, resulting in the need for methods to forecast the process of innovation. A well-known method is the use of market research....

Modeling Traffic Information using Bayesian Networks

Dutch freeways suffer from severe congestion during rush hours or incidents. Research shows that 64% of congested traffic during rush hour consists of commuter traffic [30]. A traffic congestion increases travel time, resulting in a delay for travelers. Reliable travel time predictions are essential for Dynamic Routing, in which travelers can be...

Extension of the Flood Forecasting Model FloRIJN

After the floods of 1993 and 1995 in the Rhine basin, the ICPR Action Plan on Flood Defence (ICPR, 1998) was drawn up, after which the environmental affairs ministers of the Rhine riparian states agreed on several meas-ures on the subject of improvement of flood forecasting and warning systems in the Rhine basin. One of the targets of this...