Journal: Stochastic Environmental Research and Risk Assessment

Abbreviation

Stoch. environ. res. risk assess.

Publisher

Springer

Journal Volumes

ISSN

1436-3240
1436-3259

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Publications 1 - 10 of 11
  • Dimensionality reduction for efficient Bayesian estimation of groundwater flow in strongly heterogeneous aquifers
    Item type: Journal Article
    Mara, Thierry A.; Fajraoui, Noura; Guadagnini, Alberto; et al. (2017)
    Stochastic Environmental Research and Risk Assessment
  • Spatiotemporal data science: theoretical advances and applications
    Item type: Other Journal Item
    Amato, Federico; Lombardo, Luigi; Tonini, Marj; et al. (2022)
    Stochastic Environmental Research and Risk Assessment
  • Is there a "Forest Filter Effect" for airborne organic pollutants?
    Item type: Journal Article
    Scheringer, Martin; McKone, T.E. (2003)
    Stochastic Environmental Research and Risk Assessment
  • Are airborne organic pollutants subject to a "forest filter effect"?
    Item type: Journal Article
    Wegmann, F.; Scheringer, Martin; Möller, M.; et al. (2003)
    Stochastic Environmental Research and Risk Assessment
  • Climate change and modelling of extreme temperatures in Switzerland
    Item type: Journal Article
    Siliverstovs, Boriss; Ötsch, Rainald; Kemfert, Claudia; et al. (2010)
    Stochastic Environmental Research and Risk Assessment
  • Exact transverse macro dispersion coefficients for transport in heterogeneous porous media
    Item type: Journal Article
    Attinger, S.; Dentz, M.; Kinzelbach, W. (2004)
    Stochastic Environmental Research and Risk Assessment
  • On the use of observations in assessment of multi-model climate ensemble
    Item type: Journal Article
    Xu, Donghui; Ivanov, Valeriy Y.; Kim, Jongho; et al. (2019)
    Stochastic Environmental Research and Risk Assessment
  • Climate change and uncertainty assessment over a hydroclimatic transect of Michigan
    Item type: Journal Article
    Kim, Jongho; Ivanov, Valeriy Y.; Fatichi, Simone (2016)
    Stochastic Environmental Research and Risk Assessment
  • An enhanced fourier neural operator surrogate for radioactive plume transport forecasting
    Item type: Journal Article
    Ayoub, Ali; Wainwright, Haruko M.; Wang, Lijing; et al. (2024)
    Stochastic Environmental Research and Risk Assessment
    Accurate real-time forecasts of atmospheric plume behavior are crucial for effective management of environmental release incidents. However, the computational demands of weather simulations and particle transport codes limit their applicability during emergencies. In this study, we employ a U-Net enhanced Fourier Neural Operator (U-FNO) to statistically emulate the calculations of the WSPEEDI dose forecasting numerical simulator, using pre-calculated ensemble simulations. The developed emulator is capable of effectively simulating any radioactive-release scenario and generating the time series of dose distribution in the environment 4000 times faster than the numerical simulator, while still maintaining high accuracy. It predicts the plume direction, extent, and dose-rate magnitudes using initial- and boundary-condition meteorological data as input. The speed and efficiency of this framework offers a powerful tool for swift decision-making during emergencies, facilitating risk-informed protective actions, evacuation execution, and zone delineation. Its application extends to various contaminant release and transport problems, and can be instrumental in engineering tasks requiring uncertainty quantification (UQ) for environmental risk assessment.
  • Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential
    Item type: Journal Article
    Amato, Federico; Guignard, Fabian; Walch, Alina; et al. (2022)
    Stochastic Environmental Research and Risk Assessment
    With wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related uncertainty is crucial for energy planners and policy-makers. Here, we propose a methodological framework which (1) uses machine learning to reconstruct a spatio-temporal field of wind speed on a regular grid from spatially irregularly distributed measurements and (2) transforms the wind speed to wind power estimates. Estimates of both model and prediction uncertainties, and of their propagation after transforming wind speed to power, are provided without any assumptions on data distributions. The methodology is applied to study hourly wind power potential on a grid of 250 × 250 m2 for turbines of 100 m hub height in Switzerland, generating the first dataset of its type for the country. We show that the average annual power generation per turbine is 4.4 GWh. Results suggest that around 12,000 wind turbines could be installed on all 19,617 km2 of available area in Switzerland resulting in a maximum technical wind potential of 53 TWh. To achieve the Swiss expansion goals of wind power for 2050, around 1000 turbines would be sufficient, corresponding to only 8% of the maximum estimated potential.
Publications 1 - 10 of 11