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Statistical modelling of the snow depth distribution in open alpine terrain

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Snowfall deposition in mountainous terrain: a statistical downscaling scheme from high-resolution model data on simulated topographies

Michael Lehning

One of the primary causes of non-uniform snowfall deposition on the ground in mountainous regions is the preferential deposition of snow, which results from the interaction of near-surface winds with topography and snow particles. However, producing high-r ...

Lausanne2024

At what spatial scale does natural selection operate? Using multiscale frameworks to calibrate the effects of environmental factors across landscape continuums

Annie Sandrine Guillaume

Patterns in nature arise from processes interacting across a continuum of spatial scales, where new relationships emerge at each level of investigation. These patterns are nested features encompassing fine-scale local patterns, such as topography and geolo ...

EPFL2024

Ozonation of secondary-treated wastewater for the abatement of micropollutants requires a reliable control of ozone doses. Changes in the UV absorbance of dissolved organic matter (DOM) during ozonation allow to estimate micropollutant abatement on-line an ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Dataset for the paper "Influence of hydrograph shape and sediment augmentation repetition frequency on sediment transport dynamics and bed morphology evolution". It includes coordinates of the defined areas of interest and digital elevation models from the ...

2022

Multi-modal interactions at the network-level remain unexplored due to the lack of highresolution data for all transportation modes involved. The current work investigates the effect of multi-modal interactions at space-mean network speed for each mode usi ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

Zhenzhu Meng, Yating Hu, Chenfei Shao

Both numerical simulations and data-driven methods have been applied in dam's displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and ...

2020

Improve the Model Stability of Dam's Displacement Prediction Using a Numerical-Statistical Combined Model

Zhenzhu Meng, Yating Hu, Chenfei Shao

In most studies of dam's displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to im ...

2020

Enhancing discrete choice models with representation learning

Alexandre Massoud Alahi, Virginie Janine Camille Lurkin

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specif ...

2020

Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

Charlotte Marie Bürki

The emission parameterization is a crucial part of numerical pollen dispersion models. This paper shows that Artificial Neural Networks (ANNs) can substantially improve the performance of the Ambrosia pollen emission in numerical pollen dispersion models s ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Knowledge about the spatial distribution of seasonal snow is essential e.g. to efficiently manage fresh water resources or for hydro-power companies. The large-scale gradient of snow accumulation over mountain ranges is mainly determined by lifting condens ...

EPFL2018