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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 ...
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 ...
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 ...
We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...
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 ...
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes o ...
In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, b ...
The objective of this thesis is to develop a general methodology to incorporate a disaggregate demand representation in supply-oriented optimization problems that allows to capture the interplay between the behavior of individuals and the decisions to be o ...
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 ...
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 ...
