Environmental Modelling & Software

Journal: Environmental Modelling & Software

Abbreviation

Environ. Model. Softw.

Publisher

Elsevier

ISSN

1364-8152
1873-6726

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Publications 1 - 10 of 66

Groundwater recharge predictions in contrasted climate: The effect of model complexity and calibration period on recharge rates
Moeck, Christian; von Freyberg, Jana; Schirmer, Mario (2018)
Environmental Modelling & Software
Implications of data sampling resolution on water use simulation, end-use disaggregation, and demand management
Cominola, Andrea; Giuliani, Matteo; Castelletti, Andrea; et al. (2018)
Environmental Modelling & Software
Snow avalanche hazard modelling of large areas using shallow water numerical methods and GIS
Gruber, U.; Bartelt, P. (2007)
Environmental Modelling & Software
An Object-oriented computational framework for the simulation of variably saturated flow in soils, using a reduced complexity model
Anagnostopoulos, Grigorios G.; Burlando, Paolo (2012)
Environmental Modelling & Software
Model bias and complexity - Understanding the effects of structural deficits and input errors on runoff predictions
Del Giudice, D.; Reichert, P.; Bares, V.; et al. (2015)
Environmental Modelling & Software
Scenario techniques for energy and environmental research: An overview of recent developments to broaden the capacity to deal with complexity and uncertainty
Guivarch, Céline; Lempert, Robert; Trutnevyte, Evelina (2017)
Environmental Modelling & Software
Active learning for anomaly detection in environmental data
Russo, Stefania; Lürig, Moritz; Hao, Wenjin; et al. (2020)
Environmental Modelling & Software
Due to the growing amount of data from in-situ sensors in environmental monitoring, it becomes necessary to automatically detect anomalous data points. Nowadays, this is mainly performed using supervised machine learning models, which need a fully labelled data set for their training process. However, the process of labelling data is typically cumbersome and, as a result, a hindrance to the adoption of machine learning methods for automated anomaly detection. In this work, we propose to address this challenge by means of active learning. This method consists of querying the domain expert for the labels of only a selected subset of the full data set. We show that this reduces the time and costs associated to labelling while delivering the same or similar anomaly detection performances. Finally, we also show that machine learning models providing a nonlinear classification boundary are to be recommended for anomaly detection in complex environmental data sets. © 2020 The Authors
Combining expert knowledge and local data for improved service life modeling of water supply networks
Scholten, Lisa; Scheidegger, Andreas; Reichert, Peter; et al. (2013)
Environmental Modelling & Software
The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction
Rinderknecht, Simon L.; Albert, Carlo; Borsuk, Mark E.; et al. (2014)
Environmental Modelling & Software
A systemic approach to managing uncertainties in repetitive multibeam bathymetric surveys
Sauter, Gaétan; Fabbri, Stefano C.; Frischknecht, Corine; et al. (2025)
Environmental Modelling & Software
Multibeam Echo Sounder systems have enhanced the precision of modern bathymetric mapping, enabling the creation of high-resolution digital bathymetry models that characterise ocean and lake floors. However, the inferred models contain uncertainties that necessitate consideration, especially when conducting quantitative temporal comparisons. By exploring the results of two bathymetric surveys targeting a lacustrine delta, this study examines how geomorphological changes can effectively be interpreted through repetitive multi-temporal bathymetric surveys. We propose to use a workflow for Geographic Information System aiming at providing the basis for diverse studies that will implement bathymetric difference maps, also ensuring consistency. The proposed methodology incorporates the use of confidence intervals, based on the estimated uncertainties. The groundwork for interpretation relies on: (i) qualitative display using multivariate choropleth, (ii) quantitative assessment with the calculation of volumes of raw changes in cubic metres (m³), along with confidence intervals (±m³) and (iii) volumetric histograms accompanied with error bars.

Publications 1 - 10 of 66

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