Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Roland Löwe"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Ecosystem services (ES) are essential to sustainable development at multiple spatial scales. Monitoring ES potential (ESP) at the metropolitan level is imperative to sustainable cities. We developed a procedure for long-term monitoring of me
Externí odkaz:
https://doaj.org/article/c9078f065a43408996251280043ae8d3
Autor:
Martina Viti, Jacob Ladenburg, Roland Löwe, Hjalte J.D. Sørup, Ursula S. McKnight, Karsten Arnbjerg-Nielsen
Publikováno v:
Nature-Based Solutions, Vol 6, Iss , Pp 100146- (2024)
Nature-Based Solutions (NBS) are growing in popularity as approaches for, among others, the reduction of hydro-meteorological risks. However, their uptake is still slow despite the recognition of their role in enabling a smarter, more systemic, and f
Externí odkaz:
https://doaj.org/article/d4f8a6c296dd48378ab5ad135497dc67
Publikováno v:
Ecological Indicators, Vol 148, Iss , Pp 110078- (2023)
Current adaptation responses to sea-level rise tend to focus on protecting existing infrastructure resulting in unsustainable adaptation pathways. At the same time, urban development compromises a city’s adaptive capacity if the climate risk compon
Externí odkaz:
https://doaj.org/article/81d09e5a6ec441e4a23e8dc7de3c5893
Publikováno v:
Journal of Hydroinformatics, Vol 23, Iss 6, Pp 1368-1381 (2021)
In this work, an unsupervised model selection procedure for identifying data-driven forecast models for urban drainage systems is proposed and evaluated. Specifically, we consider the case of predicting inflows to wastewater treatment plants for acti
Externí odkaz:
https://doaj.org/article/35c8a2c4025e4166b900eab326a609ec
Autor:
Peter Bauer-Gottwein, Henrik Grosen, Daniel Druce, Christian Tottrup, Heidi E. Johansen, Roland Löwe
Publikováno v:
Water, Vol 14, Iss 22, p 3742 (2022)
Mapping and prediction of inundated areas are increasingly important for climate change adaptation and emergency preparedness. Flood forecasting tools and flood risk models have to be compared to observe flooding patterns for training, calibration, v
Externí odkaz:
https://doaj.org/article/62fd2cc5ecd94a14aa3f63b33e5691c8
Autor:
Jonas W. Pedersen, Nadia S. V. Lund, Morten Borup, Roland Löwe, Troels S. Poulsen, Peter S. Mikkelsen, Morten Grum
Publikováno v:
Water, Vol 8, Iss 9, p 381 (2016)
High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initia
Externí odkaz:
https://doaj.org/article/322c384cf7d141989aca5f9763cbc749
Publikováno v:
Water, Vol 8, Iss 3, p 87 (2016)
This study evaluated methods for automated classification of rain events into groups of “high” and “low” spatial and temporal variability in offline and online situations. The applied classification techniques are fast and based on rainfall d
Externí odkaz:
https://doaj.org/article/ade2c5fe33e24e8d903ae22d0104636d
Dynamic operation of urban water infrastructure has many times been demonstrated as an efficient way to manage storm- and wastewater flows with a minimum of cost and material resources, and to improve the health of surface water environments by reduc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3dc32df01f71df447ad498c1bd8f71a
https://doi.org/10.5194/egusphere-egu23-4178
https://doi.org/10.5194/egusphere-egu23-4178
Human activities have a profound impact on climate and hydrological processes, contributing to changes in the frequency and severity of hydrological extremes and, consequently, growing socioeconomic vulnerability [1]. Rising sea levels, continuous ur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d09695824139a8755300d369cfaaa534
https://doi.org/10.5194/egusphere-egu23-3422
https://doi.org/10.5194/egusphere-egu23-3422
Autor:
Roland Löwe, Matthias Kjær Adamsen, Phillip Aarestrup, Franca Bauer, Allan Peter Engsig-Karup, Morten Grum, Frederik Tinus Jeppesen, Peter Steen Mikkelsen
In this work we illustrate how scientific machine learning algorithms (SciML) can be used to facilitate the development of digital twins for urban drainage systems. Scientific machine learning integrates classical, modelling techniques from scientifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c679d64472ac5a0fad89f03e03305843
https://doi.org/10.5194/egusphere-egu23-5672
https://doi.org/10.5194/egusphere-egu23-5672