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pro vyhledávání: '"Eicker SO"'
Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences the measurements at other stations. However, the dynamic and implicit
Externí odkaz:
http://arxiv.org/abs/2409.15213
The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. However, the task is difficult due to the dynamic nature and limited data of water systems. Highly
Externí odkaz:
http://arxiv.org/abs/2312.05961
Publikováno v:
npj Climate and Atmospheric Science, Vol 7, Iss 1, Pp 1-12 (2024)
Abstract We evaluate trends in terrestrial water storage over 1950–2100 in CMIP6 climate models against a new global reanalysis from assimilating GRACE and GRACE-FO satellite observations into a hydrological model. To account for different timescal
Externí odkaz:
https://doaj.org/article/c61b31465a4c490aa507b3e6679ac544
Autor:
Naghmeh Shafiee Roudbari, Shubham Rajeev Punekar, Zachary Patterson, Ursula Eicker, Charalambos Poullis
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Forecasting floods encompasses significant complexity due to the nonlinear nature of hydrological systems, which involve intricate interactions among precipitation, landscapes, river systems, and hydrological networks. Recent efforts in hydr
Externí odkaz:
https://doaj.org/article/6ec87701f8174705a14ebdc8d037c8f0
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-4-W10-2024, Pp 147-154 (2024)
This paper presents a concept and first glimpse at the development of an urban digital twin framework to estimate and forecast the carbon footprints of urban neighbourhoods, with a focus on household consumption choices, specifically in buildings, fo
Externí odkaz:
https://doaj.org/article/83acc819dd4e4c2c842b7f2d58fa265f
In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tasks. This is particularly the case for traffic forecasting, where GNN m
Externí odkaz:
http://arxiv.org/abs/2209.03858
Publikováno v:
In Energy & Buildings 1 September 2024 318
Publikováno v:
In Applied Energy 1 August 2024 367
Autor:
A. Bishnoi, O. Stein, C. I. Meyer, R. Redler, N. Eicker, H. Haak, L. Hoffmann, D. Klocke, L. Kornblueh, E. Suarez
Publikováno v:
Geoscientific Model Development, Vol 17, Pp 261-273 (2024)
The confrontation of complex Earth system model (ESM) codes with novel supercomputing architectures poses challenges to efficient modeling and job submission strategies. The modular setup of these models naturally fits a modular supercomputing archit
Externí odkaz:
https://doaj.org/article/38c3916033914f39a3f27d46b639c81f
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 97-105 (2023)
This paper presents an investigation into the interoperability of 3D building energy data management, delivery, processing, and visualization via web clients using Open Geospatial Consortium – Application Programming Interface (OGC API) standard-ba
Externí odkaz:
https://doaj.org/article/3aa5f72636a44408a4a8dae2c5168a93