Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Sit, Muhammed"'
Rainfall data collected by various remote sensing instruments such as radars or satellites has different space-time resolutions. This study aims to improve the temporal resolution of radar rainfall products to help with more accurate climate change m
Externí odkaz:
http://arxiv.org/abs/2303.05552
Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets. Due to limitations in technical capabilities or resources, the acquisition of high-quality data for many environmental disciplines is costly.
Externí odkaz:
http://arxiv.org/abs/2109.09661
The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar,
Externí odkaz:
http://arxiv.org/abs/2109.09289
Effective environmental planning and management to address climate change could be achieved through extensive environmental modeling with machine learning and conventional physical models. In order to develop and improve these models, practitioners a
Externí odkaz:
http://arxiv.org/abs/2107.03432
The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper pr
Externí odkaz:
http://arxiv.org/abs/2107.07039
Publikováno v:
In Environmental Modelling and Software May 2024 176
Autor:
Sit, Muhammed, Demiray, Bekir Z., Xiang, Zhongrun, Ewing, Gregory J., Sermet, Yusuf, Demir, Ibrahim
The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, wate
Externí odkaz:
http://arxiv.org/abs/2007.12269
LIDAR (light detection and ranging) is an optical remote-sensing technique that measures the distance between sensor and object, and the reflected energy from the object. Over the years, LIDAR data has been used as the primary source of Digital Eleva
Externí odkaz:
http://arxiv.org/abs/2004.04788
In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images th
Externí odkaz:
http://arxiv.org/abs/2003.00826
Publikováno v:
In ISPRS Journal of Photogrammetry and Remote Sensing November 2023 205:176-190