A comprehensive review of deep learning applications in hydrology and water resources
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 …
variety and velocity of water-related data are increasing due to large-scale sensor networks …
[HTML][HTML] Long short-term memory models of water quality in inland water environments
Water quality is substantially influenced by a multitude of dynamic and interrelated variables,
including climate conditions, landuse and seasonal changes. Deep learning models have …
including climate conditions, landuse and seasonal changes. Deep learning models have …
A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning
Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still
room for improvement, researchers have been developing physical and machine learning …
room for improvement, researchers have been developing physical and machine learning …
Distributed long-term hourly streamflow predictions using deep learning–A case study for State of Iowa
Accurate streamflow forecasting has always been a challenge. Although there were many
novel approaches using deep learning models, accuracy of these models is often limited to …
novel approaches using deep learning models, accuracy of these models is often limited to …
Comprehensive flood vulnerability analysis in urban communities: Iowa case study
Y Alabbad, I Demir - International journal of disaster risk reduction, 2022 - Elsevier
Floods are one of the most prevalent types of natural disasters affecting communities
worldwide. It is expected to persist with increasing magnitude and frequency as a result of …
worldwide. It is expected to persist with increasing magnitude and frequency as a result of …
D-SRGAN: DEM super-resolution with generative adversarial networks
Digital elevation model (DEM) is a critical data source for variety of applications such as
road extraction, hydrological modeling, flood mapping, and many geospatial studies. The …
road extraction, hydrological modeling, flood mapping, and many geospatial studies. The …
An integrated flood risk assessment and mitigation framework: A case study for middle Cedar River Basin, Iowa, US
E Yildirim, I Demir - International Journal of Disaster Risk Reduction, 2021 - Elsevier
Property buyout is one of the most frequently preferred flood mitigation applications by
decision-makers for long-term risk reduction. Due to its high-level funding requirements as a …
decision-makers for long-term risk reduction. Due to its high-level funding requirements as a …
Flood mitigation data analytics and decision support framework: Iowa Middle Cedar Watershed case study
Y Alabbad, E Yildirim, I Demir - Science of The Total Environment, 2022 - Elsevier
Flooding is one of the most frequent natural disasters, causing billions of dollars in damage
and threatening vulnerable communities worldwide. Although the impact of flooding can …
and threatening vulnerable communities worldwide. Although the impact of flooding can …
Virtual and augmented reality applications for environmental science education and training
Recent advancements in positioning and depth sensing using sensor technologies, graphic
processor units and artificial intelligence led to the development of generalized and …
processor units and artificial intelligence led to the development of generalized and …
An ethical decision-making framework with serious gaming: a smart water case study on flooding
Sensors and control technologies are being deployed at unprecedented levels in both urban
and rural water environments. Because sensor networks and control allow for higher …
and rural water environments. Because sensor networks and control allow for higher …