120 citations · 228 across the 14 of their papers we have counts for
14 papers
EfficientTempNet: Temporal Super-Resolution of Radar Rainfall
Bekir Z Demiray, Muhammed Sit, Ibrahim Demir
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…
Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education
Ramteja Sajja, Yusuf Sermet, David Cwiertny +1
Miscommunication and communication challenges between instructors and students represents one of the primary barriers to post-secondary learning. Students often avoid or miss oppor…
The Flood Mitigation Problem in a Road Network
Vahid Eghbal Akhlaghi, Ann Melissa Campbell, Ibrahim Demir
Natural disasters are highly complex and unpredictable. However, long-term planning and preparedness activities can help to mitigate the consequences and reduce the damage. For exa…
High-resolution rainfall-runoff modeling using graph neural network
Zhongrun Xiang, Ibrahim Demir
Time-series modeling has shown great promise in recent studies using the latest deep learning algorithms such as LSTM (Long Short-Term Memory). These studies primarily focused on w…
DEM Super-Resolution with EfficientNetV2
Bekir Z Demiray, Muhammed Sit, Ibrahim Demir
Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets. Due to limitations in technical capabilities or resources, the acquisit…
TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs
Muhammed Sit, Bong-Chul Seo, Ibrahim Demir
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. Rai…
Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks
Muhammed Sit, Bekir Demiray, Ibrahim Demir
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…
IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation
Muhammed Sit, Bong-Chul Seo, Ibrahim Demir
Effective environmental planning and management to address climate change could be achieved through extensive environmental modeling with machine learning and conventional physical…
A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case Study
Yusuf Sermet, Ibrahim Demir
COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with differen…
A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources
Muhammed Sit, Bekir Z. Demiray, Zhongrun Xiang +3
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 netw…
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks
Bekir Z Demiray, Muhammed Sit, Ibrahim Demir
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…
Realistic River Image Synthesis using Deep Generative Adversarial Networks
Akshat Gautam, Muhammed Sit, Ibrahim Demir
In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) mod…
A Generalized Web Component for Domain-Independent Smart Assistants
Yusuf Sermet, Ibrahim Demir
This article introduces an open-source web component, Instant Expert, which allows robust and efficient integration of a natural language question answering system to web-based pla…
Decentralized Flood Forecasting Using Deep Neural Networks
Muhammed Sit, Ibrahim Demir
Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict…