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Found 9 papers, 1 papers with code
EfficientTempNet: Temporal Super-Resolution of Radar Rainfall
Rainfall data collected by various remote sensing instruments such as radars or satellites has different space-time resolutions.
DEM Super-Resolution with EfficientNetV2
Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets.
TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs
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.
IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation
Effective environmental planning and management to address climate change could be achieved through extensive environmental modeling with machine learning and conventional physical models.
Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks
The frequency and impact of floods are expected to increase due to climate change.
A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources
The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges.
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks
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.
Realistic River Image Synthesis using Deep Generative Adversarial Networks
In this paper, we demonstrated a practical application of realistic river image generation using deep learning.
Decentralized Flood Forecasting Using Deep Neural Networks
Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management.
