Search Results for author: Muhammed Sit

Found 6 papers, 1 papers with code

Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks

no code implementations7 Jul 2021 Muhammed Sit, Bekir Demiray, Ibrahim Demir

The frequency and impact of floods are expected to increase due to climate change.

IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation

1 code implementation7 Jul 2021 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 models.

A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

no code implementations17 Jun 2020 Muhammed Sit, Bekir Z. Demiray, Zhongrun Xiang, Gregory J. Ewing, Yusuf Sermet, Ibrahim Demir

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.

Decision Making Disaster Response

D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

no code implementations9 Apr 2020 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.

Image Super-Resolution

Realistic River Image Synthesis using Deep Generative Adversarial Networks

no code implementations14 Feb 2020 Akshat Gautam, Muhammed Sit, Ibrahim Demir

In this paper, we demonstrated a practical application of realistic river image generation using deep learning.

Image Generation

Decentralized Flood Forecasting Using Deep Neural Networks

no code implementations6 Feb 2019 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.

Time Series Time Series Forecasting

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