no code implementations • 9 Nov 2021 • Hamed Farahmand, Yuanchang Xu, Ali Mostafavi
We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting.
no code implementations • 30 Aug 2021 • Faxi Yuan, William Mobley, Hamed Farahmand, Yuanchang Xu, Russell Blessing, Shangjia Dong, Ali Mostafavi, Samuel D. Brody
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models.
no code implementations • 15 Jun 2020 • Shangjia Dong, Tianbo Yu, Hamed Farahmand, Ali Mostafavi
The objective of this study is to create and test a hybrid deep learning model, FastGRNN-FCN (Fast, Accurate, Stable and Tiny Gated Recurrent Neural Network-Fully Convolutional Network), for urban flood prediction and situation awareness using channel network sensors data.