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Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Data
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models.
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data
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.
