Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks

• Published in: Water resources management Vol. 33; no. 11; pp. 3759 - 3780
• Main Authors: Wang, Jhih-Huang, Lin, Gwo-Fong, Chang, Ming-Jui, Huang, I-Hang, Chen, Yu-Ren
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2019; Springer Nature B.V
• Subjects: Artificial neural networks; Atmospheric Sciences; Civil Engineering; data collection; Disaster management; Earth and Environmental Science; Earth Sciences; Emergency preparedness; Emergency response; Emergency warning programs; Environment; Flood damage; Flood forecasting; Forecasting; Forecasting techniques; Geotechnical Engineering & Applied Earth Sciences; Hurricanes; Hydrogeology; Hydrology/Water Resources; Lead time; Mathematical models; Multilayer perceptrons; Neural networks; River basins; Rivers; Support vector machines; Taiwan; Training; Typhoons; Warning systems; Water levels; watersheds; Taiwan; Artificial neural network; Support vector machine; Flood-warning system; Water-level forecasting; Dilated causal convolutional neural network

Accurate forecasts of hourly water levels during typhoons are crucial to disaster emergency response. To mitigate flood damage, the development of a water-level forecasting model has played an essential role. We propose a model based on a dilated causal convolutional neural network (DCCNN) that can...