Time series-based groundwater level forecasting using gated recurrent unit deep neural networks
H Lin, A Gharehbaghi, Q Zhang, SS Band… - Engineering …, 2022 - Taylor & Francis
In this research, the mean monthly groundwater level with a range of 3.78 m in Qoşaçay
plain, Iran, is forecast. Regarding three different layers of gated recurrent unit (GRU) …
plain, Iran, is forecast. Regarding three different layers of gated recurrent unit (GRU) …
Nonlinear unsteady bridge aerodynamics: Reduced-order modeling based on deep LSTM networks
T Li, T Wu, Z Liu - Journal of Wind Engineering and Industrial …, 2020 - Elsevier
Rapid increase in the bridge spans and the attendant innovative bridge deck cross-sections
have placed significant importance on effectively modeling of the nonlinear, unsteady bridge …
have placed significant importance on effectively modeling of the nonlinear, unsteady bridge …
[HTML][HTML] Time-varying surface deformation retrieval and prediction in closed mines through integration of SBAS InSAR measurements and LSTM algorithm
B Chen, H Yu, X Zhang, Z Li, J Kang, Y Yu, J Yang… - Remote Sensing, 2022 - mdpi.com
After a coal mine is closed, the coal rock mass could undergo weathering deterioration and
strength reduction due to factors such as stress and groundwater, which in turn changes the …
strength reduction due to factors such as stress and groundwater, which in turn changes the …
A comparative study on effect of news sentiment on stock price prediction with deep learning architecture
The accelerated progress in artificial intelligence encourages sophisticated deep learning
methods in predicting stock prices. In the meantime, easy accessibility of the stock market in …
methods in predicting stock prices. In the meantime, easy accessibility of the stock market in …
The utility of information flow in formulating discharge forecast models: A case study from an arid snow‐dominated catchment
Streamflow forecasts often perform poorly because of improper representation of hydrologic
response timescales in underlying models. Here, we use transfer entropy (TE), which …
response timescales in underlying models. Here, we use transfer entropy (TE), which …
Modeling nonlinear flutter behavior of long‐span bridges using knowledge‐enhanced long short‐term memory network
T Li, T Wu - Computer‐Aided Civil and Infrastructure …, 2023 - Wiley Online Library
The nonlinear characteristics of bridge aerodynamics preclude a closed‐form solution of
limit‐cycle oscillation (LCO) amplitude and frequency in the post‐flutter stage. To address …
limit‐cycle oscillation (LCO) amplitude and frequency in the post‐flutter stage. To address …
Practical end-to-end optical music recognition for pianoform music
The majority of recent progress in Optical Music Recognition (OMR) has been achieved with
Deep Learning methods, especially models following the end-to-end paradigm that read …
Deep Learning methods, especially models following the end-to-end paradigm that read …
Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach
D Dutta, SK Pal - Environmental Monitoring and Assessment, 2023 - Springer
The present study focuses on the prediction and assessment of the impact of lockdown
because of coronavirus pandemic on the air quality during three different phases, viz …
because of coronavirus pandemic on the air quality during three different phases, viz …
A data‐driven approach for flood prediction using grid‐based meteorological data
Y Wang, J Liu, C Li, Y Liu, L Xu, F Yu - Hydrological Processes, 2023 - Wiley Online Library
Establishing a physically‐based hydrological model for flood prediction in ungauged or data‐
limited catchments has always been a difficult problem. In this study, a data‐driven approach …
limited catchments has always been a difficult problem. In this study, a data‐driven approach …
[HTML][HTML] Streamflow simulation with high-resolution WRF input variables based on the CNN-LSTM hybrid model and gamma test
Y Wang, J Liu, L Xu, F Yu, S Zhang - Water, 2023 - mdpi.com
Streamflow modelling is one of the most important elements for the management of water
resources and flood control in the context of future climate change. With the advancement of …
resources and flood control in the context of future climate change. With the advancement of …