A review of hybrid deep learning applications for streamflow forecasting
Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …
applications have garnered significant interest in the hydrological community. Despite the …
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research …
River flow (Q flow) is a hydrological process that considerably impacts the management and
sustainability of water resources. The literature has shown great potential for nature-inspired …
sustainability of water resources. The literature has shown great potential for nature-inspired …
Interpretable spatio-temporal attention LSTM model for flood forecasting
Modeling interpretable artificial intelligence (AI) for flood forecasting represents a serious
challenge: both accuracy and interpretability are indispensable. Because of the uncertainty …
challenge: both accuracy and interpretability are indispensable. Because of the uncertainty …
Runoff forecasting using convolutional neural networks and optimized bi-directional long short-term memory
J Wu, Z Wang, Y Hu, S Tao, J Dong - Water Resources Management, 2023 - Springer
Water resources matters considerably in maintaining the biological survival and sustainable
socio-economic development of a region. Affected by a combination of factors such as …
socio-economic development of a region. Affected by a combination of factors such as …
Prediction and analysis of train arrival delay based on XGBoost and Bayesian optimization
R Shi, X Xu, J Li, Y Li - Applied Soft Computing, 2021 - Elsevier
Accurate train arrival delay prediction is critical for real-time train dispatching and for the
improvement of the transportation service. This study proposes a data-driven method that …
improvement of the transportation service. This study proposes a data-driven method that …
Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month …
Everywhere around the globe, the hot topic of discussion today is the ongoing and fast-
spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory …
spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory …
Hybridization of hybrid structures for time series forecasting: A review
Achieving the desired accuracy in time series forecasting has become a binding domain,
and develo** a forecasting framework with a high degree of accuracy is one of the most …
and develo** a forecasting framework with a high degree of accuracy is one of the most …
Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis
The frequency and severity of floods have noticeably increased worldwide in the last
decades due to climate change and urbanization. This study aims to build an urban flood …
decades due to climate change and urbanization. This study aims to build an urban flood …
Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting
B Du, Q Zhou, J Guo, S Guo, L Wang - Expert Systems with Applications, 2021 - Elsevier
A reliable and accurate urban water demand forecasting plays a significant role in building
intelligent water supplying system and smart city. Due to the high frequency noise and …
intelligent water supplying system and smart city. Due to the high frequency noise and …
Daily streamflow forecasting in Sobradinho Reservoir using machine learning models coupled with wavelet transform and bootstrap**
Improving forecasting techniques for streamflow time series is of extreme importance for
water resource planning. Among the available techniques, those based on machine …
water resource planning. Among the available techniques, those based on machine …