A review of hybrid deep learning applications for streamflow forecasting

KW Ng, YF Huang, CH Koo, KL Chong, A El-Shafie… - Journal of …, 2023 - Elsevier
Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …

Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM

J Guo, Y Liu, Q Zou, L Ye, S Zhu, H Zhang - Journal of Hydrology, 2023 - Elsevier
Accurate prediction of runoff is an important foundation for optimizing water resource
allocation and reservoir scheduling operations. However, due to its complex characteristics …

Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting

M Rahimzad, A Moghaddam Nia, H Zolfonoon… - Water Resources …, 2021 - Springer
Streamflow forecasting plays a key role in improvement of water resource allocation,
management and planning, flood warning and forecasting, and mitigation of flood damages …

Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM-a case study for runoff simulation in an arid area

L Bian, X Qin, C Zhang, P Guo, H Wu - Journal of Hydrology, 2023 - Elsevier
The runoff prediction can provide scientific basis for flood control, disaster reduction and
water resources planning. Due to a large number of uncertainties in runoff prediction, it is …

Ship trajectory planning for collision avoidance using hybrid ARIMA-LSTM models

M Abebe, Y Noh, YJ Kang, C Seo, D Kim, J Seo - Ocean Engineering, 2022 - Elsevier
In maritime transportation, accurate estimation of ship trajectories has a great impact on
collision-free trajectory planning. Previously, many approaches were proposed for ship …

[HTML][HTML] Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations

L **, H Xue, G Dong, Y Han, Z Li, Y Lian - Journal of Hydrology, 2024 - Elsevier
Global climate change has led to an increase in the frequency and scale of extreme weather
events worldwide, and there is an urgent need to develop better-performing hydrological …

Deep learning approach with LSTM for daily streamflow prediction in a semi-arid area: a case study of Oum Er-Rbia river basin, Morocco

K Nifa, A Boudhar, H Ouatiki, H Elyoussfi, B Bargam… - Water, 2023 - mdpi.com
Daily hydrological modelling is among the most challenging tasks in water resource
management, particularly in terms of streamflow prediction in semi-arid areas. Various …

Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models

P Bai, X Liu, J **e - Journal of Hydrology, 2021 - Elsevier
Hydrologic models are commonly used to assess climate change impact on water
resources. Several studies have reported that hydrologic models often experience severe …

Impact of COVID-19 on the US Construction Industry as Revealed in the Purdue Index for Construction

JH Jeon, S Padhye, A Bhattacharyya, H Cai… - … of Management in …, 2022 - ascelibrary.org
Abstract The coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented
impacts (eg, labor shortage, suspension and cancellation of projects, and disrupted supply …

Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin

C Yang, M Xu, S Kang, C Fu, D Hu - Journal of Hydrology, 2023 - Elsevier
Robust streamflow simulation at glacial basins is essential for the improvement of water
sustainability assessment, water security evaluation, and water resource management …