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 …

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research …

H Tao, SI Abba, AM Al-Areeq, F Tangang… - … Applications of Artificial …, 2024 - Elsevier
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 …

Recent advances in Grey Wolf Optimizer, its versions and applications

SN Makhadmeh, MA Al-Betar, IA Doush… - IEEE …, 2023 - ieeexplore.ieee.org
The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm
intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's …

[HTML][HTML] Short-term streamflow forecasting using hybrid deep learning model based on grey wolf algorithm for hydrological time series

HC Kilinc, A Yurtsever - Sustainability, 2022 - mdpi.com
The effects of developing technology and rapid population growth on the environment have
been expanding gradually. Particularly, the growth in water consumption has revealed the …

Scale effects of the monthly streamflow prediction using a state-of-the-art deep learning model

W Xu, J Chen, XJ Zhang - Water Resources Management, 2022 - Springer
The accurate prediction of monthly streamflow is important in sustainable water resources
planning and management. There is a growing interest in the development of deep learning …

[HTML][HTML] Comprehensive analysis for long-term hydrological simulation by deep learning techniques and remote sensing

C Xu, Y Wang, H Fu, J Yang - Frontiers in earth science, 2022 - frontiersin.org
Hydrological simulation plays a very important role in understanding the hydrological
processes and is of great significance to flood forecasting and optimal allocation of water …

Review of recent trends in the hybridisation of preprocessing-based and parameter optimisation-based hybrid models to forecast univariate streamflow

BA Kareem, SL Zubaidi, N Al-Ansari… - … -Computer Modeling in …, 2024 - diva-portal.org
Forecasting river flow is crucial for optimal planning, management, and sustainability using
freshwater resources. Many machine learning (ML) approaches have been enhanced to …

Deformation forecasting of pulp-masonry arch dams via a hybrid model based on CEEMDAN considering the lag of influencing factors

C Lin, X Wang, Y Su, T Zhang, C Lin - Journal of Structural …, 2022 - ascelibrary.org
Deformations in dam structures can have a critical impact on dam safety and life. Accurate
methods for dam deformation prediction and safety evaluation are thus highly needed. Dam …

Monthly streamflow prediction using hybrid extreme learning machine optimized by bat algorithm: a case study of Cheliff watershed, Algeria

S Difi, Y Elmeddahi, A Hebal, VP Singh… - Hydrological …, 2023 - Taylor & Francis
In the present paper, we propose a new approach for monthly streamflow prediction based
on the extreme learning machine (ELM) and the metaheuristic bat algorithm (Bat-ELM). The …

Stochastic (S [ARIMA]), shallow (NARnet, NAR-GMDH, OS-ELM), and deep learning (LSTM, Stacked-LSTM, CNN-GRU) models, application to river flow forecasting

M Kheimi, M Almadani, M Zounemat-Kermani - Acta Geophysica, 2024 - Springer
Forecasting river flow is an important stage in reservoir operation, urban water management,
and water resource optimization. The goal of this research is to forecast daily river flows for …