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 and new frontiers in riverine and coastal flood modeling

K Jafarzadegan, H Moradkhani… - Reviews of …, 2023 - Wiley Online Library
Over the past decades, the scientific community has made significant efforts to simulate
flooding conditions using a variety of complex physically based models. Despite all …

Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as …

RM Adnan, Z Liang, S Heddam… - Journal of …, 2020 - Elsevier
Monthly streamflow prediction is very important for many hydrological applications in
providing information for optimal use of water resources. In this study, the prediction …

Water quality evaluation and prediction using irrigation indices, artificial neural networks, and partial least square regression models for the Nile River, Egypt

M Gad, AH Saleh, H Hussein, S Elsayed, M Farouk - Water, 2023 - mdpi.com
Water quality is identically important as quantity in terms of meeting basic human needs.
Therefore, evaluating the surface-water quality and the associated hydrochemical …

A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data

S Yang, D Yang, J Chen, J Santisirisomboon, W Lu… - Journal of …, 2020 - Elsevier
Physically distributed hydrological models are effective in hydrological simulations of large
river basins, but the complex characteristics of hydrological features limit their application …

Ensemble learning using multivariate variational mode decomposition based on the transformer for multi-step-ahead streamflow forecasting

J Fang, L Yang, X Wen, H Yu, W Li, JF Adamowski… - Journal of …, 2024 - Elsevier
Accurate streamflow forecasting is critical in the domain of water resources management.
However, the inherently non-stationary and stochastic nature of streamflow poses a …

[HTML][HTML] An integrated statistical-machine learning approach for runoff prediction

AK Singh, P Kumar, R Ali, N Al-Ansari… - Sustainability, 2022 - mdpi.com
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …

Fastest‐growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

W Qiao, Z Li, W Liu, E Liu - International Journal of Energy …, 2022 - Wiley Online Library
Electricity is an important indicator for economic development, especially electricity
production (EP), which is electricity industry managers making strategic decisions. There are …

An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical …

W Wang, Q Cheng, K Chau, H Hu, H Zang, D Xu - Journal of Hydrology, 2023 - Elsevier
Reliable runoff prediction plays a significant role in reservoir scheduling, water resources
management, and efficient utilization of water resources. To effectively enhance the …

Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States

P Parisouj, H Mohebzadeh, T Lee - Water Resources Management, 2020 - Springer
Streamflow estimation plays a significant role in water resources management, especially for
flood mitigation, drought warning, and reservoir operation. Hence, the current study …