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 …
Application of machine learning in water resources management: A systematic literature review
In accordance with the rapid proliferation of machine learning (ML) and data management,
ML applications have evolved to encompass all engineering disciplines. Owing to the …
ML applications have evolved to encompass all engineering disciplines. Owing to the …
The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
Precise streamflow prediction is necessary for better planning and managing available
water and future water resources, especially for high altitude mountainous glacier melting …
water and future water resources, especially for high altitude mountainous glacier melting …
Development of new machine learning model for streamflow prediction: Case studies in Pakistan
For accurate estimation of streamflow of a mountainous river basin, a novel hybrid method is
developed in this study, where gradient-based optimization (GBO) algorithm is employed to …
developed in this study, where gradient-based optimization (GBO) algorithm is employed to …
Improving long-term streamflow prediction in a poorly gauged basin using geo-spatiotemporal mesoscale data and attention-based deep learning: A comparative …
Precise long-term streamflow prediction has always been important in the hydrology field,
and has provided essential information for efficient water-resource management and …
and has provided essential information for efficient water-resource management and …
Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH
H Khodakhah, P Aghelpour, Z Hamedi - Environmental Science and …, 2022 - Springer
River flow variations directly affect the hydro-climatological, environmental, and ecological
characteristics of a region. Therefore, an accurate prediction of river flow can critically be …
characteristics of a region. Therefore, an accurate prediction of river flow can critically be …
Short-term probabilistic prediction of significant wave height using bayesian model averaging: Case study of chabahar port, Iran
Accurate predictions of significant wave heights are important for a number of maritime
applications, such as design of coastal and offshore structures. In the present study, an …
applications, such as design of coastal and offshore structures. In the present study, an …
Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling
Carbonate petroleum reservoirs typically have lower permeabilities and recovery factors
than sandstone reservoirs, so the natural fractures they often incorporate have positive …
than sandstone reservoirs, so the natural fractures they often incorporate have positive …
A long-term multivariate time series forecasting network combining series decomposition and convolutional neural networks
X Wang, H Liu, J Du, X Dong, Z Yang - Applied Soft Computing, 2023 - Elsevier
In multivariate time series forecasting tasks, expanding forecast length and improving
forecast efficiency is an urgent need for practical applications. Accurate long-term …
forecast efficiency is an urgent need for practical applications. Accurate long-term …
Covariance matrix adaptation evolution strategy for improving machine learning approaches in streamflow prediction
Precise streamflow estimation plays a key role in optimal water resource use, reservoirs
operations, and designing and planning future hydropower projects. Machine learning …
operations, and designing and planning future hydropower projects. Machine learning …