Ensemble machine learning paradigms in hydrology: A review
Recently, there has been a notable tendency towards employing ensemble learning
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
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 …
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 …
allocation and reservoir scheduling operations. However, due to its complex characteristics …
Daily streamflow forecasting in Sobradinho Reservoir using machine learning models coupled with wavelet transform and bootstrapping
SV Saraiva, F de Oliveira Carvalho, CAG Santos… - Applied Soft …, 2021 - Elsevier
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 …
Application of machine learning and process-based models for rainfall-runoff simulation in Dupage River basin, Illinois
Rainfall-runoff simulation is vital for planning and controlling flood control events. Hydrology
modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC …
modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC …
[HTML][HTML] Comparative study of machine learning methods and GR2M model for monthly runoff prediction
Monthly runoff time-series estimation is imperative information for water resources planning
and development projects. This article aims to comparatively investigate the applicability of …
and development projects. This article aims to comparatively investigate the applicability of …
Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques
The development of the stage-discharge relationship is a fundamental issue in hydrological
modeling. Due to the complexity of the stage-discharge relationship, discharge prediction …
modeling. Due to the complexity of the stage-discharge relationship, discharge prediction …
Modeling multistep ahead dissolved oxygen concentration using improved support vector machines by a hybrid metaheuristic algorithm
Dissolved oxygen (DO) concentration is an important water-quality parameter, and its
estimation is very important for aquatic ecosystems, drinking water resources, and agro …
estimation is very important for aquatic ecosystems, drinking water resources, and agro …
Novel ensemble forecasting of streamflow using locally weighted learning algorithm
The development of advanced computational models for improving the accuracy of
streamflow forecasting could save time and cost for sustainable water resource …
streamflow forecasting could save time and cost for sustainable water resource …
A review of deep learning and machine learning techniques for hydrological inflow forecasting
SD Latif, AN Ahmed - Environment, Development and Sustainability, 2023 - Springer
Conventional machine learning models have been widely used for reservoir inflow and
rainfall prediction. Nowadays, researchers focus on a new computing architecture in the …
rainfall prediction. Nowadays, researchers focus on a new computing architecture in the …