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An overview on twin support vector regression
H Huang, X Wei, Y Zhou - Neurocomputing, 2022 - Elsevier
Twin support vector regression (TSVR) is a useful extension of traditional support vector
regression (SVR). As a new regression model, the basic idea of TSVR is generating a pair of …
regression (SVR). As a new regression model, the basic idea of TSVR is generating a pair of …
Artificial intelligence for suspended sediment load prediction: a review
The estimation of sediment yield concentration is crucial for the development of stream
ventures, watershed management, toxins estimation, soil disintegration, floods, and so on. In …
ventures, watershed management, toxins estimation, soil disintegration, floods, and so on. In …
Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation
Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many
regions due to a variety of factors. Groundwater resources (GWR) play a crucial role in daily …
regions due to a variety of factors. Groundwater resources (GWR) play a crucial role in daily …
Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks
Researchers around the world are applying various prediction models for COVID-19 to
make informed decisions and impose appropriate control measures. Because of a high …
make informed decisions and impose appropriate control measures. Because of a high …
Density-weighted support vector machines for binary class imbalance learning
In real-world binary classification problems, the entirety of samples belonging to each class
varies. These types of problems where the majority class is notably bigger than the minority …
varies. These types of problems where the majority class is notably bigger than the minority …
Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform
Structural damage detection is of very importance to improve reliability and safety of civil
structures. A novel sensor data-driven structural damage detection method is proposed in …
structures. A novel sensor data-driven structural damage detection method is proposed in …
Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study
The impact of the suspended sediment load (SSL) on environmental health, agricultural
operations, and water resources planning, is significant. The deposit of SSL restricts the …
operations, and water resources planning, is significant. The deposit of SSL restricts the …
Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
High loads of suspended sediments in rivers are known to cause detrimental effects to
potable water sources, river water quality, irrigation activities, and dam or reservoir …
potable water sources, river water quality, irrigation activities, and dam or reservoir …
[HTML][HTML] Quantitative forecasting of bed sediment load in river engineering: an investigation into machine learning methodologies for complex phenomena
The intricate calculation of bed sediment load (BSL), which is influenced by hydraulic,
hydrological, and sedimentary factors, is vital for informed decision-making in water …
hydrological, and sedimentary factors, is vital for informed decision-making in water …