[HTML][HTML] 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 …
Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques
IMK Ho, KY Cheong, A Weldon - Plos one, 2021 - journals.plos.org
Despite the wide adoption of emergency remote learning (ERL) in higher education during
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …
[HTML][HTML] Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
Accurate and reliable forecasting models for electricity demand (G) are critical in
engineering applications. They assist renewable and conventional energy engineers …
engineering applications. They assist renewable and conventional energy engineers …
Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States
Streamflow estimation plays a significant role in water resources management, especially for
flood mitigation, drought warning, and reservoir operation. Hence, the current study …
flood mitigation, drought warning, and reservoir operation. Hence, the current study …
Simulation and forecasting of streamflows using machine learning models coupled with base flow separation
Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …
to the high number of interrelated hydrological processes. It is well-known that machine …
[HTML][HTML] A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset
RC Deo, X Wen, F Qi - Applied Energy, 2016 - Elsevier
A solar radiation forecasting model can be utilized is a scientific contrivance for investigating
future viability of solar energy potentials. In this paper, a wavelet-coupled support vector …
future viability of solar energy potentials. In this paper, a wavelet-coupled support vector …
Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information
T Yang, AA Asanjan, E Welles, X Gao… - Water Resources …, 2017 - Wiley Online Library
Reservoirs are fundamental human‐built infrastructures that collect, store, and deliver fresh
surface water in a timely manner for many purposes. Efficient reservoir operation requires …
surface water in a timely manner for many purposes. Efficient reservoir operation requires …
Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network …
J Adamowski, H Fung Chan, SO Prasher… - Water resources …, 2012 - Wiley Online Library
Daily water demand forecasts are an important component of cost‐effective and sustainable
management and optimization of urban water supply systems. In this study, a method based …
management and optimization of urban water supply systems. In this study, a method based …
Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting
PS Yu, TC Yang, SY Chen, CM Kuo, HW Tseng - Journal of hydrology, 2017 - Elsevier
This study aims to compare two machine learning techniques, random forests (RF) and
support vector machine (SVM), for real-time radar-derived rainfall forecasting. The real-time …
support vector machine (SVM), for real-time radar-derived rainfall forecasting. The real-time …
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model
A drought forecasting model is a practical tool for drought-risk management. Drought models
are used to forecast drought indices (DIs) that quantify drought by its onset, termination, and …
are used to forecast drought indices (DIs) that quantify drought by its onset, termination, and …