Hybridization of hybrid structures for time series forecasting: A review
Z Hajirahimi, M Khashei - Artificial Intelligence Review, 2023 - Springer
Achieving the desired accuracy in time series forecasting has become a binding domain,
and developing a forecasting framework with a high degree of accuracy is one of the most …
and developing a forecasting framework with a high degree of accuracy is one of the most …
A review of the application of hybrid machine learning models to improve rainfall prediction
Rainfall is one of the most important meteorological phenomena that impacts many fields,
including agriculture, energy, water resources management, and mining, among others …
including agriculture, energy, water resources management, and mining, among others …
Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach
In this study, a novel SARIMA-SVR model is proposed to forecast statistical indicators in the
aviation industry that can be used for later capacity management and planning purpose …
aviation industry that can be used for later capacity management and planning purpose …
A stacking ensemble learning model for monthly rainfall prediction in the Taihu Basin, China
The prediction of monthly rainfall is greatly beneficial for water resources management and
flood control projects. Machine learning (ML) techniques, as an increasingly popular …
flood control projects. Machine learning (ML) techniques, as an increasingly popular …
A novel multiscale forecasting model for crude oil price time series
R Li, Y Hu, J Heng, X Chen - Technological Forecasting and Social …, 2021 - Elsevier
Forecasting crude oil prices is an essential research field in the international bulk
commodities market. However, price movements present more complex nonlinear behavior …
commodities market. However, price movements present more complex nonlinear behavior …
Utilization of random vector functional link integrated with manta ray foraging optimization for effluent prediction of wastewater treatment plant
An innovative predictive model was employed to predict the key performance indicators of a
full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment …
full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment …
Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS
KS Mohammed, S Shabanlou, A Rajabi… - Applied Water …, 2023 - Springer
Groundwater level fluctuations are one of the main components of the hydrogeological cycle
and one of the required variables for many water resources operation models. The …
and one of the required variables for many water resources operation models. The …
Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and grey wolf optimization
Accurate influent flow forecasting plays a significant role in management, operation,
scheduling and utilization of the sewage treatment plants. In design and operate such …
scheduling and utilization of the sewage treatment plants. In design and operate such …
Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology
Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids
(TDS) and total suspended solids (TSS) are the most commonly regulated wastewater …
(TDS) and total suspended solids (TSS) are the most commonly regulated wastewater …
Lake water-level fluctuations forecasting using minimax probability machine regression, relevance vector machine, Gaussian process regression, and extreme …
Forecasting freshwater lake levels is vital information for water resource management,
including water supply management, shoreline management, hydropower generation …
including water supply management, shoreline management, hydropower generation …