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Hybridized gated recurrent unit with variational mode decomposition and an error compensation mechanism for multi-step-ahead monthly rainfall forecasting
Highly accurate monthly rainfall predictions can provide early warnings for rain-related disasters, such as floods and droughts, and allow...
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Carbon price prediction model based on adaptive variational mode decomposition and optimized extreme learning machine
The open carbon trading market is an important means to reduce carbon emissions, develop a low-carbon economy, and promote environmental protection....
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Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model
Landslide displacement prediction is essential to establish the early warning system (EWS). According to the dynamic characteristics of landslide...
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Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling
Machines learning models have recently been proposed for predicting rivers water temperature ( T w ) using only air temperature ( T a ). The proposed...
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Monthly runoff prediction based on variational modal decomposition combined with the dung beetle optimization algorithm for gated recurrent unit model
Highly accurate monthly runoff forecasts play a pivotal role in water resource management and utilization. This article proposes a coupling of...
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Improving the accuracy of air relative humidity prediction using hybrid machine learning based on empirical mode decomposition: a comparative study
This paper proposes a hybrid air relative humidity prediction based on preprocessing signal decomposition. New modelling strategy was introduced...
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Enhanced Prediction of Dissolved Oxygen Concentration using a Hybrid Deep Learning Approach with Sinusoidal Geometric Mode Decomposition
Dissolved Oxygen (DO) is a crucial indicator in water bodies, enabling assessment of eutrophication degree, ecosystem status, self-purification...
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Carbon price prediction based on multiple decomposition and XGBoost algorithm
Carbon trading is an effective way to limit global carbon dioxide emissions. The carbon pricing mechanisms play an essential role in the decision of...
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A novel hybrid wind speed interval prediction model based on mode decomposition and gated recursive neural network
Wind energy has become one of the most efficient renewable energy sources. However, the wind has the characteristics of intermittence and...
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A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit
Dissolved oxygen is one of the important comprehensive indicators of river water quality, which reflects the degree of pollution in the water body....
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A hybrid Daily PM2.5 concentration prediction model based on secondary decomposition algorithm, mode recombination technique and deep learning
Accurate and effective PM 2.5 concentration prediction has important implications for public health and the ecological environment. To provide more...
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Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows
Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models...
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A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction
Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently...
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A hybrid multi-scale fusion paradigm for AQI prediction based on the secondary decomposition
With rapid industrialization and urbanization, air pollution has become an increasingly severe problem. As a key indicator of air quality, accurate...
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Air pollutant concentration prediction based on a new hybrid model, feature selection, and secondary decomposition
The concentration of air pollutants is closely related to people’s production and life. Air quality prediction is the premise for environmental...
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Forecasting China carbon price using an error-corrected secondary decomposition hybrid model integrated fuzzy dispersion entropy and deep learning paradigm
Forecasting China’s carbon price accurately can encourage investors and manufacturing industries to take quantitative investments and emission...
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A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China
Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the...
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Short-term prediction of urban PM2.5 based on a hybrid modified variational mode decomposition and support vector regression model
PM 2.5 (particulate matter with a size/diameter ≤ 2.5 μm) is an important air pollutant that affects human health, especially in urban environments....
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A novel hybrid prediction model for PM2.5 concentration based on decomposition ensemble and error correction
PM 2.5 concentration is an important index to measure the degree of air pollution. It is necessary to establish an accurate PM 2.5 concentration...
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Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy
Accurate prediction of carbon emissions is vital to achieving carbon neutrality, which is one of the major goals of the global effort to protect the...
