Over the past two decades, the Bootstrap AGGregatING (bagging) method has been widely used for improving simulation. The computational cost of this method scales with the size of the ensemble, but excessively reducing the ensemble size comes at the c...
Surrogate modeling techniques are widely used to replace the computationally expensive black-box functions in engineering. As a combination of individual surrogate models, an ensemble of surrogates is preferred due to its strong robustness. However,...
As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and c...
In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the...
Surrogate models are often used as alternatives to considerably reduce the computational burden of the expensive computer simulations that are required for engineering designs. The development of surrogate models for complex relationships between the...
Accurately predicting the carbon price sequence is important and necessary for promoting the development of China’s national carbon trading market. In this paper, a multiscale ensemble forecasting model that is based on ensemble empirical mode...
In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component...
With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial market operati...
Natural gas, a key low-emission energy source with significant strategic value in modern energy systems, necessitates accurate forecasting of its market price to ensure effective policy planning and economic stability. This paper proposes an ensemble...
Speech emotion recognition is a challenging and widely examined research topic in the field of speech processing. The accuracy of existing models in speech emotion recognition tasks is not high, and the generalization ability is not strong. Since the...
Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global...
22 May 2017
Although fine particulate matter with a diameter of <2.5 μm (PM2.5) has a greater negative impact on human health than particulate matter with a diameter of <10 μm (PM10), measurements of PM2.5 have only recently been performed, and the spatial...
Focusing on emotion recognition, this paper addresses the task of emotion classification and its performance with respect to accuracy, by investigating the capabilities of a distributed ensemble model using precision-based weighted blending. Research...
The assimilation of LAI measurements, repeatedly taken at sub-field level, into dynamic crop simulation models could provide valuable information for precision farming applications. Commonly used updating methods such as the Ensemble Kalman Filter (E...
Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model...
A wave forecast numerical simulation was performed for Typhoon Lingling around the Korean Peninsula and in the East Asia region using sea winds from 24 members produced by the Ensemble Prediction System for Global (EPSG) of Korea Meteorological Admin...
In the evolving field of Brain–Computer Interfaces (BCIs), accurately classifying Electroencephalography (EEG) signals for Motor Imagery (MI) tasks is challenging. We introduce the Correlation-Optimized Weighted Stacking Ensemble (COWSE) model,...
An efficient regional hybrid ensemble-variational (EnVar) data assimilation method using the global-ensemble-model-augmented error covariance is proposed and preliminarily tested in this study. This method uses the global ensemble error covariance as...
Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the exi...
The accuracy of monthly runoff predictions is crucial for decision-making and efficiency in various areas, such as water resources management, flood control and disaster mitigation, hydraulic engineering scheduling, and agricultural irrigation. There...
The multi-model ensemble (MME) approaches are highly regarded in climate prediction and risk assessment for their capacity to integrate multiple global climate models (GCMs) and minimize uncertainties associated with individual models. However, the q...
Given the wide applications of photovoltaic (PV) power generation, the volatility in generation caused by solar radiation, which limits the capacity of the power grid, cannot be ignored. Therefore, much research has aimed to address this issue throug...
The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the fi...
The development of an efficient and accurate hydrological forecasting model is essential for water management and flood control. In this study, the ensemble model was applied to predict the daily discharge; it not only could enhance the algorithm and...
Previous studies on multi-model ensemble forecasting mainly focused on the weight allocation of each model, but did not discuss how to suppress the reduction of ensemble forecasting accuracy when adding poorer models. Based on a variant weight (VW) m...
Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA)...
The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integratio...
The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmolog...
While the use of machine learning (ML) in automated real estate valuation is growing, research on stacking ML models into ensembles remains limited. In this paper, we propose a stacked ensemble model for valuing residential properties. By applying ou...
In the real-life, time-series data comprise a complicated pattern, hence it may be challenging to increase prediction accuracy rates by using machine learning and conventional statistical methods as single learners. This research outlines and investi...
This study investigated the strength and limitations of two widely used multi-model averaging frameworks—Bayesian model averaging (BMA) and reliability ensemble averaging (REA), in post-processing runoff projections derived from coupled hydrological...
In the process of hydrological forecasting, there are uncertainties in data input, model parameters, and model structure, which cause a deterministic forecasting to fail to provide useful risk information to decision-makers. Therefore, the study of e...
Background/Objectives: Cardiovascular diseases (CVDs) remain the primary cause of mortality worldwide, underscoring the critical importance of developing accurate early prediction models. In this study, we propose an advanced stacking ensemble learni...
This study introduces HYTREM, a hybrid tree-based ensemble learning model conceived with the sustainable development of eco-friendly transportation and renewable energy in mind. Designed as a digital model, HYTREM primarily aims to enhance solar powe...
East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial inte...
As global electricity demand increases and concerns over fossil fuel usage intensify, renewable energy sources have gained significant attention. Solar energy stands out due to its low installation costs and suitability for deployment. However, solar...
This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advantage and become key components of IoT devices in at...
Gender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, inte...
With global warming and accelerated urbanization, urban flooding became one of the top ten international natural disasters in 2024. In order to accurately and efficiently simulate the impact of upstream river water transport on downstream river inund...
Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-...
Can bi-encoders, without additional fine-tuning, achieve a performance comparable to fine-tuned BERT models in classification tasks? To answer this question, we present a simple yet effective approach to text classification using bi-encoders without...
Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in eff...
Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special c...
This publication presents the methodological aspects of designing of a scoring model for an early prediction of bankruptcy by using ensemble classifiers. The main goal of the research was to develop a scoring model (with good classification propertie...
Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, an...
Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are...
Background: Age-related macular degeneration (AMD) is a leading cause of visual impairment in the elderly population. Periodic examinations through fundus image analysis are paramount for early diagnosis and adequate treatment. Automatic artificial i...
Over the past two decades, air-quality prediction has moved from a mainly single-model paradigm toward ensemble systems that make explicit use of diversity across models, observations, and data streams. This review connects developments that are ofte...
More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past dec...
Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At...
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