An outlier detection framework for Air Quality Index prediction using linear and ensemble models

Decision Analytics Journal, Год журнала: 2025, Номер unknown, С. 100546 - 100546

Опубликована: Янв. 1, 2025

Язык: Английский

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Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery

Journal of Petroleum Exploration and Production Technology, Год журнала: 2025, Номер 15(3)

Опубликована: Фев. 25, 2025

Язык: Английский

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Pipeline deformation prediction based on multi-source monitoring information and novel data-driven model

Engineering Structures, Год журнала: 2025, Номер 337, С. 120461 - 120461

Опубликована: Май 9, 2025

Язык: Английский

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Hybrid model for air quality prediction based on LSTM with random search and Bayesian optimization techniques

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 12, 2024

Язык: Английский

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Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas

Atmosphere, Год журнала: 2025, Номер 16(3), С. 255 - 255

Опубликована: Фев. 23, 2025

Keeping track of air quality is paramount to issue preemptive measures mitigate adversarial effects on the population. This study introduces a new quantum–classical approach, combining graph-based deep learning structure with quantum neural network predict ozone concentration up 6 h ahead. The proposed architecture utilized historical data from Houston, Texas, major urban area that frequently fails comply regulations. Our results revealed smoother transition between classical framework and its counterpart enhances model’s results. Moreover, we observed min–max normalization increased ansatz repetitions also improved hybrid performance. was evident evaluating assessment metrics root mean square error (RMSE), coefficient determination (R2) forecast skill (FS). Values for R2 FS horizons considered were 94.12% 31.01% 1 h, 83.94% 48.01% 3 75.62% 57.46% forecasts. A comparison existing literature both QML models methodology could provide competitive results, even surpass some well-established forecasting models, proving be valuable resource forecasting, thus validating this approach.

Язык: Английский

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Corrosion Depth Prediction of a Buried Pipeline Based on TEM and MSDBO-BiLSTM

Journal of Pipeline Systems Engineering and Practice, Год журнала: 2025, Номер 16(2)

Опубликована: Март 12, 2025

Язык: Английский

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Monte Carlo simulation framework for assessing heavy metal exposure and adverse health effects in fly-in fly-out workers

Journal of Environmental Management, Год журнала: 2025, Номер 380, С. 125074 - 125074

Опубликована: Март 20, 2025

Язык: Английский

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Comparative Analysis of Data Visualization and Deep Learning Models in Air Quality Forecasting

Sakarya University Journal of Computer and Information Sciences, Год журнала: 2025, Номер 8(1), С. 89 - 111

Опубликована: Март 27, 2025

This study utilizes air pollution data from the Continuous Monitoring Center of Ministry Environment, Urbanization, and Climate Change in Turkey to predict various pollutants using three advanced deep learning approaches: LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), RNN (Recurrent Network). Missing dataset were imputed K-Nearest Neighbor (K-NN) algorithm ensure completeness. Furthermore, a fusion technique was applied integrate multiple pollutant enhancing richness reliability input features for modeling. The increasing issue, driven by factors such as population growth, urbanization, industrial development, is major environmental concern. evaluates these models estimate concentrations selects most accurate, RNN, forecasting over next years. Each prediction assessed performance metrics MAE, RMSE, R² robust model evaluation. Visualization forecast results achieved through methods like Box Plots, Violin Point Scatter Graphs, making quality information more accessible general audiences. In terms performance, an 0.88 PM10 0.93 SO2, while demonstrated 0.94 0.95 SO2. However, emerged accurate model, achieving 0.97 both SO2 forecasts. allows forecasts levels three-year period. findings indicate that predictive modeling, combined with visualization techniques, could significantly contribute mitigating future uncertainties enhance comprehension patterns non-expert

Язык: Английский

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Comprehensive Scale Fusion Networks with High Spatiotemporal Feature Correlation for Air Quality Prediction

Atmosphere, Год журнала: 2025, Номер 16(4), С. 429 - 429

Опубликована: Апрель 8, 2025

The escalation of industrialization has worsened air quality, underscoring the essential need for accurate forecasting to inform policies and protect public health. Current research primarily emphasized individual spatiotemporal features prediction, neglecting interconnections between these features. To address this, we proposed generative Comprehensive Scale Spatiotemporal Fusion Air Quality Predictor (CSST-AQP). novel dual-branch architecture combines multi-scale spatial correlation analysis with adaptive temporal modeling capture complex interactions in pollutant dispersion enhanced pollution forecasting. Initially, a fusion preprocessing module based on localized high-correlation encodes multidimensional quality indicators geospatial data into unified Then, core employs collaborative framework: processing branch extracts at varying granularities, an enhancement concurrently models local periodicities global evolutionary trends. feature engine hierarchically integrates spatiotemporally relevant regional scales while aggregating from related sites. In experimental results across 14 Chinese regions, CSST-AQP achieves state-of-the-art performance compared LSTM-based networks RMSE 6.11–9.13 μg/m3 R2 0.91–0.93, demonstrating highly robust 60 h capabilities diverse pollutants.

Язык: Английский

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Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation

Atmosphere, Год журнала: 2025, Номер 16(5), С. 513 - 513

Опубликована: Апрель 28, 2025

Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of across diverse geographical climatic regions, this study proposes novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Quality Index (AQI) time-series prediction. Through systematic analysis multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover climate zones (subtropical temperate), gradients (coastal inland), topographical variations (plains mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (p < 0.05) revealed non-Gaussian distribution characteristics observational data, providing statistical justification implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation extended beyond conventional single-city approaches, demonstrating model generalizability distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance 23.6–59.6% reduction Root-Mean-Square Error (RMSE) relative baseline LSTM models, along consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed exhibited robust generalization capabilities divisions (R2 = 0.92–0.99), establishing reliable decision-support platform regionally adaptive early-warning systems. This provides valuable insights addressing spatial heterogeneity modeling applications.

Язык: Английский

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