Unveiling Drivers of Zone-Specific Air Quality Predictions Using Explainable Ai: Shapley Additive Explanations-Based Insights Across Formal and Informal End-of-Life Vehicle Recycling Zones with a Green Zone Benchmark

Опубликована: Янв. 1, 2025

Язык: Английский

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Predicting particulate matter (PM2.5) air pollution levels in Almaty city using machine learning techniques

Modeling Earth Systems and Environment, Год журнала: 2025, Номер 11(4)

Опубликована: Апрель 28, 2025

Язык: Английский

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Forecasting PM2.5 and Assessing Health Impacts in Texas Using Advanced Deep Learning Models

Earth Systems and Environment, Год журнала: 2025, Номер unknown

Опубликована: Апрель 30, 2025

Язык: Английский

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Application of Deep Learning Techniques for Air Quality Prediction: A Case Study in Macau

Processes, Год журнала: 2025, Номер 13(5), С. 1507 - 1507

Опубликована: Май 14, 2025

To better inform the public about ambient air quality and associated health risks prevent cardiovascular chronic respiratory diseases in Macau, local government authorities apply Air Quality Index (AQI) for management within its jurisdiction. The application of AQI requires first determining sub-indices several pollutants, including respirable suspended particulates (PM10), fine (PM2.5), nitrogen dioxide (NO2), ozone (O3), sulfur (SO2), carbon monoxide (CO). Accurate prediction is crucial providing early warnings to before pollution episodes occur. improve accuracy, deep learning methods such as artificial neural networks (ANNs) long short-term memory (LSTM) models were applied forecast six pollutants commonly found AQI. data this study was accessed from Macau High-Density Residential Monitoring Station (AQMS), which located an area with high traffic population density near a 24 h land border-crossing facility connecting Zhuhai Macau. novelty work lies potential enhance operational forecasting ANN LSTM run five times, average pollutant forecasts obtained each model. Results demonstrated that both accurately predicted concentrations upcoming h, PM10 CO showing highest predictive reflected Pearson Correlation Coefficient (PCC) between 0.84 0.87 Kendall’s Tau (KTC) 0.66 0.70 values low Mean Bias (MB) 0.06 0.10, Fractional (MFB) 0.09 0.11, Root Square Error (RMSE) 0.14 0.21, Absolute (MAE) 0.11 0.17. Overall, model consistently delivered PCC (0.87) KTC (0.70) lowest MB (0.06), MFB (0.09), RMSE (0.14), MAE (0.11) across all SD (0.01), indicating greater precision reliability. As result, concludes outperforms offering more accurate consistent tool management.

Язык: Английский

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Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach

Computers in Biology and Medicine, Год журнала: 2024, Номер 182, С. 109209 - 109209

Опубликована: Сен. 26, 2024

Язык: Английский

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Electricity Production Prediction by Microsoft Azure Machine Learning Service and Python User Blocks

Advances in environmental engineering and green technologies book series, Год журнала: 2024, Номер unknown, С. 227 - 267

Опубликована: Май 1, 2024

In this chapter, the forecasting of electricity consumption and production is conducted by analyzing indicators from previous years. The problem addressed using machine learning within Microsoft Azure Machine Learning Studio. outcome an independent service integrated into Excel, enabling for specified dates. Excel user interface developed Visual Basic Applications. Python was used to create blocks modifying input data pools forming graphical dependencies, seamlessly original modules An additional aspect forecast results involves evaluating quality predicted indicators. materials chapter were sourced with support Ukraine's National Power Company UKRENERGO.

Язык: Английский

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Machine Learning-Based Prediction and Analysis of Air and Noise Pollution in Urban Environments

Опубликована: Июль 10, 2024

Язык: Английский

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