Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 109, P. 105537 - 105537

Published: May 18, 2024

Language: Английский

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A systematic survey of air quality prediction based on deep learning

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 93, P. 128 - 141

Published: March 16, 2024

The impact of air pollution on public health is substantial, and accurate long-term predictions quality are crucial for early warning systems to address this issue. Air prediction has drawn significant attention, bridging environmental science, statistics, computer science. This paper presents a comprehensive review the current research status advances in methods. Deep learning, novel machine learning approach, demonstrated remarkable proficiency identifying complex, nonlinear patterns data, yet its application still relatively nascent. also conducts systematic analysis summarizes how cutting-edge deep models applied prediction. Initially, historical evolution methods datasets presented. followed by an examination conventional techniques. A thorough comparative progress made with both traditional learning-based provided. particularly focuses three aspects: temporal modeling, spatiotemporal attention mechanisms. Finally, emerging trends field identified discussed.

Language: Английский

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PM2.5 concentration prediction using machine learning algorithms: an approach to virtual monitoring stations

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 8, 2025

One of the most important pollutants is PM2.5, which particularly to monitor pollutant levels keep concentration under control. In this research, an attempt has been made predict concentrations PM2.5 using four Machine Learning (ML) models. The ML methods include Light Gradient Boosting (LGBM), Extreme Regressor (XGBR), Random Forest (RF) and (GBR). mean maximum were recorded 32.84 µg/m3 160.25 µg/m2, respectively, indicating occurrence occasional episodes high pollution from 2016 2022. dropped below 30 µg/m2 in 2018 due reduced human activities during COVID-19 lockdowns but significantly increased because ongoing operation heavy industries post-COVID-19 2021. models performed very well predicting with around 95% their predictions falling within factor observed concentration. results presented that among algorithms, GBR confirmed good model performance compared other models, lowest MSE (5.33) RMSE (2.31), as accuracy measures. This suggests best for reducing large errors, making it more robust capturing variations levels. conclusion, study proposed a method obtain high-accuracy prediction are useful air quality monitoring on global scale improving acute exposure assessment epidemiological research.

Language: Английский

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Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: July 26, 2023

Abstract Air pollution is a serious problem that affects economic development and people’s health, so an efficient accurate air quality prediction model would help to manage the problem. In this paper, we build combined accurately predict AQI based on real data from four cities. First, use ARIMA fit linear part of CNN-LSTM non-linear avoid blinding in hyperparameter setting. Then, dilemma setting, Dung Beetle Optimizer algorithm find hyperparameters model, determine optimal hyperparameters, check accuracy model. Finally, compare proposed with nine other widely used models. The experimental results show paper outperforms comparison models terms root mean square error (RMSE), absolute (MAE) coefficient determination (R 2 ). RMSE values for cities were 7.594, 14.94, 7.841 5.496; MAE 5.285, 10.839, 5.12 3.77; R 0.989, 0.962, 0.953 respectively.

Language: Английский

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Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Atmospheric Pollution Research, Journal Year: 2023, Volume and Issue: 14(6), P. 101766 - 101766

Published: April 18, 2023

Language: Английский

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AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: March 21, 2024

Abstract The integration of multi-source sensors based AIoT (Artificial Intelligence Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields sustainable smart environmental design, urban development, pollution control. This study focuses on enhancing prediction emission, with a special emphasis pollutants, utilizing advanced deep learning (DL) techniques. Recurrent neural networks (RNNs) long short-term memory (LSTM) have shown promise predicting trends time series data. However, challenges persist due to unpredictability data scarcity long-term historical for training. To address these challenges, this introduces AIoT-enhanced EEMD-CEEMDAN-GCN model. innovative approach involves decomposing input signal using EEMD (Ensemble Empirical Mode Decomposition) CEEMDAN (Complete Ensemble Decomposition Adaptive Noise) extract intrinsic mode functions. These functions are then processed through GCN (Graph Convolutional Network) model, enabling precise trends. model’s effectiveness validated datasets from four provinces China, demonstrating its superiority over various models (GCN, EMD-GCN) decomposition (EEMD-GCN, CEEMDAN-GCN). It achieves higher accuracy better fitting, outperforming other key metrics such as MAE (Mean Absolute Error), MSE Squared MAPE Percentage R 2 (Coefficient Determination). implementation model allows decision-makers more accurately anticipate changes quality, particularly concerning carbon emissions. facilitates effective planning mitigation measures, improvement public health, optimization resource allocation. Moreover, adeptly addresses complexities data, contributing significantly enhanced monitoring management strategies context development conservation.

Language: Английский

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Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind

Environmental Pollution, Journal Year: 2023, Volume and Issue: 342, P. 123082 - 123082

Published: Dec. 5, 2023

Language: Английский

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A deep learning model integrating a wind direction-based dynamic graph network for ozone prediction

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 946, P. 174229 - 174229

Published: June 24, 2024

Language: Английский

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An optimized decomposition integration model for deterministic and probabilistic air pollutant concentration prediction considering influencing factors

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(7), P. 102144 - 102144

Published: April 4, 2024

Language: Английский

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Deep learning for spatiotemporal forecasting in Earth system science: a review

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Aug. 19, 2024

Deep learning (DL) has demonstrated strong potential in addressing key challenges spatiotemporal forecasting across various Earth system science (ESS) domains. This review examines 69 studies applying DL to tasks within climate modeling and weather prediction, disaster management, air quality modeling, hydrological renewable energy forecasting, oceanography, environmental monitoring. We summarize commonly used architectures for ESS, technical innovations, the latest advancements predictive applications. While have proven capable of handling data, remain tackling complexities specific such as complex scale dependencies, model interpretability, integration physical knowledge. Recent innovations demonstrate growing efforts integrate knowledge, improve explainability, adapt domain-specific needs, quantify uncertainties. Finally, this highlights future directions, including (1) developing more interpretable hybrid models that synergize traditional approaches, (2) extending generalizability through techniques like domain adaptation transfer learning, (3) advancing methods uncertainty quantification missing data handling.

Language: Английский

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