Assessment of Bimodal Machine Learning framework in predicting air quality index articulated as numerical and text encoded targets over urban centers DOI Creative Commons
Jagadish Kumar Mogaraju

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 6, 2024

Abstract Machine learning tools were used in this study to extract information on prediction capabilities using regression and classification modalities. PM10, PM2.5, NO, NO2, NOX, NH3, SO2, CO, O3, Benzene, Toluene, Xylene as predictors. AQI was a target variable with numerical text-encoded values. Nineteen regressor fifteen classifier models tested for capabilities, features influencing presented. We six evaluation metrics, i.e., MAE, MSE, RMSE, R2, RMSLE, MAPE, under mode Accuracy, AUC, Recall, Precision, F1, Kappa, MCC mode. When used, we observed that the Extra Trees Regressor performed well an R2 of 0.94. For mode, Random Forest Classifier relatively better accuracy precision 0.824. PM2.5 PM10 are vital essential conclude Particulate matter is crucial predicting over stations considered supported by ML-based observations.

Язык: Английский

Does urban–rural integration contribute to environmental health? Exploring the interplay between urban–rural integration and air quality dynamics in Yangtze River middle reaches city cluster DOI Creative Commons
Jixin Yang, Bowen Fu, Xufeng Cui

и другие.

Frontiers in Public Health, Год журнала: 2025, Номер 12

Опубликована: Янв. 7, 2025

Exploring the coordinated relationship between urban-rural integration and air quality has significant implications for promoting development, preventing pollution ensuring residents' health. This study takes Yangtze River middle reaches city cluster as a case study, calculates levels of analyzes their coupled coordination driving factors, explores path development. constructs coupling degree model to analyze development level level. We use trend surface method spatial divergence characteristics quality. In addition, we used geographic detector factors affecting degree. (1) The overall showed an upward trend. High-value regions were concentrated in Wuhan, Chang-Zhu-Tan, Nanchang metropolitan areas. (2) Air Quality Index decline, with most improvements observed Changsha, Jiujiang. (3) increased from 0.570 2013 0.794 2021, 0.337 0.591 2021. link deepened over time, two promote each other, making develop towards environmental friendliness. distribution shows "high west low east, high north south" (4) Per capita GDP, non-agricultural employment ratio, circulation media, population urbanization level, fixed asset investment identified core confirms that are gradually changing direction high-quality coordination. However, there great differences among cities, regional imbalance is prominent, driven by multidimensional factors.

Язык: Английский

Процитировано

0
Wind Extremes over Built Terrain: Characterization and Geometric Determinants DOI
Jing Wang, Maider Llaguno-Munitxa, Qi Li

и другие.

Boundary-Layer Meteorology, Год журнала: 2025, Номер 191(2)

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Fast prediction of odor concentration along pig manure chain based on machine learning: Monitoring 20 instead of over 100 odorous substances DOI
Tiantian Cao, Yunhao Zheng,

Bin Shang

и другие.

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 233, С. 110146 - 110146

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

0
Machine learning-guided integration of fixed and mobile sensors for high resolution urban PM2.5 mapping DOI Creative Commons
Tianshuai Li, Xin Huang, Xin Zhang

и другие.

npj Climate and Atmospheric Science, Год журнала: 2025, Номер 8(1)

Опубликована: Март 10, 2025

Язык: Английский

Процитировано

0
Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment DOI Creative Commons
Peng Wei, Hao Song, Yuan Shi

и другие.

Environment International, Год журнала: 2024, Номер 191, С. 108992 - 108992

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

3
Machine learning and deep learning approaches for PM2.5 prediction: a study on urban air quality in Jaipur, India DOI

Saurabh Singh,

Gourav Suthar

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

Опубликована: Дек. 22, 2024

Язык: Английский

Процитировано

2
Refined prediction of SO2 concentration around Chinese coking enterprises and exposure risk assessment of different populations based on buffer Latin hypercube DOI
Lei Mei, Yuan Xu, Tienan Ju

и другие.

Journal of Cleaner Production, Год журнала: 2024, Номер unknown, С. 143833 - 143833

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

1
Spatial and Spatiotemporal Modeling of Intra-Urban Ultrafine Particles: A Comparison of Linear, Nonlinear, Regularized, and Machine Learning Methods DOI
Julien Vachon, Stéphane Buteau, Ying Liu

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Assessment of Bimodal Machine Learning framework in predicting air quality index articulated as numerical and text encoded targets over urban centers DOI Creative Commons
Jagadish Kumar Mogaraju

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 6, 2024

Abstract Machine learning tools were used in this study to extract information on prediction capabilities using regression and classification modalities. PM10, PM2.5, NO, NO2, NOX, NH3, SO2, CO, O3, Benzene, Toluene, Xylene as predictors. AQI was a target variable with numerical text-encoded values. Nineteen regressor fifteen classifier models tested for capabilities, features influencing presented. We six evaluation metrics, i.e., MAE, MSE, RMSE, R2, RMSLE, MAPE, under mode Accuracy, AUC, Recall, Precision, F1, Kappa, MCC mode. When used, we observed that the Extra Trees Regressor performed well an R2 of 0.94. For mode, Random Forest Classifier relatively better accuracy precision 0.824. PM2.5 PM10 are vital essential conclude Particulate matter is crucial predicting over stations considered supported by ML-based observations.

Язык: Английский

Процитировано

0