Pragmatic Method Based on Intelligent Big Data Analytics to Prediction Air Pollution

Al-Janabi, Samaher; Yaqoob, Ali Falah; Mohammad, Mustafa

doi:10.1007/978-3-030-23672-4_8

Cited by 12 publications

(4 citation statements)

References 21 publications

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Section: Resultsmentioning

confidence: 99%

“…Finally, the proposed method in this study can be compared to recent approaches that utilize Big Data and intelligent computation, such as those presented by Al-Janabi et al ( 2021 ), Al-Janabi et al ( 2020b ), and Al-Janabi et al ( 2019 ). These methods aimed to predict multiple air pollution concentrations using the Intelligent Forecaster of Concentrations caused air pollution (IFCsAP) (Al-Janabi et al 2021 ), a pragmatic method based on intelligent big data analytics (Al-Janabi et al 2019 ), and intelligent computation (Al-Janabi et al 2020b ). However, the main difference between our method and these previous studies is that our focus is on clustering and visualizing spatial–temporal air pollutant curves through functional data approaches, while their focus was on predicting discrete air pollutant data through intelligent big data analytics.…”

Section: Resultsmentioning

confidence: 99%

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Unveiling air pollution patterns in Yemen: a spatial–temporal functional data analysis

Hael

2023

Environ Sci Pollut Res

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Unveiling air pollution patterns in Yemen: a spatial–temporal functional data analysis

Hael

2023

Environ Sci Pollut Res

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“…Zhang et al (2019), for example, assume that big data analysis (BDA) is helpful to provide decision makers with a sound scientific advice on solving global sustainable development problems. Therefore, BDA is gradually used in forecasting air pollutant concentrations (Alaoui et al, 2019;Al_Janabi et al, 2019;Xu et al, 2020) and other research fields.…”

Section: Introductionmentioning

confidence: 99%

Assessments of Air Pollution Control Effectiveness Based on a Sharp Regression Discontinuity Design —Evidence From China’s Environmental Big Data

Wang

Huang

Zhang

et al. 2021

Front. Environ. Sci.

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The Assessment andAppraisal Method for Ecological Construction Targets (the Method) was promulgated in 2016, which provided a concrete instruction for China’s air pollution control and established an explicit standard for reducing air pollutant concentration. This study implements a sharp regression discontinuity (RD) design and makes an assessment on air quality control effectiveness of the Method based on the high-volume big data acquired from 173 cities in China. The results show that the Method has significantly improved air pollution control on the overall air quality index (AQI) and reducing concentrations of PM2.5, PM10, SO2, NO2, and CO across the country in the observation periods. However, no reduction effect was observed for O3. The robustness tests support the conclusion as well. Besides, the heterogeneity analysis illustrates that the policy had a significant short-term treatment effect in East, South, Central, North, Northwest, Southwest, and Northeast China. However, the Method’s effect is found to decline over time either nationwide or regionally according to the persistence analysis. Therefore, this article puts forward several suggestions regarding the formulation of long-term regulations for air pollution control, the transformation of the growth model for sustainable development, and optimization of the incentive system for improved pollution control and prevention.

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“…Data mining algorithms are classified into two functional types, predictive and descriptive [5], and eight application types, classification, estimation, prediction, correlation analysis, sequence, time sequence, description, and visualization [6]. The successful application of data mining in biomedical research provides reliable support for clinical decision-making (e.g., disease diagnosis, therapy selection, and disease prognosis prediction) and management decision-making (e.g., staffing, medical insurance, and quality control) [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

Zhao

Cui

et al. 2020

BMC Med Inform Decis Mak

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Background: Surgical resection of pheochromocytoma may lead to high risk factors for intraoperative hemodynamic instability (IHD), which can be life-threatening. This study aimed to investigate the risk factors that could predict IHD during pheochromocytoma surgery by data mining. Method: Relief-F was used to select the most important features. The accuracies of seven data mining models (CART, C4.5, C5.0, and C5.0 boosted), random forest algorithm, Naive Bayes and logistic regression were compared, the cross-validation, hold-out, and bootstrap methods were used in the validation phase. The accuracy of these models was calculated independently by dividing the training and the test sets. Receiver-Operating Characteristic curves were used to obtain the area under curve (AUC). Result: Random forest had the highest AUC and accuracy values of 0.8636 and 0.8509, respectively. Then, we improved the random forest algorithm according to the classification of imbalanced data. Improved random forest model had the highest specificity and precision among all algorithms, including relatively higher sensitivity (recall) and the highest f1-score integrating recall and precision. The important attributes were body mass index, mean age, 24 h urine vanillylmandelic acid/upper normal limit value, tumor size and enhanced computed tomography difference. Conclusions: The improved random forest algorithm may be useful in predicting IHD risk factors in pheochromocytoma surgery. Data mining technologies are being increasingly applied in clinical and medical decision-making, and provide continually expanding support for the diagnosis, treatment, and prevention of various diseases.

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Pragmatic Method Based on Intelligent Big Data Analytics to Prediction Air Pollution

Cited by 12 publications

References 21 publications

Unveiling air pollution patterns in Yemen: a spatial–temporal functional data analysis

Unveiling air pollution patterns in Yemen: a spatial–temporal functional data analysis

Assessments of Air Pollution Control Effectiveness Based on a Sharp Regression Discontinuity Design —Evidence From China’s Environmental Big Data

Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery

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