A comparison of machine learning methods for ozone pollution prediction

Pan, Qilong; Harrou, Fouzi; Sun, Ying

doi:10.1186/s40537-023-00748-x

Cited by 17 publications

(3 citation statements)

References 69 publications

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“…It is a curve drawn in 𝑇𝑃 and 𝐹𝑃 coordinates according to different decision valves. [35] 𝐴𝑈𝐶is the area around the coordinate axis under the ROC curve, ROC curve is generally located on the 𝑦 = 𝑥 line, therefore 𝐴𝑈𝐶 value is generally between 0.5 and 1, the closer to 1 means the better detection method.…”

Section: Evaluating Indicatormentioning

confidence: 99%

A multi-source heterogeneous data-driven method for the site selection of elderly care facilities

Wang

2023

Preprint

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In the site selection of elderly care facilities era, the traditional methods of site selection for elderly care facilities are subjective, the data sources of methods are single, and the research objects of the methods are not realistic, this paper presents a multi-source heterogeneous data-driven method for the site selection of elderly care facilities. This method takes the Xicheng and Dongcheng districts of Beijing as the research objects. On the grid scale of 500m*500m, six features of population factors, economic level, traffic accessibility, medical conditions, the convenience of life, and natural factors are constructed through multi-source data. Using an integrated algorithm, the location of elderly care facilities is predicted quantitatively, and the priority of features is sorted. The research found that: (1) The model in this paper predicts more accurately than traditional models (2) the number of education facilities, proportion of green areas, medical conditions, traffic accessibility, and the number of shopping facilities are the main features for the site selection of elderly care facilities in Xicheng and Dongcheng districts. This method can also provide references for the site selection of other facilities.

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Section: Evaluating Indicatormentioning

confidence: 99%

A multi-source heterogeneous data-driven method for the site selection of elderly care facilities

Wang

2023

Preprint

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“…To improve the performance of the RFR and XGBoost models, a distribution lag was applied to the derived case fatality rate variable. Distribution lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable [47], [48]. Time lag variables were created for the previous day's, week's, and month's case fatality rate using the shift() method from the Pandas Library in Python.…”

Section: ) Random Forest Regressor With Distribution Lag and Extreme ...mentioning

confidence: 99%

Accuracy and Performance of Machine Learning Methodologies: Novel Assessments of Country Pandemic Vulnerability Based on Non-Pandemic Predictors

Vlajnic,

Simske

2023

IEEE Access

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The devastating effects of the COVID-19 pandemic created a need for sensitive and accurate machine learning methodologies for assessment of predictors of pandemic vulnerability. The performance of machine learning methodologies was assessed to correlate, predict, and rank selected demographic, health, and economic public health parameters, relative to COVID-19 case fatality rates in 26 countries. Random Forest Regressor (RFR) and Extreme Gradient Boosting models (XGBoost), both with distribution lags, a novel K-means-Coefficient of Variance (K-means-COV) sensitivity analysis approach and Ordinary Least Squares Multifactor Regression methodologies were used to evaluate correlation of predictive nonpandemic features, grouped into two novel public health indices, Population Health Index (PHI) and Country Health Index (CHI). A novel scoring model was developed for country level pandemic risk assessment. Multiple analyses demonstrated that XGBoost methodology had higher sensitivity and accuracy across all performance metrics relative to RFR, proving that cardiovascular death rate was the most dominant predictive feature for PHI for 46% of countries, and hospital beds per thousand people for CHI (46%). The novel K-means-COV sensitivity analysis approach performed with high accuracy and was successfully validated across all three methods, demonstrating that female smokers was the most common predictive feature across different analysis sets. All assessed machine learning methodologies performed with high accuracy and demonstrated strong predictive value. Only 42.3% of countries in the PHI and 15.4% in the CHI were identified to have a low pandemic vulnerability risk.

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“…Deep learning often requires a large amount of training data to improve the accuracy and robustness of the model, and a small amount of data suffers from many problems, such as difficulty in convergence and overfitting. Therefore, machine learning is the main method used in prediction studies and performs well with small samples [50,51]. Kawamura et al [35,44] found that the machine learning model showed higher accuracy than the traditional linear model (e.g., multiple linear stepwise regression and partial least squares) when predicting soil parameters (e.g., N and P) using Vis-NIR.…”

Section: Introductionmentioning

confidence: 99%

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

Jiang,

Zhao,

2023

Agronomy

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Visible and near-infrared spectroscopy (Vis–NIR, 350–1100 nm) has great potential for predicting soil properties. However, current research on the hyperspectral prediction of soil parameters in agricultural areas of alpine regions and the types of parameters included is limited, and optimal spectral treatments and predictive models applicable to different parameters have not been sufficiently investigated. Therefore, we evaluated the accuracy of predicting total nitrogen (TN), phosphorus pentoxide (TP2O5), total potassium oxide (TK2O), alkali-hydrolyzable nitrogen (AHN), effective phosphorus (AP), effective potassium (AK), soil organic matter (SOM), and pH in the Qinghai–Tibet Plateau using the Vis–NIR technique in combination with spectral transformations, correlation analysis, feature selection, and machine learning. The results show that spectral transformations improve the correlation between spectra and parameters but are dependent on the parameter type and the method used. Continuum removal (CR), logarithmic first-order differential (FDL), and inverse first-order differential (FDR) had the most significant effects. The feature bands were extracted using the SPA and modeled using partial least squares (PLSR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and backpropagation neural networks (BPNNs). The accuracy was evaluated based on R2, RMSE, RPD, and RPIQ. We found that the PLSR model only enables the prediction of SOM and pH with lower accuracy than the remaining models. XGBoost can predict all of the parameters but only for AHN; the prediction performance is better than other methods (R2 = 0.776, RMSE = 0.043 g/kg, and RPIQ = 2.88). The RF, SVM, and BPNN models cannot predict AK, AP, and AHN, respectively. In addition, TP2O5, AP, and pH are best suited for modeling using RF (RPIQ = 2.776, 3.011, and 3.198); TN, AK, and SOM are best suited for modeling using BPNN (RPIQ = 2.851, 2.394, and 3.085); and AHN and TK2O are best suited for XGBoost and SVM, respectively (RPIQ = 2.880 and 3.217). Therefore, this study can provide technical and data support for the accurate and efficient acquisition of soil parameters in alpine agriculture.

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A comparison of machine learning methods for ozone pollution prediction

Cited by 17 publications

References 69 publications

A multi-source heterogeneous data-driven method for the site selection of elderly care facilities

A multi-source heterogeneous data-driven method for the site selection of elderly care facilities

Accuracy and Performance of Machine Learning Methodologies: Novel Assessments of Country Pandemic Vulnerability Based on Non-Pandemic Predictors

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

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