Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia

Ng, Kar Yong; Awang, Norhashidah

doi:10.1007/s10661-017-6419-z

Cited by 43 publications

(20 citation statements)

References 11 publications

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“…Table 1 summarizes several AI techniques in predicting PM 10 concentration and TSP. Also, similar and relevant work on the prediction of PM 10 concentration can be found at the following references [52][53][54][55][56][57][58][59][60][61]. Given the previous work, it can be seen that it is feasible to use AI techniques to predict dust concentration as well as PM 10 emissions.…”

Section: Introductionmentioning

confidence: 86%

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

et al. 2019

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Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM10. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM10 in open-pit mines. Therefore, the prediction and control of PM10 concentration in the production process are necessary for environmental and health protection. In this study, PM10 concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM10 concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM10 concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R2) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R2 of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R2 of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R2 of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM10 concentration caused by drilling operations in open-pit mines.

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Section: Introductionmentioning

confidence: 86%

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

et al. 2019

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“…In this study multiple linear regression (MLR) (İçağa and Sabah 2009;Ng and Awang 2018;Sousa et al 2007;Ul-Saufie et al 2012) was used to determine the relationship between daily COVID-19 confirmed cases density (CCD) and the average concentrations of the four criteria air pollutants (i.e., CO, NO 2 , O 3 , and SO 2 ). CCD is obtained by dividing the number of total confirmed cases by the population size.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Spatial sensitivity analysis of COVID-19 infections concerning the satellite-based four air pollutants levels

Bigdeli

Taheri

Mohammadian

2021

Int. J. Environ. Sci. Technol.

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“…For example, if R 2 = 0.80, it means that 80% points will be included inside the regression track. A higher value of R 2 indicates a better fit for the given dataset [22]. It also helps to examine how differences in one parameter can be explained by a difference in a second variable.…”

Section: Developing Statistical Model For the Process Performance Chamentioning

confidence: 99%

Multi performance optimization in machining of EN31-535A99SS with interchangeable straight cemented tungsten carbide-cobalt mixed (WC–Co) insert grade (CSTC—K20) using Taguchi coupled grey relational analysis

Hussain

2020

SN Appl. Sci.

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EN31-535A99 SS is an advanced and high carbon chromium-molybdenum alloy steel offering a high degree of compressive strength and abrasion resistance. Best suitable for machined components subjected to abrasive wear, swaging dies, press tools, roller bearings which do not merit a more complex quality. However, its machining is one of the challenging fields in the metal cutting industries due to the high hardness which results in low productivity and high manufacturing cost. In this paper a new interchangeable two phases straight cemented tungsten carbide-cobalt mixed (WC-Co) insert grade (CSTC-K20) tool has been introduced to obtain the optimal turning parameters for the multi-performance characteristics in the turning process by Taguchi coupled grey relational analysis. The flank wear (V bc) and surface finish (R a) were considered as the performance characteristics while the cutting speed (V), feed rate (F) and depth of cut (D) as the controllable process parameters. Experiments were planned as per Taguchi L16 orthogonal array taking three process parameters at four levels. The results revealed that the optimal parametric settings for multi-response becomes cutting speed of 75 m/min, feed rate of 0.05 mm/tooth and depth of cut as 0.25 mm respectively. At this stage the surface roughness (Ra) reduced from 0.132 to 0.061 µm while tool flank wear (VBc) reduced from 0.89 to 0.69 mm and the grey relational grade enhanced up to 0.2867. The polynomial regression model presented higher R 2 value as compared to linear regression model. It is recommended that metal cutting industries could use these optimum approaches to reduce the material waste, manufacturing cost and increase productivity while machining of EN31-535A99 SS.

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Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia

Cited by 43 publications

References 11 publications

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

Spatial sensitivity analysis of COVID-19 infections concerning the satellite-based four air pollutants levels

Multi performance optimization in machining of EN31-535A99SS with interchangeable straight cemented tungsten carbide-cobalt mixed (WC–Co) insert grade (CSTC—K20) using Taguchi coupled grey relational analysis

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