Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city: A case study of New Delhi

Hariharan, Ramya

doi:10.1016/j.uclim.2021.100780

Cited by 14 publications

(9 citation statements)

References 53 publications

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“…Kumar et al (2018) used RF for the prediction of plastic waste generation rate that showed an R-square of 0.66. The size of the random forest, that is, the number of decision trees (Ntrees) and the number of features tried in each segmentation (Nfeatures) have a significant impact on the predictive ability of the RF model (Hariharan, 2021). When Ntrees exceed a certain value, the prediction performance of the model converges.…”

Section: Random Forestmentioning

confidence: 99%

Municipal Solid Waste Forecasting in China Based on Machine Learning Models

et al. 2021

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As the largest producing country of municipal solid waste (MSW) around the world, China is always challenged by a lower utilization rate of MSW due to a lack of a smart MSW forecasting strategy. This paper mainly aims to construct an effective MSW prediction model to handle this problem by using machine learning techniques. Based on the empirical analysis of provincial panel data from 2008 to 2019 in China, we find that the Deep Neural Network (DNN) model performs best among all machine learning models. Additionally, we introduce the SHapley Additive exPlanation (SHAP) method to unravel the correlation between MSW production and socioeconomic features (e.g., total regional GDP, population density). We also find the increase of urban population and agglomeration of wholesales and retails industries can positively promote the production of MSW in regions of high economic development, and vice versa. These results can be of help in the planning, design, and implementation of solid waste management system in China.

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Section: Random Forestmentioning

confidence: 99%

Municipal Solid Waste Forecasting in China Based on Machine Learning Models

et al. 2021

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“…McClymont and Hu ( 2021 ) presented a recent literature review of the effect of weather indicators on COVID-19 transmission. The meteorological factors, which were considered in recent studies to examine their correlation with the number of COVID-19 cases, include the following: Temperature (Abdelhafez et al 2021 ; Alkhowailed et al 2020 ; Auler et al 2020 ; Bashir et al 2020a ; Bilal et al 2021b ; Briz-Redón and Serrano-Aroca 2020 ; Dalal and Pandey 2021 ; Fernández-Ahúja and Martínez 2021 ; Fu et al 2021 ; Hariharan 2021 ; Iqbal et al 2020a ; Menebo 2020 ; Pani et al 2020 ; Şahin 2020 ; Shahzad et al 2020 ; Shi et al 2020a ; Sobral et al 2020 , p. 2; To et al 2021 ; Tosepu et al 2020 ; Yuan et al 2021 ) Humidity (Abdelhafez et al 2021 ; Alkhowailed et al 2020 ; Auler et al 2020 ; Bashir et al 2020a ; Basray et al 2021 , 2021; Dalal and Pandey 2021 ; Fu et al 2021 ; Hariharan 2021 ; Pani et al 2020 ; Şahin 2020 ; Shi et al 2020a ; Tosepu et al 2020 ; Yuan et al 2021 ) Rainfall (Auler et al 2020 ; Bashir et al 2020a ; Basray et al 2021 ; Fernández-Ahúja and Martínez 2021 ; Menebo 2020 ; Sobral et al 2020 , p. 2; Tosepu et al 2020 ), Wind speed (Abdelhafez et al 2021 ; Alkhowailed et al 2020 ; Bashir et al 2020a ; Coccia 2021 ; Dalal and Pandey 2021 ; Hariharan 2021 ; Menebo 2020 ; Pani et al …”

Section: Introductionmentioning

confidence: 99%

“…Temperature (Abdelhafez et al 2021 ; Alkhowailed et al 2020 ; Auler et al 2020 ; Bashir et al 2020a ; Bilal et al 2021b ; Briz-Redón and Serrano-Aroca 2020 ; Dalal and Pandey 2021 ; Fernández-Ahúja and Martínez 2021 ; Fu et al 2021 ; Hariharan 2021 ; Iqbal et al 2020a ; Menebo 2020 ; Pani et al 2020 ; Şahin 2020 ; Shahzad et al 2020 ; Shi et al 2020a ; Sobral et al 2020 , p. 2; To et al 2021 ; Tosepu et al 2020 ; Yuan et al 2021 )…”

Section: Introductionunclassified

“…Humidity (Abdelhafez et al 2021 ; Alkhowailed et al 2020 ; Auler et al 2020 ; Bashir et al 2020a ; Basray et al 2021 , 2021; Dalal and Pandey 2021 ; Fu et al 2021 ; Hariharan 2021 ; Pani et al 2020 ; Şahin 2020 ; Shi et al 2020a ; Tosepu et al 2020 ; Yuan et al 2021 )…”

Section: Introductionunclassified

“…Wind speed (Abdelhafez et al 2021 ; Alkhowailed et al 2020 ; Bashir et al 2020a ; Coccia 2021 ; Dalal and Pandey 2021 ; Hariharan 2021 ; Menebo 2020 ; Pani et al 2020 ; Şahin 2020 ; Yuan et al 2021 )…”

Section: Introductionmentioning

confidence: 99%

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