A ridge regression approach to estimate the relationship between landfill taxation and waste collection and disposal in England

Panzone, Luca A.; Ulph, Alistair; Areal, Francisco J.; Grippo, Valeria

doi:10.1016/j.wasman.2021.04.054

Cited by 29 publications

(15 citation statements)

References 95 publications

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“…The purpose of this study is to analyze the influence of various influencing factors on the error of infrared temperature measurement according to the experimental data, and to establish the error compensation model of infrared temperature measurement. In previous studies, regression analysis (such as multivariate linear regression [26][27][28][29][30], ridge regression [31,32], and logistic regression [33,34]) is usually used to analyze the relationship between multiple factors and the establishment of a mathematical model. Moreover, the ordinary least square (OLS) method is the most widely-used parameter estimation method in regression analysis.…”

Section: Modeling Methodsmentioning

confidence: 99%

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

Cheng

et al. 2021

Mathematics

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In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.

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Section: Modeling Methodsmentioning

confidence: 99%

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

Cheng

et al. 2021

Mathematics

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“…where 𝛼 is a parameter used to balance the regularization factor and the error. The second factor is 𝑙 2 regularization, which avoids overfitting [12].…”

Section: Ridge and Lasso Regressionmentioning

confidence: 99%

House Price Prediction Based on Machine Learning: A Case of King County

Wang¹,

Zhao²

2022

Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)

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This paper focuses on formulating a feasible method for house price prediction. A dataset containing features and house price of King County in the US is used. During the data preprocessing, extreme values are winsorized and highly correlated features are removed. Eight models including Catboost, lightGBM and XGBoost serve as candidate models. They are evaluated by several evaluation indicators, including rooted mean square error, R-squared score, adjusted Rsquared score and K-fold cross validation score. The model that has low RMSE, achieves a high R-squared score and adjusted R-squared score, especially in the test set, and acquires a high score in cross validation is considered a better model. This paper finds out that Catboost performs the best among all models and can be used for house price prediction. Location, living space and condition of the house are the most important features influencing house price. After comparison and contrast with other papers, it is attested that findings in this paper conform to real life. This paper formulates a model that fits better than preceding studies for house price prediction and makes necessary supplement to the exploration of features that influence house price from a microscope.

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“…According to the EU’s rules regarding Packaging and Packaging Waste, the packaging design for products must be optimized to prevent the generation of additional packaging waste [ 29 ]. Landfill taxation was introduced to price the disposal of HW in the UK, and its elastic characteristics regarding disposal were effective in shifting HW from landfill to incineration [ 30 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative Evaluation of Waste Separation Management Policies in the Yangtze River Delta Based on the PMC Index Model

Liu

2022

IJERPH

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Numerous policies have been formulated and implemented to strengthen waste separation management activities in many countries. Waste separation management policies (WSMPs) must be evaluated as the precondition for reducing deviations from policy implementation and improving waste separation performance. Based on text mining technology and the construction of a policy modeling consistency (PMC) index model, we conducted a quantitative evaluation of 22 WSMPs issued by central governmental departments and provinces in the Yangtze River Delta, China from 2013 to 2021 and analyzed their optimization paths. The results suggest that the PMC index of the selected WSMPs has an upward trend. The average PMC index of 22 WSMPs was 6.906, indicating good quality in the policy texts. The PMC index identified seven, nine, five, and one of the policies as being perfect, excellent, good, and acceptable, respectively. The characteristics of WSMPs were further illustrated through PMC surface charts. Based on this, optimization paths for WSMPs with lower PMC indexes are proposed, which indicate that existing WSMPs have great potential for optimization in terms of harsher constraint regulations, context-appropriate incentives, and cultivation of market participants. Finally, this study provides a beneficial reference for similar cities or countries to improve their performance in the management of waste separation and environmental protection.

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A ridge regression approach to estimate the relationship between landfill taxation and waste collection and disposal in England

Cited by 29 publications

References 95 publications

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

House Price Prediction Based on Machine Learning: A Case of King County

Quantitative Evaluation of Waste Separation Management Policies in the Yangtze River Delta Based on the PMC Index Model

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