About: Research on PM(2.5) Integrated Prediction Model Based on Lasso-RF-GAM

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Research on PM(2.5) Integrated Prediction Model Based on Lasso-RF-GAM
Creator	Peng, Yan Wei, Haoxiang Wu, Tingxian Zhao, Ziru
source	PMC
abstract	PM(2.5) concentration is very difficult to predict, for it is the result of complex interactions among various factors. This paper combines the random forest-recursive feature elimination algorithm and lasso regression for joint feature selection, puts forward a PM(2.5) concentration prediction model based on GAM. Firstly, the original data is standardized in the data input layer. Secondly, features were selected with RF-RFE and lasso regression algorithm in the feature selection layer. Meanwhile, weighted average method fused the two feature subsets to obtain the final subset, RF-lasso-T. Finally, the generalized additive models (GAM) is used to predict PM(2.5) concentration on the RF-lasso-T. Simulated experiments show that feature selection allows GAM model to run more efficiently. The deviance explained by the model reaches 91.5%, which is higher than only using a subset of RF-RFE. This model also reveals the influence of various factors on PM(2.5), which provides the decision-making basis for haze control.
has issue date	2020-07-11(xsd:dateTime)
bibo:doi	10.1007/978-981-15-7205-0_8
has license	no-cc
sha1sum (hex)	88fd1caab663bccda5af316490fb5a46debd9215
schema:url	https://doi.org/10.1007/978-981-15-7205-0_8
resource representing a document's title	Research on PM(2.5) Integrated Prediction Model Based on Lasso-RF-GAM
has PubMed Central identifier	PMC7351685
schema:publication	Data Mining and Big Data
resource representing a document's body	covid:88fd1caab663bccda5af316490fb5a46debd9215#body_text
is schema:about of	named entity 'algorithm' named entity 'basis' named entity 'subset' named entity 'standardized' named entity 'models' »more»

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