Non-linear Feature Selection Based on Convolution Neural Networks with Sparse Regularization

Wu, Wen-Bin; Chen, Si-Bao; Ding, Chris; Luo, Bin

doi:10.1007/s12559-023-10230-8

Cogn Comput

2023

DOI: 10.1007/s12559-023-10230-8

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Non-linear Feature Selection Based on Convolution Neural Networks with Sparse Regularization

Wen-Bin Wu,

Si-Bao Chen,

Chris Ding

et al.

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2024

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Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

Wu,

Lu,

Tian

et al. 2024

Atmosphere

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Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research.

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Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

Wu,

Lu,

Tian

et al. 2024

Atmosphere

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Non-linear Feature Selection Based on Convolution Neural Networks with Sparse Regularization

Cited by 1 publication

References 22 publications

Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

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