Feature selection by machine learning models to identify the public's changing priorities during the COVID-19 pandemic

Creative Commons License

Menguc K., Aydin N.

JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, cilt.14, sa.5, ss.385-403, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 5
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3233/ais-220200
  • Dergi Adı: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.385-403
  • Anahtar Kelimeler: Coronavirus, machine learning, feature engineering, SHAP, smart city, MULTICRITERIA DECISION-MAKING, RANDOM FOREST, BIG DATA, SMART
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

People around the world have experienced fundamental transformations during mass events. The Industrial Revolution, WorldWar II, and the collapse of the BerlinWall are some of the cases that have caused radical societal changes. COVID-19 has also been a process of mass experiences regarding society. Determining the mass impact the pandemic has had on society shows that the pandemic is facilitating the transition to the so-called new normal. Istanbul is a multi-identity city where 16 million people have intensely experienced the pandemic's impact. While determining the identities of cities in the world, one can see that different city structures provide different data sets. This study models a machine learning algorithm suitable for the data set we've determined for the 39 different districts of Istanbul and 82 different features of Istanbul. The aim of the study is to indicate the changing societal trends during the COVID-19 pandemic using machine learning techniques. Thus, this work contributes to the literature and real life in terms of redesigning cities for the post-COVID19 period. Another contribution of this study is that the proposed methodology provides clues on what people in cities consider important during a pandemic.