Cigdem Baskici, Aydan Aytar, Halil Ersoy, Camilla Wiktsröm-Grotell, Marina Arell-Sundberg, Hugo Neves, Verónica Coutinho, Aurelija Blaževičienė, Alina Vaškelytė, Anne Söderlund, Johanna Fritz, Raimonds Strods, Nora Jansone-Ratinika, Sultan Kav
"Being in the digital box". Academic staff experiences in online practical teaching: A qualitative study from six universities and countries
Heliyon
development monitoring technology variety ozone precursors detected monitoring stations increased dramatically brought great increment information ozone prediction explanation studies study completes feature mining reconstruction multisource data meteorological data conventional pollutant data precursors data using machine learning approach built crossstacked ensemble learning model csem feature engineering process study reconstructed two vocs variables associated ozone found works best use top seven variables highest contribution csem includes three base models random forest extreme gradient boosting tree lstm learning parameters model integrated training crossstacking crossstacked integrated training method enables secondlayer learner ensemble model make full use learning results base models training data thereby improving prediction performance model model predicted hourly ozone concentration r2 094 097 096 mild moderate severe pollution cases respectively mean absolute error mae 448 gm3 501 gm3 871 gm3 respectively model predicted ozone concentrations different nox vocs reduction scenarios results show 20 reduction vocs change nox study area 7528 cases achieved reduction 1573 cases got 200 gm3 addition comprehensive evaluation index prediction model proposed paper extended prediction model performance comparison analysis practical application machine learning feature selection crossstacked ensemble models jointly applied ozone realtime prediction emission reduction strategy analysis