- Seona Song, Seongjin Bang, Soyoung Cho, Hyungseok Han, Sangmin Lee, “Attentive Multi-Task Prediction of Atmospheric Particulate Matter: Effect of the COVID-19 Pandemic”, IEEE Access, 10, 2022, 10176
- Asif Iqbal Middya, Sarbani Roy, “Pollutant specific optimal deep learning and statistical model building for air quality forecasting”, Environmental Pollution, 301, 2022, 118972
- Abdellatif Bekkar, Badr Hssina, Samira Douzi, Khadija Douzi, “Air-pollution prediction in smart city, deep learning approach”, J Big Data, 8, no. 1, 2021, 161
- Yanan Lu, Kun Li, “Multistation collaborative prediction of air pollutants based on the CNN-BiLSTM model”, Environ Sci Pollut Res, 30, no. 40, 2023, 92417
- Yi-Chung Chen, Tsu-Chiang Lei, Shun Yao, Hsin-Ping Wang, “PM2.5 Prediction Model Based on Combinational Hammerstein Recurrent Neural Networks”, Mathematics, 8, no. 12, 2020, 2178
- Bozhi Yao, Guang Ling, Feng Liu, Ming-Feng Ge, “Multi-source variational mode transfer learning for enhanced PM2.5 concentration forecasting at data-limited monitoring stations”, Expert Systems with Applications, 238, 2024, 121714
- Huda Febrianto Nurrohman, Dian Candra Rini Novitasari, Fajar Setiawan, Rochimah, Amal Taufiq, Abdulloh Hamid, 2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 2022, 191
- Hao Cai, Chen Zhang, Jianlong Xu, Fei Wang, Lianghong Xiao, Shanxing Huang, Yufeng Zhang, “Water Quality Prediction Based on the KF-LSTM Encoder-Decoder Network: A Case Study with Missing Data Collection”, Water, 15, no. 14, 2023, 2542
- Siting Chen, Yi Song, Yunchuan Li, Hongjun Wang, 2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2023, 204
- Hongfeng Xu, Lei Chai, Zhiming Luo, Shaozi Li, “Stock movement prediction via gated recurrent unit network based on reinforcement learning with incorporated attention mechanisms”, Neurocomputing, 467, 2022, 214