| Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions Y Bai, Y Li, X Wang, J Xie, C Li Atmospheric pollution research 7 (3), 557-566, 2016 | 346 | 2016 |
| Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models Y Bai, Z Chen, J Xie, C Li Journal of hydrology 532, 193-206, 2016 | 223 | 2016 |
| An ensemble long short-term memory neural network for hourly PM2. 5 concentration forecasting Y Bai, B Zeng, C Li, J Zhang Chemosphere 222, 286-294, 2019 | 179 | 2019 |
| Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition C Li, Y Tao, W Ao, S Yang, Y Bai Energy 165, 1220-1227, 2018 | 170 | 2018 |
| Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator B Zeng, H Duan, Y Bai, W Meng Energy 151, 238-249, 2018 | 154 | 2018 |
| Development of an optimization method for the GM (1, N) model B Zeng, C Luo, S Liu, Y Bai, C Li Engineering Applications of Artificial Intelligence 55, 353-362, 2016 | 144 | 2016 |
| Vehicular contribution of PAHs in size dependent road dust: A source apportionment by PCA-MLR, PMF, and Unmix receptor models J Zhang, R Li, X Zhang, Y Bai, P Cao, P Hua Science of the Total Environment 649, 1314-1322, 2019 | 124 | 2019 |
| A multi-scale relevance vector regression approach for daily urban water demand forecasting Y Bai, P Wang, C Li, J Xie, Y Wang Journal of Hydrology 517, 236-245, 2014 | 123 | 2014 |
| A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction Y Bai, Z Sun, B Zeng, J Long, L Li, JV de Oliveira, C Li Journal of Intelligent Manufacturing 30, 2245-2256, 2019 | 121 | 2019 |
| A multi-pattern deep fusion model for short-term bus passenger flow forecasting Y Bai, Z Sun, B Zeng, J Deng, C Li Applied Soft Computing 58, 669-680, 2017 | 119 | 2017 |
| Deep fuzzy echo state networks for machinery fault diagnosis S Zhang, Z Sun, M Wang, J Long, Y Bai, C Li IEEE Transactions on Fuzzy Systems 28 (7), 1205-1218, 2019 | 106 | 2019 |
| Hourly PM2. 5 concentration forecast using stacked autoencoder model with emphasis on seasonality Y Bai, Y Li, B Zeng, C Li, J Zhang Journal of Cleaner Production 224, 739-750, 2019 | 106 | 2019 |
| Daily natural gas consumption forecasting based on a structure-calibrated support vector regression approach Y Bai, C Li Energy and Buildings 127, 571-579, 2016 | 102 | 2016 |
| Multiple Random Forests Modelling for Urban Water Consumption Forecasting G Chen, T Long, J Xiong, Y Bai Water Resources Management 31 (15), 4715-4729, 2017 | 101 | 2017 |
| A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledge Y Bai, J Xie, D Wang, W Zhang, C Li Computers & Industrial Engineering 155, 107227, 2021 | 85 | 2021 |
| A novel sparse echo autoencoder network for data-driven fault diagnosis of delta 3-D printers J Long, Z Sun, C Li, Y Hong, Y Bai, S Zhang IEEE Transactions on Instrumentation and Measurement 69 (3), 683-692, 2019 | 81 | 2019 |
| Manufacturing quality prediction using intelligent learning approaches: A comparative study Y Bai, Z Sun, J Deng, L Li, J Long, C Li Sustainability 10 (1), 85, 2017 | 77 | 2017 |
| Hydrological modelling of karst catchment using lumped conceptual and data mining models C Sezen, N Bezak, Y Bai, M Šraj Journal of Hydrology 576, 98-110, 2019 | 73 | 2019 |
| Intelligent fault diagnosis of delta 3D printers using attitude sensors based on support vector machines K He, Z Yang, Y Bai, J Long, C Li Sensors 18 (4), 1298, 2018 | 71 | 2018 |
| Deep feature learning architectures for daily reservoir inflow forecasting C Li, Y Bai, B Zeng Water Resources Management 30, 5145-5161, 2016 | 62 | 2016 |
Chuan LIProfessor, Chongqing Technology and Business UniversityAdresse e-mail validée de ctbu.edu.cn
