Deep learning ensembles of Long Short-Term Memory Networks to predict ammonia dynamics for aeration control in WWTPs: AdaBoost versus Bagging

To cite the provided dataset for building prediction models, please reference the following paper:
Shi H, Wei A, Zhu Y, Tang K, Hu H, Li N. Accurate and robust ammonia level forecasting of aeration tanks using long short-term memory ensembles: A comparative study of Adaboost and Bagging approaches. J Environ Manage. 2024;371:123173. doi: 10.1016/j.jenvman.2024.123173.

This data article provides the data set and all the codes for the study. The dataset is the ammonia nitrogen concentration of the aerobic tank in the wastewater treatment plant, which was recorded every 2 minutes from 04 July 2022 to 2010 July 2022, with a total of 5041 samples. The codes included the Adaboost and Bagging techniques, LSTM, CNN-LSTM and the seasonal decompose function.

若使用本数据集构建预测模型，请引用以下论文：
Shi H, Wei A, Zhu Y, Tang K, Hu H, Li N. 采用长短期记忆集成模型实现曝气池氨氮浓度精准鲁棒预测：Adaboost与Bagging方法对比研究. J Environ Manage. 2024;371:123173. doi: 10.1016/j.jenvman.2024.123173.

本数据论文提供了本研究的全部数据集与代码。该数据集为某污水处理厂曝气池的氨氮浓度数据，采集间隔为每2分钟一次，采集时段为2022年7月4日至2022年7月10日，共计5041条样本。所附代码涵盖Adaboost、Bagging技术、长短期记忆网络（LSTM）、卷积长短期记忆网络（CNN-LSTM）以及季节分解函数。