TLT: recurrent fine-tuning transfer learning for water quality long-term prediction

The water quality long-term prediction is essential to water environment management decisions. In recent years, although water quality prediction methods based on deep learning have achieved excellent performance in short-term prediction, these methods are unsuitable for long-term prediction because the accumulation use of short-term prediction will easily introduce noise. Furthermore, The long-term prediction task requires a large amount of data to train the model to obtain accurate prediction results. For some monitoring stations with limited historical data, it is challenging to fully exploit the performance of deep learning models. To this end, we introduce a transfer learning framework into water quality prediction to improve the prediction performance in data-constrained scenarios. We propose a deep Transfer Learning based on Transformer (TLT) model to enable time dependency perception and facilitate long-term water quality prediction. In TLT, we innovatively introduce a recurrent fine-tuning transfer learning method, which can transfer the knowledge learned from source monitoring stations to the target station, while preventing the deep learning model from overfitting the source data during the pre-training phase. So, TLT can fully exert the performance of deep learning models with limited samples. We conduct experiments on data from 120 monitoring stations in major rivers and lakes in China to verify the effectiveness of TLT. The results show that TLT can effectively improve the long-term prediction accuracy of four water quality indicators (pH, DO, NH₃-N, and COD_Mn) from monitoring stations with limited samples.