Assessing the Performance of Deep Learning Algorithms With and Without Transfer Learning in Similar or Identical Wastewater Treatment Processes

Abstract

This study assessed the feasibility of transfer learning from one wastewater treatment process to another using two popular deep learning algorithms. Specifically, convolutional neural network (CNN) and long short-term memory (LSTM), which consisted of four and three hidden layers, respectively, were used as benchmark algorithms for transfer learning. Input data for both deep learning and transfer learning were provided from two wastewater treatment plants with identical treatment trains in series (located in Jinju and Cheongju City) over the five-year period from 2018 to 2022. Performance evaluation was also done not only against two deep learning algorithms but also against those adopting two transfer learning strategies, one for freezing all hidden layers developed from the pre-trained model and the other for training the last hidden layer only among multiple ones, with respect to Mean Squared Error (MSE). We found that the performance of both CNN and LSTM was relatively comparative regardless of dependent variables, discharge and biochemical oxygen demand (BOD), whereas the prediction accuracy of both algorithms was slightly higher for discharge than for BOD due to its low variability. When transfer learning which froze all hidden layers of the existing model was applied to two benchmark algorithms, the predictive performance of both algorithms was found to slightly improved only for discharge. Also, there was no measurable variation in the prediction accuracy of benchmark algorithms using the other transfer learning approach. Potential applications of transfer learning include the rapid reuse of the existing models (developed from source domains) for target domains which are hard to develop new prediction models due to the lack of data in deep learning.