Investigation of water quality using transfer learning, phased LSTM and correntropy loss

Abstract

This paper aims to develop a robust dissolved oxygen (DO) prediction model of water quality to support the Hybrid Aerial Underwater Robotics System (HAUCS) project. Many challenges arise in developing such a model using the fish farm data collected, such as a small dataset containing missing data and noisy measurements taken in an irregular interval. An attempt to deal with these issues to obtain a robust prediction is discussed. Machine learning techniques, such as Long Short-Term Memory (LSTM) and Phased LSTM (PLSTM), are presented and motivated for dealing with the problem. The performances of LSTM and PLSTM against a larger and less problematic water quality dataset are first investigated. The attempts to transfer the knowledge of the models trained on this large dataset for fish farm DO data prediction through Transfer Learning are then reported. To mitigate the noisy measurement data, a loss function which can better deal with Gaussian noise: the correntropy loss is adopted. The long-range prediction experimental results using this Transfer Learning technique and the correntropy loss function are presented.