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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.