Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong

The water quality measurement of marine water is a key research topic for environmental and ocean modelers in the past several decades. Marine water quality is mainly described by its chemical, physical and biological properties. According to the Environmental Protection Department (EPD), Hong Kong, the chemical and physical measurements are integrative parameters for water quality measurements. The existence of undesirable components in marine water degrades the water quality. The increase of natural and anthropogenic events in coastal regions poses the marine ecosystem in danger day by day. Therefore, the prediction of marine water quality indicators is essential. Turbidity is a key indicator of marine water quality, whose prediction is difficult due to its non-linear time series behavior. Therefore, the objective of the present study is to predict turbidity in the marine environment of Hong Kong using Machine Learning (ML) approach. The artificial neural network (ANN), support vector regression (SVR), and Long short-term memory recurrent neural network (LSTM-RNN) have been used as ML tools in the marine environment. The ML model prediction results were compared and the obtained results suggest that the LSTM-RNN model outperforms the ANN and SVR model results with an accuracy of 88.45%. The neural network-based approaches give better prediction accuracy over the SVR model in this study. Thus, it can be concluded that the neural network-based algorithm results can be used to monitor water quality parameters in the marine environment. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

The measurement of marine water quality is a crucial research topic for environmental and ocean modelers. This study aims to predict turbidity in the marine environment of Hong Kong using machine learning approaches. Different machine learning models were compared, and the results show that the LSTM-RNN model outperformed the others with an accuracy of 88.45%.