Transfer Learning for Assessing Heavy Metal Pollution in Seaports Sediments

TL;DR

This paper introduces a transfer learning-based deep learning model that accurately predicts the Pollution Load Index in seaport sediments, reducing labor and data collection challenges for environmental monitoring.

Contribution

It presents a novel transfer learning approach to assess heavy metal pollution, addressing data scarcity and standardization issues across different ports.

Findings

Significantly lower MAE and MAPE compared to baseline models

Model performance up to 100 times better than existing methods

Effective transfer of features across diverse port datasets

Abstract

Detecting heavy metal pollution in soils and seaports is vital for regional environmental monitoring. The Pollution Load Index (PLI), an international standard, is commonly used to assess heavy metal containment. However, the conventional PLI assessment involves laborious procedures and data analysis of sediment samples. To address this challenge, we propose a deep-learning-based model that simplifies the heavy metal assessment process. Our model tackles the issue of data scarcity in the water-sediment domain, which is traditionally plagued by challenges in data collection and varying standards across nations. By leveraging transfer learning, we develop an accurate quantitative assessment method for predicting PLI. Our approach allows the transfer of learned features across domains with different sets of features. We evaluate our model using data from six major ports in New South Wales, Australia: Port Yamba, Port Newcastle, Port Jackson, Port Botany, Port Kembla, and Port Eden. The results demonstrate significantly lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of approximately 0.5 and 0.03, respectively, compared to other models. Our model performance is up to 2 orders of magnitude than other baseline models. Our proposed model offers an innovative, accessible, and cost-effective approach to predicting water quality, benefiting marine life conservation, aquaculture, and industrial pollution monitoring.