Transfer Learning for Assessing Heavy Metal Pollution in Seaports Sediments
This provides a more accessible and cost-effective method for environmental monitoring in seaports, benefiting marine life conservation, aquaculture, and industrial pollution monitoring, but it is incremental as it applies existing transfer learning techniques to a specific domain.
The paper tackles the problem of assessing heavy metal pollution in seaports sediments by proposing a deep-learning-based model using transfer learning to predict the Pollution Load Index (PLI), achieving significantly lower errors with MAE of approximately 0.5 and MAPE of 0.03 compared to other models.
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.