Monitoring Sea Water Pollution from Satellite data

Abstract

This study is motivated by the Ocean Decade declared by the United Nations. We employed machine learning techniques to detect and delineate areas of pollution in the coastal zone of Great Britain, utilizing pollution reports from the Department for Environment Food And Rural Affairs (DEFRA) and the ocean monitoring datasets from the European Space Agency (ESA). In this study, feature engineering was performed on chlorophyll concentration data. Two datasets were constructed: one with statistical metrics (mean, median, standard deviation, and percentiles) as features, and another with individual cells of the chlorophyll concentration matrix as features, utilizing different matrix n sizes, where n is from 3 to 11, where each element or pixel of the matrix represented a 1km × 1km area. Logistic regression, decision trees, random forest classifier, gradient boosting classifier, and LeNet models were applied. Hyper parameter tuning was conducted to optimize the performance of each model. Among the models, the gradient boosting classifier achieved the highest accuracy of 95.21%. Additionally, the F1 score was determined to be 0.2445, the ROC AUC was 0.7659, and the precision-recall AUC (PR-AUC) was found to be 0.1821. Detecting and delineating areas of pollution can greatly assist cleaning services in efficiently carrying out their job, resulting in improved remediation and restoration efforts. The identification of pollution areas holds significant implications for the fishing industry, as it enables informed decision-making regarding fishing practices and resource management, ensuring the sustainability and viability of the sector. Moreover, the accurate detection and delineation of pollution areas have the potential to generate substantial economic, social, and environmental benefits by facilitating targeted interventions, protecting ecosystems, preserving marine resources, and fostering a healthier and more resilient environment. The findings of this study provide valuable insights into the efficacy of classification approaches in identifying and mapping pollution sites in coastal regions using pollution reports from DEFRA.