A Comparative Analysis of Machine Learning Methods for Algal Bloom Detection Using Remote Sensing Images
C. H. Yang, Zhenyu Tan, Yimin Li, Ming Shen, Hongtao Duan
Abstract
Algal blooms are a major environmental challenge for lakes and reservoirs and pose severe threats to water on both aquatic and human health. Conventional algorithms used for al-gal bloom detection based on remote sensing reflectance proved to be effective in some lakes. However, it is still difficult to obtain high accuracy for multiple lakes using single-threshold-based de-tection. Currently, machine learning (ML) algorithms have been applied to pinpoint algal bloom locations with excellent results, but the ability of different ML models to be applied in different lakes is still unknown. This paper presents the performance of al-gal bloom detection with commonly used ML algorithms in Chi-nese eutrophic inland lakes based on Sentinel-2 images. A series of comprehensive tests for accuracy, stability, and robustness were designed for four ML models, including random forest (RF), extreme gradient boosting, artificial neural network, and support vector machine, which were tested in Lake Taihu, Lake Chaohu, and Lake Dianchi. In addition, the index-based methods, includ-ing floating algae index and adjusted floating algae index, were also calculated for comparison with ML methods. The results showed that RF model outperformed other ML models. The com-parison results between the RF model and algal indices revealed that the overall accuracy of RF remained above 0.90. Even with a single lake dataset used as training samples, the RF still main-tained a fairly high accuracy of 0.88 for other lakes. To summa-rize, four ML models demonstrate promising potential for algal bloom detection across different lakes and provide a practical ref-erence for further applications.