Water Pollution Prevention and Control Project, 2023, 4(2); doi: 10.38007/WPPCP.2023.040203.
Univ Adelaide, Fac Hlth & Med Sci, Discipline Med, Adelaide, SA, Australia
Traditional water quality detection methods will consume a lot of manpower and material resources. However, remote sensing data and field water quality monitoring data are used to establish water quality remote sensing inversion model to achieve water quality remote sensing monitoring, which makes up for the shortcomings of traditional methods and can monitor the water quality environment comprehensively, quickly and dynamically. However, due to the impact of objective conditions, field water quality data is difficult to obtain in large quantities, so machine learning theory is used, A large number of easily obtained remote sensing image(RSI) data can effectively solve the problem of water quality monitoring. In this experiment, 50 polluted RSIs and 50 unpolluted RSIs are used to extract the gray distribution features of RSIs as the features of the training set. The parameters of GBDT model were optimized, and the accuracy of identifying polluted and unpolluted areas reached 98.67%. In addition, by comparing the performance of three machine learning algorithms, GBDT, DT and SVM, it is found that GBDT algorithm has the highest accuracy in RSI classification. The use of GBDT algorithm enables us to automatically monitor the pollution in RSIs without any marks, which realizes the automation of pollution monitoring.
Remote Sensing Image, Pollution Monitoring, Machine Learning Algorithm, Gray Distribution
Ilankoon Raymond. Machine Learning Algorithm for Background Analysis of Remote Sensing Image Pollution Monitoring. Water Pollution Prevention and Control Project (2023), Vol. 4, Issue 2: 18-25. https://doi.org/10.38007/WPPCP.2023.040203.
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