SATELLITE MONITORING OF WATER BODY POLLUTION ASSESSMENT BASED ON MACHINE LEARNING

Abstract

Satellite monitoring is a reliable tool for obtaining operational information about the state of water bodies and their changes with a certain periodicity, thanks to the use of a large set of heterogeneous data. And the availability of archival data becomes a significant advantage, opening up the possibility of comparing the obtained results and analyzing factors affecting the state of water bodies and the environment. The paper describes the main methods and datasets that can be used to apply remote monitoring methods based on machine learning. The use of medium spatial resolution data is proposed as the main data set. Examples of the application of the remote sensing approach to the assessment of water pollution in large areas are analyzed. Possible machine learning methods for solving an applied problem are presented, the new artificial intelligence (AI) approach of convolutional long-short-term memory (ConvLSTM) model significantly outperforms classical methods.