Open AccessArtificial Intelligence Methods for Detecting Water Pollution
Author(s) -
Evgeniya Pavlovna Zharikova,
J Yu Grigoriev,
Anna L. Grigorieva
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/988/2/022082
Subject(s) - pollution , environmental science , water pollution , environmental monitoring , water resources , computer science , thermal pollution , environmental pollution , surface water , remote sensing , environmental engineering , environmental protection , geography , ecology , biology
In the modern world, industrial development often negatively affects the environment, including the state of water bodies. Pollution of various types, from thermal to chemical (oil spills, industrial waste dumping and thermometric disturbances), have a detrimental effect on flora and fauna. Continuous monitoring of water areas allows timely detection of pollution. One of the tasks of analyzing the state of water resources is monitoring the water surface and monitoring the coastal zone. The aim of the study is to compare classical approaches based on the application of spectral characteristics and machine learning methods to the analysis of the state of water bodies. The studies show the disadvantages of classical methods of remote sensing in solving problems of autonomous monitoring, consisting in poor resistance to noise and the need for constant expert assessment. The paper presents solutions to the problem of detecting pollution of water bodies using machine learning methods.