AUTOMATIC WATER OBJECTS CLASSIFICATION AND NATURAL WATER QUALITY ASSESSMENT

Abstract

Assessment of water quality and classification of water object plays significant role in an environmental and ecology study. Water quality evaluation by hydrochemical parameters is fairly difficult and required a long period of time. Automatic expert system was created to solve this problem. Automatization of objects classification and quality assessment for humus zone based on Karelian water bodies research data are presented in this study. Automation algorithms of the surface water geochemical classification based on the principal chemical transactions was obtained during research. Classification based on implicit scaling data by classification parameter. Alkalinity, pH, huminity, Fecom and total phosphorous were chosen as the main classification parameters. For classification by alkalinity were used alkalinity and pH, for huminity classification were used coefficient of huminity – Hum? Color OD?C Mn and Fecom, for trophic state were used huminity class and total phosphorous concentration. The water objects distribution by huminity, alkalinity and trophic state was obtained and basic geochemical classes were picked out. Natural water quality was assessed as combination of geochemical classes. Results of research presented as maps and trends of geochemical classes and natural water quality distribution over the area of Republic of Karelia.