Water Quality Assessment in a Watershed in Cusco, Peru using the Grey Clustering Method
Alexi Delgado1, Acuña M.2, Justano N.3, Llanos E., Puma I.4

1Alexi Delgado*, Department of Engineering, Mining Engineering Section, Pontificia Universidad Católica del Perú, Lima, Peru.
2Acuña M., Justano N., Llanos E., Environmental Engineering Faculty, Universidad Nacional de Ingeniería, Lima, Peru.
3Puma I., EngineeringProgram, Universidad de Ciencias y Humanidades, Lima, Peru.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 5093-5098  | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3606129219/2019©BEIESP | DOI: 10.35940/ijeat.B3606.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Water quality assessment is a current issue of increasing concern in many countries around the world for reasons such as population health, national economic development and the environmental quality of ecosystems. At this juncture, the Grey Clustering method is used to assess water quality at discharge points, from the beginning to the end of the environmental monitoring process in the area of influence of the Anabi mining unit in the Chonta and Milos micro-watershed. The parameters evaluated were pH, dissolved oxygen, total suspended solids (TSS), iron and manganese. The results obtained through the Grey Clustering methodology showed a monitoring point with contamination from a treated water discharge. On the other hand, in order to obtain greater efficiency in the evaluation of water quality, national standard DS 004-2017-Minam (Water Quality Standards) and international standards were used through the PRATI index. Through the results obtained it was observed that (by means of the Prati index ) there is a better classification of the water quality in each point, therefore this research becomes an important tool for future studies to consider the Prati index for greater reliability of results.
Keywords: Grey Clustering, Water parameters, Water quality.