Change Detection Analytics on Water Contamination using Decision Tree based Classification - Shanmugavadivu, P; Kavitha, P; Dhamodharan, S (International Journal of Computer Sciences and Engineering, 2025)
The exponential growth as well as the availability of data has triggered the development of data analytics tools to harness the power of information hidden in data. These advancements have grabbed the attention of the researchers across the globe. The research frontiers of data analytics is expanding so rapidly, covering many fields including business intelligence, finance, market analysis, science, environmental studies, resource management, weather forecasting and outlier analysis. This paper describes the design of a decision-tree based classification technique to assess the degree of water contamination, based on the statistics of Government of India on water contamination, for six years from 2012-13 to 2017-18 (www.data.gov.in and indiawaters.gov.in). This statistics portrays the quality of water, based on the presence of Iron, Arsenic, Fluoride, Nitrate and Salinity. The proposed classification primarily classifies the pattern of contamination of each State in the India during specified period, in four point scale. Secondly, it classifies the quality of water into either potable or non-potable. This classification finds place in water quality assessment at regional and national level