• Priyanka Talwar
• Swapan Shakhari
• Dr. Indrajit Banerjee

Water quality is the major issue of today’s world needs to be monitored on regular basis. The water quality evaluation can be solved by the machine learning technique where preprocessing is done by clustering methodology and further according to the users’ requirement. Clustering is an art of producing natural groups to identify homogeneous data. Clustering helps in labeling (without any prior knowledge of labels) the new data. It extracts the values from unstructured data and converts to valuable data. Consumable water needs to be clean and pure. The main idea of this work is to detect the water quality of supplied water by the Municipal Corporation. And also detect the outliers for a critical decision-making process to enhance the quality of supply water by taking corrective measures. The key parameters which are used to measure the quality of water are water temperature, pH (pouvoir hydrogène), tds (total dissolved solids). The model proposed in this study is structured using K-Means technique with Davies Bouldin Index applied on the data samples being collected from different sources and at different instances and places. This resulted in 6 different clusters and their centroids. Our simulation results also establish that the proposed work is outperformed over the existing similar algorithm.

Clustering, Davies Bouldin Index, machine learning and water quality.

"Exploration and Visualization of Municipal Supply Water Quality using Advanced Clustering Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.378-383, May - 2019, Available :http://www.jetir.org/papers/JETIRCN06072.pdf

"Exploration and Visualization of Municipal Supply Water Quality using Advanced Clustering Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp378-383, May - 2019, Available at : http://www.jetir.org/papers/JETIRCN06072.pdf