Water Quality Assessment in the Lam Pa Thao Dam, Chaiyaphum, Thailand with K-Means Clustering Algorithm

Abstract

Water resource management is one of the biggest challenges that are being faced, such as a warming climate, arid land, and toxic chemicals in the water. It is essential to deal with water resource management urgently. In this article, researchers mainly focus on monitoring the water quality in the Lam Pa Thao dam, Chaiyaphum, Thailand. The farmer in that area directly affected by the water quality in the dam because they raise fish in floating fish cages. To prevent losses from fish farming, they should have the ability to monitor and control the factors that affect the water quality. As a result, the farmer can monitor the water quality and the monitor system can report to the farmer in time. In this case, to monitor the water quality, researchers designed the buoys, which is the internet of things device, to collect data from the Lam Pa Thao dam. researchers collected the water quality data from January - March 2021, including 13,608 instances. The five important parameters were obtained, including dissolved oxygen, temperature, pH, total dissolved solids, and electric conductivity. Due to the number of parameters, researchers decided not to apply dimension reduction. In these experiments, researchers proposed using K-means clustering algorithms to group the water data into appropriate clusters. For the K-Means algorithm, we calculated the silhouette coefficient to analyze the effectiveness of cluster separation. The best cluster that was grouped using the K-means algorithm achieved the silhouette score of 0.6839. Furthermore, researchers evaluated the K-means algorithm on Charles river and Fitzroy river datasets. It obtained the silhouette score of 0.5489 and 0.6589, respectively.