Utilization of Machine Learning to Predict the Correlation between Color of River Water and Other Water Quality Characters Ikhwanussafa Sadidan, Gina Lova Sari, Edmund Ucok Armin, Fakhri Ikhwanul Alifin, Amalia Rizka Sugiarto
Environmental Engineering Study Program, Engineering Faculty, Universitas Singaperbangsa Karawang, West Java, Indonesia
Abstract
This study using Artificial Intelligence investigates the intricate relationship between water color and key water quality parameters, such as DO, BOD, COD, TSS, and Fe concentrations. The primary objective is to establish a predictive model employing SVR analysis and DTR to discern the correlation patterns among these parameters. The purpose of this study is to predict and analyze the the correlation between key water quality parameters with the water color. These models are constructed by scrutinizing the intricate associations between water color and the aforementioned water quality parameters using machine learning. Total Dissolved Solid and pH are two parameters that show a very high correlation with water color. Both show figures of 0.95 and 0.93. The results of this study can be implemented by various institutions such as educational institutions, environmental services, or consultants who want to make predictions and modeling of water quality, especially on color parameters.
Keywords: Machine Learning, Water Quality, Regression, Water Color
