Unlocking Complex Water Quality Dynamics: Principal Component Analysis and Multivariate Adaptive Regression Splines Integration for Predicting Water Quality Index in the Kızılırmak River

Abstract

In the field of environmental sustainability, the preservation of water resources and the maintenance of water quality are of utmost importance. The aim of this study is to develop a predictive model for assessing the water quality of the K & imath;z & imath;l & imath;rmak river by integrating Principal Component Analysis (PCA) and Multivariate Adaptive Regression Splines (MARS) methodologies. To assess water quality, surface water samples obtained from six distinct locations during the 2022-2023 period were analyzed with respect to seventeen physicochemical parameters. The first stage of the present study was the determination of the most informative variables in the water quality data set using the dimensionality reduction method PCA. In the second phase, a predictive model was developed using the MARS algorithm based on the principal components derived from the PCA-reduced dataset. The MARS algorithm was proposed to predict Water Quality Index (WQI) values using this reduced dataset. A coefficient of determination (R2) value of 0.997 was achieved for predicting the WQI in the study area. According to the results of this study, the MARS model developed using PCA demonstrated high precision and performance in estimating the WQI. This methodological framework clarified the interactions between parameters in water quality assessment studies, allowing for a comprehensive analysis of their overall effects on WQI.