A Hybrid Machine Learning and Stochastic Modeling Framework for Probabilistic Reliability Analysis of Kızılırmak River Water Quality

ABSTRACT In order to enhance the efficiency of water usage intended for drinking or utility purposes, the determination of water quality has assumed greater significance. In this study, water samples were taken from a region of the Kızılırmak River over the course of 1 year and analyzed. A probabilistic water quality assessment was then made, taking uncertainties into account, using artificial neural networks (ANN) and Monte Carlo simulation (MCS) methods. The Weighted Arithmetic Water Quality Index method was employed. Analyses of dissolved oxygen (DO), pH, temperature, turbidity, chloride, and sulfate were conducted on samples collected between the years 2023 and 2024. The independent variables (temperature, sulfate, and chloride) were generated, and the dependent variables (pH, turbidity, and DO) were estimated with ANN. The R value and the root mean square error (RMSE) of ANN models are used to evaluate the effectiveness of the model by assessing both the accuracy and the margin of error. The findings of the sensitivity analysis demonstrated that the parameters of DO, pH, and turbidity exerted a significant influence on the quality of water in the Kızılırmak River. A methodology was implemented in which three distinct ANN prediction models function collectively. The study yielded reliability levels corresponding to different WQI categories, namely 10% for “excellent” quality, 29% for “good” quality, 59% for “poor” quality, and 86% for “very poor” quality. This indicates that the ANN and MCS models are effective estimation tools for determining the water quality of the Kızılırmak River.