Abstract
Comprehensive assessment of River Water Quality (RWQ) is essential for the sustainable management of water resources. This study aimed to develop an innovative integrated index for river water quality and ecological risk (RWQI) and evaluate its applicability in the Gharehsoo River. The proposed RWQI, for the first time, provided a unified framework for the simultaneous assessment of overall water quality (based on 16 physicochemical and microbial parameters) and the risk posed by heavy metals. In this regard, the first step involved analyzing water quality for various uses (drinking, recreation, wildlife, industry, and agriculture) using the Enhanced River Pollution Index (ERPI) across seven stations over two seasons. Subsequently, the performance of Multiple Linear Regression (MLR) and Support Vector Regression (SVR) models in predicting the values of these indices was examined and compared. Finally, the combined RWQI was calculated based on the same dataset, and uncertainty analysis of the models was performed using the Markov Chain Monte Carlo (MCMC) method. Descriptive statistical analysis (based on ERPI-DD calculations for 14 samples from 7 stations across two seasons) revealed that the water status of the Gharehsoo River is critical for drinking purposes, with 64.28% of the samples classified as "unsuitable" according to the DD index (drinking without treatment). In contrast, water quality for wildlife and fisheries (WF) was evaluated as "good to excellent" for 100% of the samples based on Central Pollution Control Board (CPCB) standards and the ERPI-WF model. The SVR models with polynomial and Radial Basis Function (RBF) kernels demonstrated very high predictive accuracy in estimating the ERPI values (as the reference criterion) for various water use categories, with coefficients of determination (R²) approaching 1 in many cases. Furthermore, the RWQI effectively identified critical points; for example, the Samian station was determined to be a critical point for recreational use (swimming). Uncertainty analysis also revealed that the RWQI model exhibits higher uncertainty compared to other models, primarily due to the inherent complexity of the model and the incorporation of combined pollutant risks. Consequently, the developed RWQI index proved to be a powerful tool for integrated monitoring of water quality and ecological risk assessment in river management, enabling the adoption of more targeted protection strategies.