<![CDATA[Nitrate (NO₃⁻) concentration is one of the key indicators for evaluating river water quality, particularly in tropical regions with relatively high temperatures, which accelerate the nitrification process in the nitrogen cycle. Increasing human activities, such as residential development, agricultural practices, livestock farming, and aquaculture near rivers, can increase the nitrogen nutrient load, posing a risk of degrading water quality. Traditional methods such as the Pollutant Index (PI) and Water Quality Index (WQI) only reflect current conditions and cannot accurately predict changes in water quality. Therefore, this study aims to analyze the effectiveness of the Radial Basis Function Neural Network (RBFNN) model in predicting nitrate concentrations based on water quality parameters in rivers in tropical regions. This study was conducted by analyzing 164 publications providing data on water physico-chemical parameters, such as temperature, pH, TDS, TSS, DO, BOD, COD, nitrite, ammonia, and electrical conductivity. The obtained data were then processed through data cleaning, logarithmic transformation, feature engineering, and model training using an RBFNN combined with Gradient Boosting and Random Forest methods via an ensemble approach. Model evaluation was performed using the coefficient of determination (R²). The results of the study indicate that this model exhibits excellent predictive capability, with an R² value of 0.9907 for residential riverbanks and 0.9953 for agro-aquatic riverbanks, outperforming conventional ANN models in similar studies in Indonesia, which generally yield R² values in the range of 0.90–0.97, and comparable to the best RBFNN models ever reported in the international literature. Sensitivity analysis indicates that the parameter most influential on nitrate concentration is nitrite (40.25%), followed by electrical conductivity (19.80%) and TDS (11.10%). These results indicate that the RBFNN is effective in modeling nonlinear relationships among water quality parameters and has the potential to be developed as a core component in an early warning system for nitrate pollution, a decision support system for water resource managers, and a more efficient and adaptive data-driven water quality monitoring instrument to support the sustainable management of tropical rivers. Keywords: radial basis function, nitrate, tropical rivers, water quality, neural network]]>
