Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach

Abstract

Predicting water temperature (Tw) in tropical environments is crucial for ecosystem monitoring and the sustainable management of water resources. Highly accurate and reliable Tw forecasts are essential for the ecological management of rivers. This study evaluates the performance of machine learning-based predictive models in forecasting Tw in the Catu River. The models were trained using climatic and hydrological data collected from 2009 to 2016 and validated with real data from 2023. The evaluated models include backpropagation neural network (BPNN), Random Forest, Bidirectional LSTM (BiLSTM), Air2Stream, and NARX, employing nine input variables such as atmospheric pressure, air temperature, and water vapor concentration. The results show that the BiLSTM model achieved the best performance, with a root mean square error (RMSE) of 0.12°C and R2 = 0.98, followed by BPNN with an RMSE of 0.18°C and R2 = 0.91, and the Random Forest model, which obtained an NSE of 0.95. These models demonstrated a strong ability to predict Tw under both normal and extreme conditions, capturing the thermal dynamics of the Catu River with high precision during events involving minor thermal variations. Conversely, the NARX and Air2Stream models exhibited lower performance, proving more prone to errors under conditions of extreme variability. The findings of this study provide valuable scientific insights for river Tw prediction and the protection of aquatic ecosystems, with practical applications in water resource management in tropical regions.