Multilayer perceptron model for predicting conservative solute transport in streams and rivers

My river transport research connects standardized experimental data with machine learning. This article focuses on a multilayer perceptron model for conservative solute transport in streams and rivers.

At UNM, I contributed to the development and validation of a Python machine learning framework across sites and hydrologic conditions as part of NSF project #2142691.

Citation

Rodríguez, L., Estrada, T., Tartakovsky, A., Carroll, K. C., Ginn, T., & González-Pinzón, R. (2026). Multilayer perceptron model for predicting conservative solute transport in streams and rivers. Environmental Modelling & Software, 107011.

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