Gerald Corzo

TL;DR: An extensive data-driven modeling experiment with six DDM techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbors is proposed and explained.

TL;DR: The results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies, and should be ignored as a potential modeling technique for hydrological applications.

TL;DR: In this paper, the authors explored the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow forecasting.

TL;DR: Models incorporating hydrological knowledge into the modelling process through the use of a modular architecture that takes into account the existence of various flow regimes showed to be more accurate than the traditional ANN-based models.

TL;DR: This paper explores the complementary use of data-driven models, e.g. artificial neural networks (ANN) to improve the flow simulation accuracy of a semi-distributed process-based model and concludes that the presented two schemes can improve the performance of process- based models in the context of flow forecasting.