Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models

By G. Sahoo, S. Schladow, J. Reuter. 2009

172 citations recorded by the source corpus.

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A hybrid model for river water temperature as a function of air temperature and discharge (2015, methodology): “Thus, the model can be seen as a hybrid tool that is physically based, but takes advantage from the data to inform its structure, in a way that resembles neural networks (e.g., Sahoo et al 2009).”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, methodology): “…methods can be divided into local optimization algorithms, such as the classic back-propagation method developed by Rumelhart et al. (1986) and Newton’s methods, among others (Maier and Dandy, 1999, 2000); and global optimization algorithms, such as genetic algorithms (Sahoo et al., 2009).”
Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods (2018, methodology): “they are less effective to model complex features such as the catchments (Sahoo et al. 2009).”
A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, methodology): “u GA Introducing creep mutation in a small population [140]”