Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava

Comparing various artificial neural network types for water temperature prediction in rivers

“…Streamwater temperature prediction approaches proposed in the past mainly included physically-based, temperature equilibrium concept-based or simple statistical models (Webb et al, 2008;Wehrly et al, 2009;Bustillo et al, 2014). In recent years various kinds of deterministic models (Caissie et al, 2007), data-driven approaches (St-Hilaire et al, 2012;Grbic et al, 2013;Cole et al, 2014) or artificial neural networks (ANNs) (Sahoo et al, 2006;Sivri et al, 2007;Chenard and Caissie, 2008;Sahoo et al, 2009;Daigle et al, 2009;Faruk, 2010;McKenna et al, 2010;Jeong et al, 2013;Napiorkowski et al, 2014;Piotrowski et al, 2014;Hadzima-Nyarko et al, 2014;Rabi et al, in press) have been applied to this task. In some studies (Sahoo et al, 2009;Bustillo et al, 2014) regression and ANN models are claimed to perform not worse than the more sophisticated empirical or heat budget-based models.…”

“…For example Sahoo et al (2006) compared regression, chaotic and multi-layer perceptron ANN models, Wehrly et al (2009) compared various statistical models, Cole et al (2014) compared three data-driven approaches with heat flux model and Bustillo et al (2014) verified the performance of various regression and temperature equilibrium-based models in the context of streamwater temperature prediction. However, although a large number of different types of neural networks have been developed so far, for the prediction of streamwater temperatures almost always the ''classical'' multi-layer perceptron ANNs (MLP) have been used (Sahoo et al, 2006;Sivri et al, 2007;Chenard and Caissie, 2008;Daigle et al, 2009;McKenna et al, 2010;Jeong et al, 2013;Piotrowski et al, 2014;Hadzima-Nyarko et al, 2014;Cole et al, 2014;Rabi et al, in press). Similar MLP networks were also applied for somehow related problem, the prediction of temperatures of stormwater runoff in urban watershed (He et al, 2011;Sabouri et al, 2013).…”

“…Similar MLP networks were also applied for somehow related problem, the prediction of temperatures of stormwater runoff in urban watershed (He et al, 2011;Sabouri et al, 2013). In a number of studies it is claimed that MLP outperforms various non-ANN-based regression approaches (Chenard and Caissie, 2008;Daigle et al, 2009;Faruk, 2010;Jeong et al, 2013;Rabi et al, in press); however, according to our knowledge, among other ANN types for streamwater temperature prediction only radial-basis function ANNs (RBF) were compared with MLP ones in just two papers (Sahoo et al, 2009;Hadzima-Nyarko et al, 2014). In both papers it was found that MLPs trained either by a kind of Genetic Algorithm (Sahoo et al, 2009) or Levenberg-Marquardt algorithm (Hadzima-Nyarko et al, 2014) outperform other modelling methods, including RBF neural networks.…”

Prediction of the Seasonal Changes of the Chloride Concentrations in Urban Water Reservoir

“…At first, artificial neural networks were used in economic sciences [16] and in meteorology [17]. Currently, more and more often used in prediction of changes in surface waters, demersal waters and hydrological changes [18][19][20][21][22][23][24][25][26][27][28].…”

A comparative study for water temperature modelling in a small basin, the Fourchue River, Quebec, Canada

“…Hence, they have been widely applied to estimate weekly/monthly water temperature (Mohseni et al, 1998;Benyahya et al, 2007bBenyahya et al, , 2008, daily water temperature (Caissie et al, 1998(Caissie et al, , 2001Pal'shin and Efremova, 2005;Ahmadi-Nedushan et al, 2007;Larnier et al, 2010) and hourly temperature (Mestekemper et al, 2010;Pike et al, 2013;Jeong et al, 2013). Furthermore, Artificial neural networks (ANN), which belong to the non-parametric category ( Chenard and Caissie, 2008;Sahoo et al, 2009;DeWeber and Wagner, 2013;Hadzima-Nyarko et al, 2014), k-nearest neighbors algorithm (k-NN), which is a nonlinear dynamic model (Benyahya et al, 2008;Nowak et al, 2010;Caldwell et al, 2013) and dynamic chaotic models (Sahoo et al, 2009) The objective of this study is therefore to investigate the simulation efficiency of a number of water temperature models using hydro-climatic variables in the context of regulated flows. The models were compared on the Fourchue River basin, a forested catchment on which there is a managed reservoir.…”