Dispersion Coefficient Prediction Using Empirical Models and ANNs

Abstract

The concentration of a conservative pollutant is changed along a river, as a result of transport processes. The dispersion coefficient is the most important parameter of mass transport in rivers. In this paper, the dispersion coefficient was estimated in a section of Axios River, with the analytical procedure of Fischer method, under different hydrological and hydrodynamic conditions. An empirical equation and a model of artificial neural networks (ANNs) for dispersion coefficient were proposed, based on the data estimated with analytical Fischer method. The dispersion coefficients predicted by the proposed models and other empirical equations reported in earlier studies were compared to the coefficients obtained in the present study. The most accurate equations for dispersion coefficient were used to predict the concentration of conservative toxic pollutants released instantaneously in Axios River upstream of the border of Greece-Former Yugoslav Republic of Macedonia (FYROM).

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

“…However, such a decomposition could be found in the literature [l8, [49][50][51]. Table 7 Alignment of ANN models as coefficients of discrepancy of results …”

“…In this study, the results obtained using ANN were compared and evaluated [44][45][46][47][48][49][50][51]. It is important to define average forecast error by (MSE, RMSE), model fit (R 2 ), and prognostic error distribution when creating forecast models.…”

“…It is important to define average forecast error by (MSE, RMSE), model fit (R 2 ), and prognostic error distribution when creating forecast models. The robustness of the model was tested by determining the mean absolute error of the prediction (MAPE) -defining the percent precision of the model according to the formula [44][45][46][47][48][49][50][51] …”

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

“…Typical applications include the following, among many others: predicting the dispersion coefficient (D) in a river ecosystem (Antonopoulos et al 2015); modelling the permeability losses in permeable reactive barriers (Santisukkasaem et al 2015); estimating the reference evapotranspiration (ET 0 ) in India (Adamala et al 2015); calculating the dynamic coefficient in porous media ; predicting Indian monsoon rainfall (Azad et al 2015); modeling of arsenic (III) removal (Mandal et al 2015); predicting effluent biochemical oxygen demand (BOD) in a wastewater treatment plant (Heddam et al 2016); modeling Secchi disk depth (SD) in river (Heddam 2016a); and predicting phycocyanin (PC) pigment concentration in river (Heddam 2016b). Unsurprisingly, regarding the high capabilities of ANNs in developing environmental models, they have rapidly gained much popularity.…”

Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA

“…Artificial intelligence (AI) techniques have been frequently applied in environmental modelling. Some of these applications include, among others, the following: prediction of reservoir permeability from porosity measurements (Handhal 2016); predictive modeling of discharge in compound open channel ; automatic inversion tool for geoelectrical resistivity (Raj et al 2015); forecasting monthly groundwater level ; predicting the dispersion coefficient (D) in a river ecosystem (Antonopoulos et al 2015); modelling the permeability losses in permeable reactive barriers (Santisukkasaem et al 2015); estimating the reference evapotranspiration (ET 0 ) (Adamala et al 2015); calculating the dynamic coefficient in porous media (Das et al 2015); predicting Indian monsoon rainfall (Azad et al 2015), and modeling of arsenic (III) removal (Mandal et al 2015). Although RBFNN has been applied for modelling DO concentration, to the best of our knowledge, there have been no studies done on the application of RBFNN for forecasting DO in rivers; hence the present study aims to investigate the capabilities of the RBFNN in comparison to the standard MLPNN for simultaneous modelling and forecasting of hourly DO concentration.…”