An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level

Journal of Material Cycles and Waste Management - This paper presents a development of general regression neural network (a form of artificial neural network) models for the prediction of annual...     

The forecasting of municipal waste generation using artificial neural networks and sustainability indicators

The feasibility of modeling municipal waste generation (MWG) for countries at different levels of development using artificial neural networks (ANN) and selected generic indicators of sustainability was investigated. The main goals of this research were to develop ANN-based models for predicting MWG, to overcome the problem of incomplete MWG data, which is notable in developing countries, and to provide a new method for the planning of municipal solid waste management systems as well as for the simulation of various other scenarios. Data from 26 European countries was used in this study as training, test and validation datasets for the developing of ANN models. Since this kind of modeling is particularly important for developing countries where MWG data is missing or incomplete, emphasis was placed on modeling of MWG for Bulgaria and Serbia. Based on a comparison of actual MWG data with predictions given by the model, we show that ANNs can be applied successfully to modeling and forecasting MWG on a national scale. Moreover, the scope for possible application of the model is broad, since it uses generic indicators of sustainability such as gross domestic product, domestic material consumption and resource productivity, and performs well for countries with highly diversified levels of economic development, industrial structure, productivity and output.     

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study

The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN > GRNN > BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than ±10 %. In case of the MLR, only 55 % of predictions were within the error of less than ±10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.