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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.

PurposeThis paper aims to propose a solution for recommending digital library services based on data mining techniques (clustering and predictive classification).Design/methodology/approachData mining techniques are used to recommend digital library services based on the user's profile and search history. First, similar users were clustered together, based on their profiles and search behavior. Then predictive classification for recommending appropriate services to them was used. It has been shown that users in the same cluster have a high probability of accepting similar services or their patterns.FindingsThe results indicate that k‐means clustering and Naive Bayes classification may be used to improve the accuracy of service recommendation. The overall accuracy is satisfying, while average accuracy depends on the specific service. The results were better for frequently occurring services.Research limitations/implicationsDatasets were used from the KOBSON digital library. Only clustering and predictive classification was applied. If the correlation between the service and the institution were higher, it would have better accuracy.Originality/valueThe paper applied different and efficient data mining techniques for clustering digital library users based on their profiles and their search behavior, i.e. users' interaction with library services, and obtain user patterns with respect to the library services they use. A digital library may apply this approach to offer appropriate services to new users more easily. The recommendations will be based on library items that similar users have already found useful.

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