Modelling Municipal Rating by Cluster Analysis and Neural Networks

Municipal Creditworthiness Modelling by Clustering Methods

The paper presents the design of municipal creditworthiness parameters. Municipal creditworthiness modelling is realized by means of unsupervised methods, namely neural networks, cluster and fuzzy cluster analysis methods. Analysis of the gained results is based on clustering quality evaluation and further, on the comparison to results gained by the designed hierarchical structure of fuzzy infe...

Monthly runoff forecasting by means of artificial neural networks (ANNs)

Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...

Municipal Creditworthiness Modelling by Neural Networks

The paper presents the design of municipal creditworthiness parameters. Further, the design of model for municipal creditworthiness classification is presented. The realized data pre-processing makes the suitable economic interpretation of results possible. Municipalities are assigned to clusters by unsupervised methods. The combination of Kohonen’s self-organizing feature maps and K-means algo...

Municipal credit rating modelling by neural networks

Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding

In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under in...