Artificial neural network approach for forecasting nitrogen oxides concentrations

This paper presents the application of feed-forward multilayer perceptron networks to forecast hourly nitrogen oxides levels 24 hours in advance. Input data were meteorological variables, average hourly traffic and nitrogen oxides hourly levels. The introduction of four periodic components (sine and cosine terms for the daily and weekly cycles) was analyzed in order to improve the models prediction power. The data were measured during three years at monitoring stations in Valencia (Spain) in two locations with high traffic density. The models evaluation criteria were the mean absolute error, the root mean square error, the mean absolute percentage error, and the correlation coefficient between observations and predictions. Comparisons of multilayer perceptron-based models proved that the insertion of the four additional seasonal input variables improved the ability of obtaining more accurate predictions, which emphasizes the importance of taking into account the seasonal character of nitrogen oxides. When using seasonal components as predictors, the root mean square error (RMSE) improves from 20.29 to 19.35 when predicting nitrogen dioxide, and from 45.07 to 42.37 when forecasting nitric oxides if the model includes seasonal components At one study location. At the other location the RMSE changes from 23.76 to 23.05 when predicting nitrogen dioxide and from 33.94 to 33.10 for the other pollutant s forecasts. Neural networks did not require very exhaustive information about air pollutants, reaction mechanisms, meteorological parameters or traffic characteristics, and they had the ability of allowing nonlinear and complex relationships between very different predictor variables in an urban environment.Capilla Roma, CA. (2015). Artificial neural network approach for forecasting nitrogen oxides concentrations. Environmental Engineering Science. 32(9):781-788. doi:10.1089/ees.2014.0556S78178832

Statistical methods for characterizing diversity of microbial communities by analysis of terminal restriction fragment length polymorphisms of 16S rRNA genes

The analysis of terminal restriction fragment length polymorphisms (T-RFLP) of 16S rRNA genes has proven to be a facile means to compare microbial communities and presumptively identify abundant members. The method provides data that can be used to compare different communities based on similarity or distance measures. Once communities have been clustered into groups, clone libraries can be prepared from sample(s) that are representative of each group in order to determine the phylogeny of the numerically abundant populations in a community. In this paper methods are introduced for the statistical analysis of T-RFLP data that include objective methods for (i) determining a baseline so that 'true' peaks in electropherograms can be identified; (ii) a means to compare electropherograms and bin fragments of similar size; (iii) clustering algorithms that can be used to identify communities that are similar to one another; and (iv) a means to select samples that are representative of a cluster that can be used to construct 16S rRNA gene clone libraries. The methods for data analysis were tested using simulated data with assumptions and parameters that corresponded to actual data. The simulation results demonstrated the usefulness of these methods in their ability to recover the true microbial community structure generated under the assumptions made. Software for implementing these methods is available at http://www.ibest.uidaho.edu/tools/trflp_stats/index.php.Zaid Abdo, Ursel M.E. Schüette, Stephen J. Bent, Christopher J. Williams, Larry J. Forney and Paul Joyc

A survey of the challenges and pitfalls of cluster analysis application in market segmentation

Tuma MN, Decker R, Scholz S. A survey of the challenges and pitfalls of cluster analysis application in market segmentation. International Journal of Market Research. 2011;53(3):391-414.Market segmentation is a widely accepted concept in marketing research and planning. Although cluster analysis has been extensively applied to segment markets in the last SO years, the ways in which the results were obtained have often been reported to be less than satisfactory by both practitioners (Yankelovich & Meer 2006) and academics (Dolnicar 2003). In order to provide guidance to those undertaking market segmentation, this study discusses the critical issues involved when using cluster analysis to segment markets, makes suggestions for best practices and potential improvements, and presents an empirical survey that seeks to provide an up-to-date assessment of cluster analysis application in market segmentation within a six-stage framework. Analyses of more than 200 journal articles published since 2000, in which cluster analysis was empirically used in a marketing research setting, indicate that many critical issues are still ignored rather than addressed adequately