The prediction of tropospheric ozone concentrations is very important due to the negative impacts of ozone on human health, climate and vegetation. The development of models to predict ozone concentrations is thus very useful because it can provide early warnings to the population and also reduce the number of measuring sites. The aim of this study was to predict next day hourly ozone concentrations through a new methodology based on feedforward artificial neural networks using principal components as inputs. The developed model was compared with multiple linear regression, feedforward artificial neural networks based on the original data and also with principal component regression. Results showed that the use of principal components as inputs improved both models prediction by reducing their complexity and eliminating data collinearity.

Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations

Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor

Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction

Heavy metal contents, distribution, and prediction in a regional soil-wheat system.

Quantity prediction of municipal solid waste (MSW) is crucial for design and programming municipal solid waste management system (MSWMS). Because effect of various parameters on MSW quantity and its high fluctuation, prediction of generated MSW is a difficult task that can lead to enormous error. The works presented here involve developing an improved support vector machine (SVM) model, which combines the principal component analysis (PCA) technique with the SVM to forecast the weekly generated waste of Mashhad city. In this study, the PCA technique was first used to reduce and orthogonalize the original input variables (data). Then these treated data were used as new input variables in SVM model. This improved model was evaluated by using weekly time series of waste generation (WG) and the number of trucks that carry waste in week of t. These data have been collected from 2005 to 2008. By comparing the predicted WG with the observed data, the effectiveness of the proposed model was verified. Therefore, in authors' opinion, the model presented in this article is a potential tool for predicting WG and has advantages over the traditional SVM model.

Handan Çamdevýren

Papers

Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs