Development of Environmental Quality Predictor Using Feedforward Artificial Neural Network (Fann) In Matlab Graphical User Interface (Gui)

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Date
2015-05-01
Authors
Nazira Anisa Rahim
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Abstract
The environmental conservation efforts always deal with the complexity problem as it involves a large number of variables. However, in the process development of the model, the correct input selection for the corresponding output prediction is so important. Furthermore, traditional reports on the environmental quality tend to be too technical, presenting monitoring data without providing a complete and easy to understand facts of the environmental quality. Due to the redundancy of the environmental datas, input data selection methods were introduced; Canonical Correspondence Analysis (CCA) and Canonical Correlation Analysis (CCorA). These approaches could be applied as a feature selection tools and combined with Feedforward Artificial Neural Networks (FANN) to develop the graphical prediction interface for the end users. The proposed graphical userinterface for environmental prediction, will give an indication of the water and air pollution degree and their qualities, with the terms that are familiar within the community. To achieve those objectives, this research was divided into three main phases; determination of input feature selection, FANN model development and finally, GUI development for offline monitoring. Two case studies were used in this research which was based on river water and air quality data. The application of CCA and CCorA to determine the input for the prediction was successfully applied with 7 (SS, NO3, K, NH3-NL, TS, Zn and Tur) and 3 (humidity, temperature and wind speed) input variables were selected for Case Study 1 and 2, respectively. The results show that the developed prediction networks for the environmental quality prediction system has been executed well for less of input data. The developed prediction system based on FANN with the combination of CCA and CCorA generally has generally performed well and helped in simplifying the environmental prediction system. The final multi-input single output (MISO) models that have been used to predict the water quality index (WQI) and air pollution index (API) were successfully developed with the regression values of 0.90 and 0.91 for both of the networks for the unseen validation data input.
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