Prediction Of Air Pollution Index (API) And Water Quality Index (WQI) Using Support Vector Machine (SVM)
This study was about to generate a suitable support vector machine (SVM) model to predict the air pollution index (API) and water quality index (WQI). The current calculations of API and WQI were complex and time consuming. With the SVM model, the API and WQI can be predicted immediately by using...
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| Main Author: | |
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2017
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| Subjects: | |
| Online Access: | http://eprints.usm.my/53210/1/Prediction%20Of%20Air%20Pollution%20Index%20%28API%29%20And%20Water%20Quality%20Index%20%28WQI%29%20Using%20Support%20Vector%20Machine%20%28SVM%29_Leong%20Wei%20Cong_K4_2017.pdf http://eprints.usm.my/53210/ |
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| Summary: | This study was about to generate a suitable support vector machine (SVM) model
to predict the air pollution index (API) and water quality index (WQI). The current
calculations of API and WQI were complex and time consuming. With the SVM model,
the API and WQI can be predicted immediately by using the same predictors used in the
calculation. There were three main parameters that control the performance of the SVM
model, they were parameter C, ε and the type of kernel function used. In this study, only
the type kernel function was investigated, they were linear, radial basis function (RBF)
and polynomial kernel function. The results of the model were then analysed by using
sum squares error (SSE), mean of sum squares error (MSSE) and coefficient of
determination (R2). After the best type of kernel function was chosen for API and WQI
SVM models, the types of kernel function were further utilised to train the least square
support vector machine (LS-SVM) models to compare the accuracy between SVM and
LS-SVM models. It was found that the best kernel function for API SVM model was RBF
kernel function with R2 of 0.9843 while for WQI SVM model was polynomial kernel
function with R2 of 0.8796. Moreover, it was found that WQI LS-SVM model that trained
with correct predictors was having higher accuracy with R2 of 0.9227 compared with
WQI SVM model that trained with all the predictors with R2 of 0.9184. Unfortunately,
API LS-SVM model was not be able to train since the large amount of set of data was
difficult to be processed by the computer available. |
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