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Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah

Many of natural sources had been polluted such as water, air, sound and soil(mineral). This research is for making the classification of soil contamination with uncontaminated soil for sand and laterite type of soil. The contamination will be use is formed by hydrocarbon compound which was diesel. T...

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Bibliographic Details
Main Author: Abdullah, Mohamad Adib
Format: Thesis
Language:English
Published: 2021
Subjects:
Remote Sensing
Soils. Soil science. Including soil surveys, soil chemistry, soil structure, soil-plant relationships
Online Access:http://ir.uitm.edu.my/id/eprint/42327/1/42327.pdf
http://ir.uitm.edu.my/id/eprint/42327/
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spelling my.uitm.ir.423272021-02-25T04:48:52Z http://ir.uitm.edu.my/id/eprint/42327/ Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah Abdullah, Mohamad Adib Remote Sensing Soils. Soil science. Including soil surveys, soil chemistry, soil structure, soil-plant relationships Many of natural sources had been polluted such as water, air, sound and soil(mineral). This research is for making the classification of soil contamination with uncontaminated soil for sand and laterite type of soil. The contamination will be use is formed by hydrocarbon compound which was diesel. This research will be conducted at test bed of GPR scanning in UiTM Perlis. After the data of contaminated and uncontaminated soil are collected, the raw data need to process using Reflexw. The preprocessing of data radar gram consists of move start time, dynamic correction, and hyperbola fitting. GPR data interpretation can be use for classify the buried feature by using machine learning. In this research the classification method that will be using Support Vector Machine (SVM) classifier. The open source provided SVM function is Waikato Environment for Knowledge Analysis (Weka). The SVM classification provided a good quality of classification. All of three soil type classification produce correct instances classified above than 95%. This classification also had been compared with logistic regression classification. The root mean square of these classification provided good result all of them were below 0.05. 2021-02-23 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/42327/1/42327.pdf Abdullah, Mohamad Adib (2021) Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah. Degree thesis, Universiti Teknologi Mara Perlis.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Remote Sensing
Soils. Soil science. Including soil surveys, soil chemistry, soil structure, soil-plant relationships
spellingShingle Remote Sensing
Soils. Soil science. Including soil surveys, soil chemistry, soil structure, soil-plant relationships
Abdullah, Mohamad Adib
Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah
description Many of natural sources had been polluted such as water, air, sound and soil(mineral). This research is for making the classification of soil contamination with uncontaminated soil for sand and laterite type of soil. The contamination will be use is formed by hydrocarbon compound which was diesel. This research will be conducted at test bed of GPR scanning in UiTM Perlis. After the data of contaminated and uncontaminated soil are collected, the raw data need to process using Reflexw. The preprocessing of data radar gram consists of move start time, dynamic correction, and hyperbola fitting. GPR data interpretation can be use for classify the buried feature by using machine learning. In this research the classification method that will be using Support Vector Machine (SVM) classifier. The open source provided SVM function is Waikato Environment for Knowledge Analysis (Weka). The SVM classification provided a good quality of classification. All of three soil type classification produce correct instances classified above than 95%. This classification also had been compared with logistic regression classification. The root mean square of these classification provided good result all of them were below 0.05.
format Thesis
author Abdullah, Mohamad Adib
author_facet Abdullah, Mohamad Adib
author_sort Abdullah, Mohamad Adib
title Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah
title_short Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah
title_full Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah
title_fullStr Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah
title_full_unstemmed Soil contamination classification based on ground penetrating radar data using support vector machine / Mohamad Adib Abdullah
title_sort soil contamination classification based on ground penetrating radar data using support vector machine / mohamad adib abdullah
publishDate 2021
url http://ir.uitm.edu.my/id/eprint/42327/1/42327.pdf
http://ir.uitm.edu.my/id/eprint/42327/
_version_ 1692994686677942272
score 13.211869

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