GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique

Mahbina Akter Mim; K. M. Shawkat Zamil

doi:doi:10.11648/j.mlr.20180302.11

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GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique

Mahbina Akter Mim, K. M. Shawkat Zamil

Published in Machine Learning Research (Volume 3, Issue 2)

Received: 22 July 2018 Accepted: 1 August 2018 Published: 3 September 2018

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Abstract

Water is one of the essential natural resources of nature. All living creature depends on water. Living creatures are using water for their different purposes. Earth’s large portion is covered by salt water but very less has fresh water. Freshwater can be found as groundwater and surface water. Surface water is stored as waterbodies on the surface of this world. Ponds, canals, rivers, and lakes are some of the waterbodies that provide fresh water to us. These waterbodies are fulfilling our need for fresh water. Most of the waterbodies are drying up for natural disasters or they are continuously filling by humans. These resources need some of our attention to preserve them. Rajshahi Development Authority (RDA) and United States Geological Survey (USGS) provide important data for this research. Waterbodies are detected by using Geographic Information System (GIS), GIS gives us the power of mapping and store, detect, and manipulate spatial or geographic data. Images are collected from the Landsat 4-5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI). They are classified by using ArcGIS. Images are classified in maximum likelihood classification by generating signature files to extract feature. Percentage of waterbodies in each year is calculated from the attribute table. A dataset is prepared from these features and tested on different classification techniques. Support Vector Machine (SVM), Decision Tree and Random Forest Technique are implemented on this dataset. Among them, Random Forest shows 92% accuracy, which is better from other techniques. These algorithms also measure the precision, recall, and f1 scores of the classifiers. The precision, recall, and f1-score of random forest technique show 0.943, 0.920, 0.922, which indicate better accuracy than other techniques.

Published in	Machine Learning Research (Volume 3, Issue 2)
DOI	10.11648/j.mlr.20180302.11
Page(s)	11-17
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Waterbodies, GIS, Remote Sensing, ArcGIS, Maximum Likelihood Classification, Support Vector Machine (SVM), Decision Tree, Random Forest

References

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[2]	Md. Habibur Rahman. "Pond filling plagues Rajshahi city." DhakaTribune, 2 Sept. 2014, archive.dhakatribune.com/environment/2014/sep/02/pond-filling-plagues-rajshahi-city.
[3]	"Rajshahi City Corporation." BANGLAPEDIA, 9Mar. 2015, en.banglapedia.org/index.php?title=Rajshahi_City_Corporation.
[4]	Perlman, USGS Howard. "Groundwater depletion." Groundwater depletion, USGS water science, 9 Dec. 2016, water.usgs.gov/edu/gwdepletion.html.
[5]	Ahmeduzzaman, Mohammad, Shantanu Kar, and Abdullah Asad. "A Study on Ground Water Fluctuation at Barind Area, Rajshahi." International Journal of Engineering Research and Applications (IJERA) ISSN (2012): 2248-9622.
[6]	George, Geeja K., et al. "Study of Ground Water Pollution around an Industry Using GIS."
[7]	Melgani, Farid, and Lorenzo Bruzzone. "Classification of hyperspectral remote sensing images with support vector machines." IEEE Transactions on geoscience and remote sensing 42.8 (2004): 1778-1790.
[8]	"Landsat Program." Wikipidea, Wikimedia Function, 29 Nov. 2017, en.wikipidea.org/wiki/Landsat_program.
[9]	Natrella, Mary. "NIST/SEMATECH e-handbook of statistical methods." (2010).
[10]	Franc, Vojtech, and Václav Hlavác. "Multi-class support vector machine." Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002.
[11]	Patel, Savan. "Chapter 2: SVM (Support Vector Machine) - Theory – Machine Learning 101 – Medium." Medium, Machine Learning 101, 3 May 2017, medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72.
[12]	Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin. "A practical guide to support vector classification." (2003): 1-16.
[13]	Jin, Chen, Luo De-Lin, and Mu Fen-Xiang. "An improved ID3 decision tree algorithm." Computer Science & Education, 2009. ICCSE'09. 4th International Conference on. IEEE, 2009.
[14]	Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
[15]	Qi, Yanjun, Judith Klein-Seetharaman, and Ziv Bar-Joseph. "Random forest similarity for protein-protein interaction prediction from multiple sources." Biocomputing 2005. 2005. 531-542.
[16]	Kotsiantis, Sotiris B., I. Zaharakis, and P. Pintelas. "Supervised machine learning: A review of classification techniques." (2007): 3-24.
[17]	"Supervised and Unsupervised Classification in ArcGIS."GISGeography, 22 Jan. 2017, gisgeography.com/supervised-unsupervised-classification-arcgis/.

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APA Style

Mahbina Akter Mim, K. M. Shawkat Zamil. (2018). GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique. Machine Learning Research, 3(2), 11-17. https://doi.org/10.11648/j.mlr.20180302.11

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ACS Style

Mahbina Akter Mim; K. M. Shawkat Zamil. GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique. Mach. Learn. Res. 2018, 3(2), 11-17. doi: 10.11648/j.mlr.20180302.11

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AMA Style

Mahbina Akter Mim, K. M. Shawkat Zamil. GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique. Mach Learn Res. 2018;3(2):11-17. doi: 10.11648/j.mlr.20180302.11

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@article{10.11648/j.mlr.20180302.11,
  author = {Mahbina Akter Mim and K. M. Shawkat Zamil},
  title = {GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique},
  journal = {Machine Learning Research},
  volume = {3},
  number = {2},
  pages = {11-17},
  doi = {10.11648/j.mlr.20180302.11},
  url = {https://doi.org/10.11648/j.mlr.20180302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20180302.11},
  abstract = {Water is one of the essential natural resources of nature. All living creature depends on water. Living creatures are using water for their different purposes. Earth’s large portion is covered by salt water but very less has fresh water. Freshwater can be found as groundwater and surface water. Surface water is stored as waterbodies on the surface of this world. Ponds, canals, rivers, and lakes are some of the waterbodies that provide fresh water to us. These waterbodies are fulfilling our need for fresh water. Most of the waterbodies are drying up for natural disasters or they are continuously filling by humans. These resources need some of our attention to preserve them. Rajshahi Development Authority (RDA) and United States Geological Survey (USGS) provide important data for this research. Waterbodies are detected by using Geographic Information System (GIS), GIS gives us the power of mapping and store, detect, and manipulate spatial or geographic data. Images are collected from the Landsat 4-5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI). They are classified by using ArcGIS. Images are classified in maximum likelihood classification by generating signature files to extract feature. Percentage of waterbodies in each year is calculated from the attribute table. A dataset is prepared from these features and tested on different classification techniques. Support Vector Machine (SVM), Decision Tree and Random Forest Technique are implemented on this dataset. Among them, Random Forest shows 92% accuracy, which is better from other techniques. These algorithms also measure the precision, recall, and f1 scores of the classifiers. The precision, recall, and f1-score of random forest technique show 0.943, 0.920, 0.922, which indicate better accuracy than other techniques.},
 year = {2018}
}

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TY - JOUR
T1 - GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique
AU - Mahbina Akter Mim
AU - K. M. Shawkat Zamil
Y1 - 2018/09/03
PY - 2018
N1 - https://doi.org/10.11648/j.mlr.20180302.11
DO - 10.11648/j.mlr.20180302.11
T2 - Machine Learning Research
JF - Machine Learning Research
JO - Machine Learning Research
SP - 11
EP - 17
PB - Science Publishing Group
SN - 2637-5680
UR - https://doi.org/10.11648/j.mlr.20180302.11
AB - Water is one of the essential natural resources of nature. All living creature depends on water. Living creatures are using water for their different purposes. Earth’s large portion is covered by salt water but very less has fresh water. Freshwater can be found as groundwater and surface water. Surface water is stored as waterbodies on the surface of this world. Ponds, canals, rivers, and lakes are some of the waterbodies that provide fresh water to us. These waterbodies are fulfilling our need for fresh water. Most of the waterbodies are drying up for natural disasters or they are continuously filling by humans. These resources need some of our attention to preserve them. Rajshahi Development Authority (RDA) and United States Geological Survey (USGS) provide important data for this research. Waterbodies are detected by using Geographic Information System (GIS), GIS gives us the power of mapping and store, detect, and manipulate spatial or geographic data. Images are collected from the Landsat 4-5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI). They are classified by using ArcGIS. Images are classified in maximum likelihood classification by generating signature files to extract feature. Percentage of waterbodies in each year is calculated from the attribute table. A dataset is prepared from these features and tested on different classification techniques. Support Vector Machine (SVM), Decision Tree and Random Forest Technique are implemented on this dataset. Among them, Random Forest shows 92% accuracy, which is better from other techniques. These algorithms also measure the precision, recall, and f1 scores of the classifiers. The precision, recall, and f1-score of random forest technique show 0.943, 0.920, 0.922, which indicate better accuracy than other techniques.
VL - 3
IS - 2
ER -

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Author Information

Mahbina Akter Mim

Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
K. M. Shawkat Zamil

Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh

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APA Style

Mahbina Akter Mim, K. M. Shawkat Zamil. (2018). GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique. Machine Learning Research, 3(2), 11-17. https://doi.org/10.11648/j.mlr.20180302.11

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ACS Style

Mahbina Akter Mim; K. M. Shawkat Zamil. GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique. Mach. Learn. Res. 2018, 3(2), 11-17. doi: 10.11648/j.mlr.20180302.11

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AMA Style

Mahbina Akter Mim, K. M. Shawkat Zamil. GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique. Mach Learn Res. 2018;3(2):11-17. doi: 10.11648/j.mlr.20180302.11

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@article{10.11648/j.mlr.20180302.11,
  author = {Mahbina Akter Mim and K. M. Shawkat Zamil},
  title = {GIS-Based Analysis of Changing Surface Water in Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique},
  journal = {Machine Learning Research},
  volume = {3},
  number = {2},
  pages = {11-17},
  doi = {10.11648/j.mlr.20180302.11},
  url = {https://doi.org/10.11648/j.mlr.20180302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20180302.11},
  abstract = {Water is one of the essential natural resources of nature. All living creature depends on water. Living creatures are using water for their different purposes. Earth’s large portion is covered by salt water but very less has fresh water. Freshwater can be found as groundwater and surface water. Surface water is stored as waterbodies on the surface of this world. Ponds, canals, rivers, and lakes are some of the waterbodies that provide fresh water to us. These waterbodies are fulfilling our need for fresh water. Most of the waterbodies are drying up for natural disasters or they are continuously filling by humans. These resources need some of our attention to preserve them. Rajshahi Development Authority (RDA) and United States Geological Survey (USGS) provide important data for this research. Waterbodies are detected by using Geographic Information System (GIS), GIS gives us the power of mapping and store, detect, and manipulate spatial or geographic data. Images are collected from the Landsat 4-5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI). They are classified by using ArcGIS. Images are classified in maximum likelihood classification by generating signature files to extract feature. Percentage of waterbodies in each year is calculated from the attribute table. A dataset is prepared from these features and tested on different classification techniques. Support Vector Machine (SVM), Decision Tree and Random Forest Technique are implemented on this dataset. Among them, Random Forest shows 92% accuracy, which is better from other techniques. These algorithms also measure the precision, recall, and f1 scores of the classifiers. The precision, recall, and f1-score of random forest technique show 0.943, 0.920, 0.922, which indicate better accuracy than other techniques.},
 year = {2018}
}

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