The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W1, 199–208, 2016
https://doi.org/10.5194/isprs-archives-XLII-4-W1-199-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W1, 199–208, 2016
https://doi.org/10.5194/isprs-archives-XLII-4-W1-199-2016

  29 Sep 2016

29 Sep 2016

SPATIAL DATA MINING TOOLBOX FOR MAPPING SUITABILITY OF LANDFILL SITES USING NEURAL NETWORKS

S. K. M. Abujayyab1, M. S. S. Ahamad1, A. S. Yahya1, and H. A. Aziz1,2 S. K. M. Abujayyab et al.
  • 1School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, P. Pinang, Malaysia
  • 2Solid Waste Management Cluster, Science and Engineering Research Center, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia

Keywords: Spatial data mining, GIS, neural networks, ArcGIS toolbox, landfill suitability analysis

Abstract. Mapping the suitability of landfill sites is a complex field and is involved with multidiscipline. The purpose of this research is to create an ArcGIS spatial data mining toolbox for mapping the suitability of landfill sites at a regional scale using neural networks. The toolbox is constructed from six sub-tools to prepare, train, and process data. The employment of the toolbox is straightforward. The multilayer perceptron (MLP) neural networks structure with a backpropagation learning algorithm is used. The dataset is mined from the north states in Malaysia. A total of 14 criteria are utilized to build the training dataset. The toolbox provides a platform for decision makers to implement neural networks for mapping the suitability of landfill sites in the ArcGIS environment. The result shows the ability of the toolbox to produce suitability maps for landfill sites.