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International Journal of Geography and Geology

October 2016, Volume 5, 10, pp 209-223

Landfill Site Selection Using Simple Additive Weighting (SAW) Method and Artificial Neural Network Method; A Case Study from Lorestan Province, Iran

E. Mokhtari


M. Khamehchian


G. Montazer


M. Nikudel

E. Mokhtari 1

M. Khamehchian 2
G. Montazer 3
M. Nikudel 4

  1. PHD of Engineering Geology Department, Tarbiat Modares University, Tehran, Iran 1

  2. Professor of Engineering Geology Department, Tarbiat Modares University, Tehran, Iran 2

  3. Associate Professor of Information Technology, School of Engineering, Tarbiat Modares University, Tehran, Iran 3

  4. Assistant Professor of Engineering Geology Department, Tarbiat Modares University, Tehran, Iran 4

Pages: 209-223

DOI: 10.18488/journal.10/2016.5.10/

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The selection of a site for landfill is one of the most difficult steps in landfilling process. Several techniques and methods have been used in a sanitary landfill site selection in the literature. In this paper two methods consist of a method based on the artificial neural network method and simple additive weighting (SAW) method have been used to landfill site selection in Lorestan province, IRAN and the results of two methods compare to each other. The input data of the research consist of 9 digitized data layers including geology, faults, slope, vegetation, residential areas, road and railways, groundwater resources, dams, drainage network maps. The land suitability map prepared by means of SAW method has been grouped in five categories with 0.5 intervals (Ai: 0 to 2.5). Derived map from the neural network modeling exhibit a gradual suitability for a landfill site. With SAW method that was used in the first step of the research, most of the area is considered unsuitable, while by the neural network method, the area with high suitability covers different parts of the study area. One of the most characteristics of the neural network methods is flexibility. The maps that are provided by these methods help the decision makers to select areas with a high suitability value and then proceed to field investigations according to the level of enforcement of the other policies.

Contribution/ Originality
This study contributes in the existing literature about the multi-criteria decision making and the using intelligence tools for site selection. This study uses new methodology for landfill site selection by means of artificial neural networks. This study originates new model for site selection based on the geological and geographical data.


Landfill site selection, SAW method, Neural network method, Municipal solid waste, Land suitability, Lorestan province.



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This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.


All authors contributed equally to the conception and design of the study.

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