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Editor in Chief: RAFFAELLO COSSU

SUITABILITY INDEX FOR COLLECTION BIN ALLOCATION USING ANALYTICAL HIERARCHY PROCESS (AHP) CASCADED TO ARTIFICIAL NEURAL NETWORK (ANN)

  • Debasree Purkayastha - Department of Civil Engineering, Indian Institute of Technology Roorkee, India
  • Mrinmoy Majumder - Department of Civil Engineering, National Institute of Technology Agartala, India
  • Sumanta Chakrabarti - Maharaja Bir Bikram University, India

DOI 10.31025/2611-4135/2019.13867

Released under CC BY-NC-ND

Copyright: © 2018 CISA Publisher

Editorial History

  • Received: 26 Jul 2018
  • Revised: 13 Sep 2019
  • Accepted: 28 Oct 2019
  • Available online: 20 Nov 2019

Abstract

Municipal solid waste is an inevitable outcome of anthropogenic activities. Proper sustainable solid waste management is the need of the hour. In this study, a Suitability Index (S.I) has been determined which can measure the relative importance of a district with regard to its necessity or requirement of collection bins in comparison to other districts in a municipality. The S.I was computed using Analytical Hierarchy Process cascaded to Artificial Neural Network. Four criteria viz. Demographic, Social, Economic and Technical considerations and seven factors viz. Population Density (P.D), Street Width (S.W), Waste Generation Rate (W.G.R), Income Group Distribution (I.G.D), Average Minimum Distance between the bins (MIN.D), Available Number of Bins (A.N.B) and Cost of Waste Bins (C.W.B) were considered for developing the model. Available Number of Bins was found to have the highest impact on the model followed by C.W.B, W.G.R, MIN D., I.G.D, P.D, and S.W. This index will particularly help developing countries with resource constraint and unskilled labor force in Solid Waste Management. It will help such countries to easily locate districts in urgent need of collection bins with an easily available set of data and will help in increasing collection efficiency.

Keywords


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