Quantitative workflow based on NN for weighting criteria in landfill suitability mapping

Our study aims to introduce a new quantitative workflow that integrates neural networks (NNs) and multi criteria decision analysis (MCDA). Existing MCDA workflows reveal a number of drawbacks, because of the reliance on human knowledge in the weighting stage. Thus, new workflow presented to form sui...

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Bibliographic Details
Main Authors: Abujayyab, Sohaib K. M., Ahamad, Mohd Sanusi S., Yahya, Ahmad Shukri, Ahmad, Siti Zubaidah, Alkhasawneh, Mutasem Sh, Aziz, Hamidi Abdul
Format: Conference Proceeding
Language:English
Subjects:
Criteria
Datasets
Decision analysis
Landfill
Landfills
Neural networks
Regional planning
Weighting
Workflow
Online Access:Get full text
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Summary:Our study aims to introduce a new quantitative workflow that integrates neural networks (NNs) and multi criteria decision analysis (MCDA). Existing MCDA workflows reveal a number of drawbacks, because of the reliance on human knowledge in the weighting stage. Thus, new workflow presented to form suitability maps at the regional scale for solid waste planning based on NNs. A feed-forward neural network employed in the workflow. A total of 34 criteria were pre-processed to establish the input dataset for NN modelling. The final learned network used to acquire the weights of the criteria. Accuracies of 95.2% and 93.2% achieved for the training dataset and testing dataset, respectively. The workflow was found to be capable of reducing human interference to generate highly reliable maps. The proposed workflow reveals the applicability of NN in generating landfill suitability maps and the feasibility of integrating them with existing MCDA workflows.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.5005760