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Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network

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  • Ali Jahani
  • Jahangir Feghhi
  • Majid F. Makhdoum
  • Mahmoud Omid

Abstract

The purpose of this article is Artificial Neural Network (ANN) modeling using ecological and associated factors with forest degradation to predict the degradation of ecosystem, thereby enabling us to assess the environmental impacts of forest projects as an Environmental Decision Support System (EDSS). Results of the Multi-Layer Feed-Forward Network (MLFN), trained for Optimized Forest Degradation Model (OFDM), indicate that the performance of OFDM is more than other degradation models. Changes in forest management activities with higher value in sensitivity analysis help forest managers to decrease OFDM entity and environment impacts. The system is an intelligent EDSS, which allows the decision-maker to model criteria in forest degradation in order to reach and employ the optimal allocation plan. Considering results, multi criteria decision analysis (MCDA) approaches based on ANN, is an encouraging and robust method for solving MCDA problems.

Suggested Citation

  • Ali Jahani & Jahangir Feghhi & Majid F. Makhdoum & Mahmoud Omid, 2016. "Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 59(2), pages 222-244, February.
    • Handle: RePEc:taf:jenpmg:v:59:y:2016:i:2:p:222-244
    DOI: 10.1080/09640568.2015.1005732
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    Cited by:

    1. Ali Jahani, 2019. "Forest landscape aesthetic quality model (FLAQM): A comparative study on landscape modelling using regression analysis and artificial neural networks," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 65(2), pages 61-69.

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