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Mapping high poplar growth areas for bioenergy cultivation: A swarm-optimized approach

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  • Jaafari, Abolfazl

Abstract

Poplar trees are a renewable energy source, and identifying high-growth areas for their cultivation is essential for successful bioenergy production. This study presents a novel approach to identify these areas using support vector regression (SVR) combined with two swarm intelligence optimization algorithms, i.e., particle swarm optimization (PSO) and gray wolf optimizer (GWO). The approach uses a spatial database of thirteen geoenvironmental variables and the location of 136 poplar farms in a rugged and heterogeneous region in western Iran. The PSO and GWO algorithms optimized the hyperparameters of the base SVR model and generated two swarm-optimized models: SVR-PSO and SVR-GWO. The swarm-optimized models were compared to the standalone SVR model using various evaluation metrics. The results showed that the SVR-GWO model achieved the highest performance (accuracy = 82%, sensitivity = 82%, specificity = 74%, and prediction rate = 92%), followed by the SVR-PSO model (accuracy = 81%, sensitivity = 81%, specificity = 72%, and prediction rate = 90%), while the standalone SVR model had accuracy and stability (accuracy = 75%, sensitivity = 77%, specificity = 68%, and prediction rate = 82%). The results also revealed that elevation, slope, topographic wetness index, and proximity to roads and rivers were strongly associated with the distribution of poplar farms in the study area. This study provided a novel and effective approach for identifying high poplar growth areas that can help decision-makers and stakeholders allocate resources and plan interventions to promote poplar cultivation for renewable energy production.

Suggested Citation

  • Jaafari, Abolfazl, 2023. "Mapping high poplar growth areas for bioenergy cultivation: A swarm-optimized approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:rensus:v:187:y:2023:i:c:s1364032123006056
    DOI: 10.1016/j.rser.2023.113748
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    References listed on IDEAS

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    1. Vera, Ivan & Wicke, Birka & Lamers, Patrick & Cowie, Annette & Repo, Anna & Heukels, Bas & Zumpf, Colleen & Styles, David & Parish, Esther & Cherubini, Francesco & Berndes, Göran & Jager, Henriette & , 2022. "Land use for bioenergy: Synergies and trade-offs between sustainable development goals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Yılmaz, Derya & Tanyer, Ali Murat & Toker, İrem Dikmen, 2023. "A data-driven energy performance gap prediction model using machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 181(C).
    3. S. B. Chavan & R. S. Dhillon & Ajit & R. H. Rizvi & Chhavi Sirohi & A. K. Handa & K. K. Bharadwaj & Vishal Johar & Tarun Kumar & Pradyuman Singh & Vijay Daneva & Sushil Kumari, 2022. "Estimating biomass production and carbon sequestration of poplar-based agroforestry systems in India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(12), pages 13493-13521, December.
    4. Donald Leffers & Gerda R. Wekerle & L. Anders Sandberg, 2022. "Competing claims in land-use policy: property development and mineral aggregates industries," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 65(10), pages 1875-1892, June.
    5. Fuertes, A. & Oliveira, N. & Cañellas, I. & Sixto, H. & Rodríguez-Soalleiro, R., 2021. "An economic overview of Populus spp. in Short Rotation Coppice systems under Mediterranean conditions: An assessment tool for decision-making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
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