Hybrid model-based prediction of biomass density in case studies in Turkiye

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dc.contributor.authorIsler, B.
dc.contributor.authorAslan, Z.
dc.contributor.authorSunar, F.
dc.contributor.authorGunes, A.
dc.contributor.authorFeoli, E.
dc.contributor.authorGabriels, D.
dc.date.accessioned2024-05-19T14:39:01Z
dc.date.available2024-05-19T14:39:01Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractGrowing global concern over natural resource degradation due to urbanisation and population growth emphasizes the critical need for innovative solutions. Addressing this imperative, our study pioneers the integration of cutting-edge artificial intelligence (AI) methods to investigate crucial changes in vegetation density. In this context, a hybrid model, which harmoniously integrates conventional artificial neural network (ANN) models with the innovative Wavelet-ANN (W-ANN) approach, was employed in two case pilot areas, namely on Alanya in Antalya and Iznik in Bursa, Turkiye, renowned for their distinct ecosystems and land cover patterns. By employing diverse data sources, encompassing satellite-derived metrics such as the Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) from the MODIS/Terra satellite, alongside atmospheric data, our investigation intricately models temporal vegetation dynamics extending to the year 2030. Remarkably, the WANN model demonstrates better predictive performance compared to conventional methodologies. It anticipates a substantial 21.4% reduction in vegetation biomass density for Iznik, achieving a minimal 5.4% error probability. Similarly, for Alanya, the model forecasts a notable 6.6% decrease with a remarkably low 2% error probability, both projections extending to the year 2030. Our study reveals a significant reduction in vegetation biomass density by comparing the projected values of the W-ANN model for 2030 with the observed data from 2018. These findings gain further support from an analysis of the Normalised Difference Built-up Index (NDBI) derived from Landsat satellites, affirming the exceptional efficacy of our innovative AI-driven approach in advancing the understanding of urbanisation's impact on ecosystems.en_US
dc.description.sponsorshipICTP; Abdus Salam International Centre for Theoretical Physics; Simons Associate Programme; Turkish State Meteorological Organizationen_US
dc.description.sponsorshipThe authors express their gratitude to ICTP, the Abdus Salam International Centre for Theoretical Physics, the Simons Associate Programme, and the Turkish State Meteorological Organization for their funding, which was provided through collaboration with the research group to support the data.en_US
dc.identifier.doi10.1016/j.ecoinf.2023.102439
dc.identifier.issn1574-9541
dc.identifier.issn1878-0512
dc.identifier.urihttps://doi.org10.1016/j.ecoinf.2023.102439
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4678
dc.identifier.volume79en_US
dc.identifier.wosWOS:001143416300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofEcological Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAnn/W-Ann Modellingen_US
dc.subjectAtmospheric Dataen_US
dc.subjectEvien_US
dc.subjectLsten_US
dc.subjectNdbien_US
dc.titleHybrid model-based prediction of biomass density in case studies in Turkiyeen_US
dc.typeArticleen_US

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