Bridging expert knowledge and machine intelligence: a hybrid spatial indicator framework for ecotourism suitability

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dc.contributor.authorKaradeniz, Enes
dc.contributor.authorEr, Selman
dc.contributor.authorAydogdu, Mujde
dc.contributor.authorSunbul, Fatih
dc.date.accessioned2026-04-04T13:35:09Z
dc.date.available2026-04-04T13:35:09Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractEcotourism suitability assessments increasingly rely on ecological indicators that capture spatial heterogeneity, ecosystem sensitivity, and biodiversity value. This study develops a hybrid indicator-based framework that combines fuzzy expert knowledge with ensemble machine learning to quantify ecotourism suitability in Malatya Province, T & uuml;rkiye. Fifteen ecological and socio-environmental predictors, including elevation, slope, climate variables, river proximity, biodiversity richness, endemic species distributions, and land-cover patterns, were incorporated into a GIS-based analytical environment. Expert-derived fuzzy weights were computed using the Fuzzy Logarithmic Methodology of Additive Weights (F-LMAW) to generate an Ecological Suitability Indicator (ESI). Complementarily, K-Means clustering was used to derive data-driven suitability classes, which were modeled using Random Forest and XGBoost with spatial-block cross-validation. XGBoost demonstrated superior classification performance (accuracy = 66.8%; kappa = 0.585). Across all models, biodiversity richness, endemic species presence, slope gradients, and river corridors consistently emerged as key ecological determinants. While the ESI produced conservative suitability zones, ensemble learning identified broader high-quality ecological landscapes, including the Levent Valley corridor, Nemrut foothills, and river-based habitats. The convergence between expert- and data-driven indicators demonstrates the reliability of hybrid ecological assessment. The proposed framework offers a transparent and transferable approach for constructing ecological suitability indicators in data-scarce, biodiversity-rich regions.
dc.identifier.doi10.1016/j.ecolind.2026.114678
dc.identifier.issn1470-160X
dc.identifier.issn1872-7034
dc.identifier.orcid0000-0003-0757-8553
dc.identifier.orcid0000-0001-6341-5463
dc.identifier.scopus2-s2.0-105029052228
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ecolind.2026.114678
dc.identifier.urihttps://hdl.handle.net/11616/109652
dc.identifier.volume183
dc.identifier.wosWOS:001681981800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofEcological Indicators
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectEcological suitability indicators
dc.subjectFuzzy LMAW
dc.subjectEnsemble machine learning
dc.subjectBiodiversity hotspots
dc.subjectSpatial ecological modeling
dc.subjectK -means clustering
dc.subjectGIS-based assessment
dc.titleBridging expert knowledge and machine intelligence: a hybrid spatial indicator framework for ecotourism suitability
dc.typeArticle

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