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    Bridging expert knowledge and machine intelligence: a hybrid spatial indicator framework for ecotourism suitability
    (Elsevier, 2026) Karadeniz, Enes; Er, Selman; Aydogdu, Mujde; Sunbul, Fatih
    Ecotourism 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.
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    Forecasting urban shifts post-earthquake: LULC change analysis in Elazığ, Turkey using ANN and Markov models
    (Wiley, 2025) Sunbul, Fatih; Karadeniz, Enes; Sengun, Mustafa Taner; Kocaoglu, Muhammed
    Understanding land use and land cover (LULC) dynamics in seismically active regions is crucial for risk-informed urban planning and sustainable post-disaster recovery. This study investigates the impact of the Mw 6.8 Elaz & imath;& gbreve; earthquake (24 January 2020) on LULC patterns in eastern Turkey by integrating high-resolution Sentinel-2 satellite imagery with geographic information systems (GIS), remote sensing (RS), artificial neural networks (ANNs), and Markov chain modelling. The methodology comprises four phases: establishing a pre-earthquake baseline (2015-2019), assessing post-earthquake changes (2015-2023), analysing transition probabilities to identify key LULC drivers, and forecasting land-use scenarios for 2030 and 2050 under seismic and non-seismic conditions. Results reveal that seismic activity significantly accelerates urban expansion, shifting development towards geologically stable zones. By 2050, artificial surfaces are projected to occupy 54.70% of the region under seismic influence, compared to 48.87% without it. Agricultural land is more preserved in the seismic scenario (26.54%) than in the non-seismic case (22.68%), while pasture and meadow areas decline sharply to 6.18%, raising concerns for biodiversity and ecosystem services. These findings emphasise the importance of integrating ecological considerations and seismic risk into land-use planning frameworks. By combining multicriteria decision-making with machine learning-based forecasting, the study offers a replicable and scalable model for balancing urban growth, environmental conservation, and resilience. Framed within interdisciplinary insights from disaster resilience theory, urban governance, and spatial risk modelling, this research contributes to the global discourse on sustainable urban transformation in the face of increasing natural hazards.
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    From local pressures to global priorities: a PSR-guided model for spatial biodiversity conservation
    (Springer, 2026) Karadeniz, Enes; Sengun, M. Taner; Sunbul, Fatih
    Biodiversity conservation in socio-ecologically complex regions requires integrative frameworks that account for multi-scalar pressures, ecological conditions, and policy responses. This study proposes a novel application of the Pressure-State-Response (PSR) model combined with the Analytic Hierarchy Process (AHP) and geospatial analysis to systematically identify conservation priorities across a Mediterranean biodiversity hotspot. Using 24 environmental, social, and economic criteria, refined through field surveys (2022-2024) and interdisciplinary expert input, we demonstrate that anthropogenic pressures such as land use, transportation infrastructure, and forest fires account for 75% of total stress on biodiversity, while natural drivers like climate and geohazards contribute 25%. Ecosystem-level variables (e.g., climate heterogeneity, topography, and vegetation structure) received higher expert-derived weights than species-level indicators, emphasizing the foundational role of habitat integrity. Education emerged as the most influential response mechanism, surpassing legal and institutional measures, highlighting the need for community-driven strategies. Spatial prioritisation identified nearly 44% of the study region as high to very high priority for conservation action. Our findings offer a replicable, policy-relevant tool for transparent conservation planning and resource allocation, applicable not only to Mediterranean landscapes but also to other global biodiversity hotspots facing similar socio-ecological pressures.
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    From mapping to decision making: a hybrid rule-based and machine learning framework for spatial land-use zoning
    (Frontiers Media Sa, 2026) Esen, Fatma; Karadeniz, Enes; Sunbul, Fatih; Adiguzel, Asli Deniz; Sajjad, Muhammad
    The rapid conversion of land use in coastal regions necessitates advanced decision support frameworks that bridge the gap between mapping and operational zoning. This study introduces the Dual-Logic Spatial Zoning Model (DLSZM), a hybrid framework designed to translate socio-ecological indicators into four planning regimes: Strict Conservation, Managed Use, Development Guidance, and Restoration. Applied to the Antalya region in T & uuml;rkiye at a 30-meter grid resolution, the results demonstrate a high degree of regional convergence between expert-driven and machine learning pathways. Quantitative evaluation via an area-weighted confusion matrix shows that both methods produced identical classifications for Managed Use zones across approximately 7,630 square kilometers. While Managed Use remains the dominant classification, occupying landscapes with moderate ecological value, significant structural divergences were identified in transitional coastal belts. Alluvial transition analysis reveals that the machine learning model, driven by non-linear interactions captured in SHAP analysis, reassigned significant land areas from Strict Conservation and Development categories into the Restoration zone. Specifically, the machine learning framework identifies approximately 2,046 square kilometers of Restoration area, indicating a substantially higher sensitivity to cumulative stressors and degradation signals compared to the expert-derived logic. These findings suggest that while expert systems provide normative clarity, the machine learning pathway offers a more intervention-oriented spatial interpretation, effectively capturing the complex vulnerability dynamics of rapidly transforming coastal environments.
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    Land use and land cover change in Duzce region following the major earthquake: implications for ANN and Markov Chain Analysis
    (Springer, 2023) Karadeniz, Enes; Sunbul, Fatih
    Earthquakes are natural events that affect the spatial and temporal dynamics of land use/land cover. Here, we have focused on the city of Duzce, NW of Turkey, which has experienced the M > 7 doublet earthquakes in 1999. In order to conduct land cover change analysis and future projection, we have utilized integrated the multilayer perceptron neural network-based Markov chain model combined with geographical information systems. Our results show that most of the land use in 1990 was 77,601 hectares (65.89% of the region) consist of forest and shrubs. By 2020, agricultural land was the most expanding land class in the region with 48.80%. The 1999 earthquake caused the city to grow horizontally in the long run, and this expansion has ended the occupation in grade-one-agricultural lands that prevailed in the region. This horizontal development also caused the residential areas that covered 2.76% of the study area in 1999 expand to the ratio of 20.37% in 2050. It has been determined that the most effective driver in these changes in the land cover is the distance from urban with a Cramer's value of 0.4299. For this reason, the area that is most exposed to land cover change is the forest and shrub areas occupied by the city, which has spread to the northeast due to the earthquake. It is concluded that the earthquakes can change the land cover of the city significantly in various aspects in the long term, contrary to the approved zoning plans.
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    Su Havzası Yönetimi ve Planlaması için Stratejiler: Bibliyometrik Bir Analiz
    (2024) Sunbul, Fatih; Karadeniz, Enes; Sengün, Mustafa Taner; Engin, Fahrettin
    Yirminci yüzyılda havza planlamasına yönelik yönetişim yaklaşımları, potansiyel çevresel sorunlara yanıt olarak zaman içinde gelişmiştir. Çalışma öncelikle havza kavramına vurgu yapmaktadır. Küresel bilim camiasında son on yılda havza planlamasında kullanılan metodolojileri, özellikle en çok tercih edilen yöntemlere ve bunların popülerliğinin ardındaki gerekçelere odaklanarak araştırmaktadır. Bilim dünyasındaki önemli indekslerin incelenmesini içeren sistematik bir literatür taraması yapılmış ve toplam 10.154 yayın analiz edilmiştir. Sistematik literatür taramasında, bibliyografik atıf yönetim aracı olarak ENDNOTE X9 kullanılmıştır (Bramer, 2018). Ayrıca, seçilen 10.154 yayın arasından ilk okunacak yayınların seçimi için, VOSviewer ver.1.6.18 yazılımı (Van Eck ve Waltman, 2018) kullanılarak \"Havza\", \"Planlama\" ve \"Yönetim\" kavramlarıyla ilgili olan ve en fazla atıf alan yayınlar belirlenmiştir. Bu çalışmada, bilim dünyasının en kapsamlı taranan indekslerinden birinden arama yöntemleri kullanılarak toplam 10.154 yayın seçilmiştirBu yayınların başlıkları, anahtar kelimeleri ve özetleri incelenmiş, çalışmanın hedefleriyle uyumlu olanlar, kapsamlı bir incelemesi için seçilmiştir. Bu tam metin incelemesi sırasında, kullanılan yaygın yöntemleri belirlemek için 270 yayın değerlendirilmiştir. Özellikle, en sık atıfta bulunulan yayınları incelemek için ilk olarak VOS görüntüleyici programı kullanılmıştır. Tam metin incelemelerinin bulguları, \"Entegre Havza Yönetim Planlaması\" ve morfometrik endeksler, örtüşme ağırlıklandırma ve Analitik Hiyerarşi Süreci (AHP) olmak üzere coğrafi bilgi sistemleri tekniklerinin kullanımı gibi yöntemlerin kayda değer bir yaygınlığını ortaya koymuştur.