Balancing Conservation and Development Through Explainable Machine Learning and NSGA-II: A Case Study of Osmaniye
5 affiliations
- 1Transportation and Traffic Services Program, Department of Transportation Services, Vocational School of Technical Sciences, Bitlis Eren University, Bitlis 13100, Türkiye
- 2Department of Geography, Faculty of Arts and Science, Inonu University, Malatya 44280, Türkiye
- 3Department of Forest Engineering, Faculty of Forestry, Bartin University, Agdaci Campus, Bartin 74100, Türkiye
- 4Department of Geography, Faculty of Humanities and Social Sciences, Manisa Celal Bayar University, Manisa 45140, Türkiye
- 5Department of Forest Engineering, Faculty of Forestry, Bartin University, Bartin 74100, Türkiye
Abstract
Land-use planning in ecologically sensitive landscapes requires balancing biodiversity conservation, ecosystem service provision, agricultural production, settlement expansion, and infrastructure demand within a single spatial system. This challenge is particularly significant in Mediterranean environments, where long-term land transformations and increasing development pressures intensify conflicts among competing land-use priorities. Accordingly, the present study develops an integrated spatial zoning and decision-support framework for Osmaniye Province, southern Türkiye. The framework integrates fuzzy multi-criteria evaluation, CatBoost-based machine learning, SHAP-based interpretability, and NSGA-II multi-objective optimization. The workflow followed a sequential decision process in which an expert-derived zoning surface was first established through fuzzy evaluation, reconstructed from continuous spatial predictors using CatBoost, interpreted through SHAP, and refined through NSGA-II under explicit spatial constraints. By using the expert-derived zoning surface as the learning target, the CatBoost stage aimed to evaluate the internal consistency and spatial learnability of the planning logic within a present-day zoning context. The results indicated that the integrated framework distinguished conservation, controlled-use, and development priorities while identifying the key environmental and anthropogenic drivers shaping class-specific zoning outcomes. The final zoning structure allocated 37.9% of the study area to conservation, 43.6% to controlled use, and 18.5% to development. The study shows that by including a transitional zone with varying proportions of conservation, controlled use, and development, a more balanced distribution among the three goals can be achieved compared to a fixed partition into these three zones. The findings further demonstrate that this approach is more effective than current zoning, which does not accommodate such trade-offs.
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BibTeX
@article{adıguzel2026balancing,
title = {{Balancing Conservation and Development Through Explainable Machine Learning and NSGA-II: A Case Study of Osmaniye}},
author = {Fatih Adıgüzel and Enes Karadeniz and Tuna Emir and Ferhat Arslan and Halil Barış Özel},
journal = {Land},
year = {2026},
month = may,
doi = {10.3390/land15050881},
url = {https://doi.org/10.3390/land15050881},
note = {CC BY},
}
RIS
TY - JOUR
TI - Balancing Conservation and Development Through Explainable Machine Learning and NSGA-II: A Case Study of Osmaniye
AU - Fatih Adıgüzel
AU - Enes Karadeniz
AU - Tuna Emir
AU - Ferhat Arslan
AU - Halil Barış Özel
PY - 2026
DA - 2026/05/19
JO - Land
DO - 10.3390/land15050881
UR - https://doi.org/10.3390/land15050881
AB - Land-use planning in ecologically sensitive landscapes requires balancing biodiversity conservation, ecosystem service provision, agricultural production, settlement expansion, and infrastructure demand within a single spatial system. This challenge is particularly significant in Mediterranean environments, where long-term land transformations and increasing development pressures intensify conflicts among competing land-use priorities. Accordingly, the present study develops an integrated spatial zoning and decision-support framework for Osmaniye Province, southern Türkiye. The framework integrates fuzzy multi-criteria evaluation, CatBoost-based machine learning, SHAP-based interpretability, and NSGA-II multi-objective optimization. The workflow followed a sequential decision process in which an expert-derived zoning surface was first established through fuzzy evaluation, reconstructed from continuous spatial predictors using CatBoost, interpreted through SHAP, and refined through NSGA-II under explicit spatial constraints. By using the expert-derived zoning surface as the learning target, the CatBoost stage aimed to evaluate the internal consistency and spatial learnability of the planning logic within a present-day zoning context. The results indicated that the integrated framework distinguished conservation, controlled-use, and development priorities while identifying the key environmental and anthropogenic drivers shaping class-specific zoning outcomes. The final zoning structure allocated 37.9% of the study area to conservation, 43.6% to controlled use, and 18.5% to development. The study shows that by including a transitional zone with varying proportions of conservation, controlled use, and development, a more balanced distribution among the three goals can be achieved compared to a fixed partition into these three zones. The findings further demonstrate that this approach is more effective than current zoning, which does not accommodate such trade-offs.
SN - 2073-445X
ER -