Geomatics and Environmental Engineering, vol. 19, no. 6

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Vol. 19 No. 6 (2025): Geomatics and Environmental Engineering

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Dec 23, 2024
Accepted
Aug 6, 2025
Published
Nov 19, 2025
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Evaluating Spatial-Plan Consistency Through Probabilistic Machine-Learning Land-Use/Land-Cover Suitability: Insights from Bogor Regency, Indonesia

https://doi.org/10.7494/geom.2025.19.6.41
Dinni Sanni Hafidzah
Bandung Institute of Technology, Faculty of Earth Sciences and Technology, Department of Geodetic and Geomatic Engineering, Bandung, Indonesia
https://orcid.org/0009-0008-5440-3995
Sitarani Safitri
National Research and Innovation Agency (BRIN), Research Center for Geoinformatics, KST Samaun Samadikun, Bandung, Indonesia
https://orcid.org/0000-0003-4821-6479
Akhmad Riqqi
Bandung Institute of Technology, Faculty of Earth Sciences and Technology, Department of Geodetic and Geomatic Engineering, Bandung, Indonesia
https://orcid.org/0000-0002-0713-7208

Corresponding Author(s) : Dinni Sanni Hafidzah

dinnisannihafidzah@gmail.com

Geomatics and Environmental Engineering, Vol. 19 No. 6 (2025): Geomatics and Environmental Engineering

Abstract

Sustainable development is contingent upon the efficient management of land resources for resolving spatial challenges such as land-use conflicts and fragmentation. A land-suitability model offers a potential instrument for assessing land-use/land-cover (LULC) consistency with spatial plans. This study employed a data-driven probabilistic approach using a support vector machine (SVM) algorithm and error-correcting output codes (ECOCs) for incorporating 11 physical parameters to generate spatial grids that reflected land-suitability levels. The probabilistic outputs were derived by calibrating SVM decision values using Platt scaling within the ECOC framework, enabling a reliable estimation of class-wise landsuitability probabilities. The model achieved the highest probability value of 0.9952, with an average of 0.8251; this demonstrated its potential for assessing the consistency of land use/land cover with spatial plans. The model exhibited robust performance and substantial agreement between the predictions and actual data, with an overall accuracy of 88.56% and a kappa index of 0.873. Additionally, the study utilized a land-suitability model and non-weighted overlay relevance matrix to identify discrepancies in Bogor Regency’s spatial plan, quantifying the compliant and noncompliant land areas for each LULC class within specified spatial-plan zones. The evaluation revealed a significant misalignment, with 25–45% of agricultural land uses that included wetland and dryland agriculture, plantations, and inland fish farms being allocated within settlement zones; this indicated a mismatch between spatial plans and land suitability. These findings underscored the importance of evaluating and revising the spatial plan to enhance its alignment with land suitability.

Keywords

land suitability probabilistic method spatial plan sustainable land-use planning
Hafidzah, D. S., Safitri, S., & Riqqi, A. (2025). Evaluating Spatial-Plan Consistency Through Probabilistic Machine-Learning Land-Use/Land-Cover Suitability: Insights from Bogor Regency, Indonesia. Geomatics and Environmental Engineering, 19(6), 41–73. https://doi.org/10.7494/geom.2025.19.6.41
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