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Advanced Machine Learning Methods as a Planning Strategy in the Capellanía Wetland

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  • Oscar Armando Cáceres Tovar

    (Facultad Ingeniería, Seccional Bogotá, Universidad Libre de Colombia, Bogotá 111711, Colombia)

  • José Alejandro Cleves-Leguízamo

    (Escuela de Administración Empresas Agropecuarias, Facultad Seccional Duitama, Universidad Pedagógica y Tecnológica de Colombia, Duitama 150461, Boyacá, Colombia)

  • Gina Paola González Angarita

    (Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, Colombia)

Abstract

This study evaluated the spatio-temporal dynamics of vegetation cover in the Capellanía wetland (Bogotá, Colombia) between 2013 and 2032 through spectral indices, machine learning, and spatial simulation. A multitemporal Random Forest model (R 2 = 0.991; RMSE = 0.0214; MAE = 0.0127) was integrated with cellular automata (MOLUSCE) to project vegetation trajectories under different urban growth scenarios. NDVI-based classification revealed a marked transition: degraded classes (bare soil and sparse vegetation) decreased from over 80% in 2013 to less than 10% in 2032, while moderate and dense vegetation surpassed 90%. Cellular automata achieved moderate agreement (Kappa = 0.640) and high internal calibration (pseudo-R 2 = 1.00); the transition matrix in scenario II, simulating the construction of the Avenida Longitudinal de Occidente (ALO), indicated a conversion 0→1 = 0.414 and persistence 1→1 = 0.709, evidencing intense urbanization pressure in peripheral areas. The Shannon index confirmed recovery but highlighted structural homogenization, underscoring the need to preserve heterogeneity to sustain ecosystem resilience. Scenario analysis showed that the ALO would act as a catalyst for urban expansion, threatening ecological connectivity and increasing pressure on vegetation. Overall, this study provides quantitative, spatial, and prospective evidence to promote preventive, integrated, and data-driven approaches for the conservation of strategic urban wetlands.

Suggested Citation

  • Oscar Armando Cáceres Tovar & José Alejandro Cleves-Leguízamo & Gina Paola González Angarita, 2025. "Advanced Machine Learning Methods as a Planning Strategy in the Capellanía Wetland," Sustainability, MDPI, vol. 17(18), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8462-:d:1754276
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    References listed on IDEAS

    as
    1. Jian Zhang & Xiaoqian Liu & Yao Qin & Yaoyuan Fan & Shuqian Cheng, 2024. "Wetlands Mapping and Monitoring with Long-Term Time Series Satellite Data Based on Google Earth Engine, Random Forest, and Feature Optimization: A Case Study in Gansu Province, China," Land, MDPI, vol. 13(9), pages 1-25, September.
    2. Rizwan Muhammad & Wenyin Zhang & Zaheer Abbas & Feng Guo & Luc Gwiazdzinski, 2022. "Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China," Land, MDPI, vol. 11(3), pages 1-24, March.
    3. Gastón Antonio Ballut-Dajud & Luis Carlos Sandoval Herazo & Gregorio Fernández-Lambert & José Luis Marín-Muñiz & María Cristina López Méndez & Erick Arturo Betanzo-Torres, 2022. "Factors Affecting Wetland Loss: A Review," Land, MDPI, vol. 11(3), pages 1-43, March.
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