Desarrollo de un modelo predictivo basado en machine learning para la biomasa aérea del bosque seco tropical en el sector suroccidental del área de compensación ambiental del proyecto hidroeléctrico el Quimbo

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dc.contributor.advisorAvella Muñoz, Edgar Andrés
dc.contributor.authorBarreto Puerto, Eddy Santiago
dc.contributor.orcidAvella Muñoz, Edgar Andrés [0000-0002-1595-1154]
dc.date.accessioned2025-05-07T17:13:33Z
dc.date.available2025-05-07T17:13:33Z
dc.date.created2024-04-19
dc.descriptionEl bosque seco tropical (bs-T) es uno de los ecosistemas más vulnerables a nivel global debido a su alto grado de deforestación y fragmentación, lo que resalta su importancia para la conservación de la biodiversidad y su papel crucial en el almacenamiento de carbono. En este contexto, se desarrolló un modelo predictivo utilizando algoritmos de aprendizaje automático (Machine Learning) para estimar la biomasa aérea en las inmediaciones del proyecto hidroeléctrico El Quimbo. El objetivo de este estudio fue identificar el modelo que mejor predijera el stock de biomasa aérea en dicha región. Para ello, se emplearon imágenes satelitales Sentinel-2 junto con diversas variables topográficas, como el Modelo Digital de Elevación (DEM), el aspecto y la pendiente, además de datos de campo para la predicción de biomasa aérea. Se implementaron tres modelos predictivos: XGBoost, Random Forest (RF) y Redes Neuronales Artificiales (ANN). Entre estos, el modelo XGBoost demostró el mejor desempeño, con un coeficiente de determinación (R²) de 0,72, un error medio absoluto (MAE) de 17,38 t ha⁻¹, una raíz del error cuadrático medio (RMSE) de 27,61 t ha⁻¹ y un Huber Loss de 22,62 t ha⁻¹.
dc.description.abstractThe tropical dry forest (bs-T) is among the most vulnerable ecosystems globally due to extensive deforestation and fragmentation. This underscores its critical importance for biodiversity conservation and its essential role in carbon storage. In this context, a predictive model was developed using machine learning algorithms to accurately estimate aboveground biomass in the tropical dry forest near the El Quimbo hydroelectric project. The objective of this study was to identify the model that most accurately predicted the aboveground biomass stock in this region. To achieve this, Sentinel-2 satellite images were utilized alongside various topographic variables, such as the Digital Elevation Model (DEM), aspect, and slope, in addition to field data for aboveground biomass prediction. Three predictive models were implemented: XGBoost, Random Forest (RF), and Artificial Neural Networks (ANN). Among these, the XGBoost model demonstrated the best performance, with a coefficient of determination (R²) of 0.72, a mean absolute error (MAE) of 17.38 t ha⁻¹, a root mean square error (RMSE) of 27.61 t ha⁻¹, and a Huber Loss of 22.62 t ha⁻¹.
dc.description.sponsorshipFundación Natura Colombia
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/95270
dc.language.isospa
dc.publisherUniversidad Distrital Francisco José de Caldas
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dc.rights.accesoRestringido (Solo Referencia)
dc.rights.accessrightsRestrictedAccess
dc.subjectBosque seco tropical
dc.subjectAprendizaje automático
dc.subjectBiomasa aérea
dc.subjectProyecto hidroeléctrico El Quimbo
dc.subjectImagenes satelitales
dc.subject.keywordTropical dry forest
dc.subject.keywordMachine learning
dc.subject.keywordAboveground biomass
dc.subject.keywordEl Quimbo
dc.subject.keywordSatellite images
dc.subject.lembIngeniería Forestal -- Tesis y disertaciones académicas
dc.subject.lembModelos predictivos en ecología
dc.subject.lembAprendizaje automático aplicado a ecosistemas
dc.subject.lembBiomasa aérea y almacenamiento de carbono
dc.subject.lembTeledetección y monitoreo ambiental
dc.titleDesarrollo de un modelo predictivo basado en machine learning para la biomasa aérea del bosque seco tropical en el sector suroccidental del área de compensación ambiental del proyecto hidroeléctrico el Quimbo
dc.title.titleenglishDevelopment of a machine learning-based predictive model for aboveground biomass in the tropical dry forest of the southwestern environmental compensation area of the el Quimbo hydroelectric projec
dc.typebachelorThesis
dc.type.degreeInvestigación-Innovación
dc.type.driverinfo:eu-repo/semantics/bachelorThesis

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