Diseño de una metodología para la estimación espacial de la eutrofización en el lago de tota: un insumo para la contabilización ambiental usando machine learning y estadística bayesiana
Fecha
Autor corporativo
Título de la revista
ISSN de la revista
Título del volumen
Editor
Compartir
Director
Altmetric
Resumen
This monograph proposes a new methodological approach that jointly implements remote sensing techniques, machine learning (ml) algorithms and bayesian statistics, for the spatial accounting of the phenomenon of eutrophication in lake tota (lt), located in the department of Boyacá (Colombia). The objective is to design an inference model that allows simulating the density of chlorophyll-a from in-situ samples and multispectral data from landsat 8 (l8) satellite images. The execution of this project began with the pre-processing of the information, the scene of interest was selected, with spatial and spectral treatments the atmospheric and topographic anomalies were corrected, the units of measurement were unified, the spatial resolution of the data was improved and water indices were generated to discriminate the area of the water body with the clustering algorithm k-means, the best clusters were evaluated and selected to train the algorithm mlp, which generated a vector of the boundary of the body of water with which the merged images were segmented. Vi and in-situ measurements were configured as explanatory variables of a linear regression model, with which the best vi associated with the in-situ measurements was determined; first spatial approximation of the behavior of the phenomenon. The selected vi was calibrated using the brsvc to simulate the spatial behavior of the data. The evaluation of the brsvc indicated that the results are satisfactory, thus allowing the production of a calibrated image of chlorophyll-a density with which the trophic state of the lt was calculated and spatialized, which turned out to be totally in the mesotrophic phase. The calibrated chlorophyll-a data and the information from the preprocessed satellite images were fitted with the support vector regression (svr) model, which showed a significant fit and allowed the information to be extrapolated to a 15-meter image of spatial resolution, enriching the input and allowing the generation of a chlorophyll-a density map with greater detail for accounting for eutrophication in the lake. Currently, the severe damage caused to the natural characteristics of lt is evident, since in recent decades the conservation of this strategic ecosystem has been suffering direct attacks due to unsustainable practices. The impact of this development is to generate an input that goes deeper into the paradigms of artificial intelligence and bayesian inference, that exposes the advantages in the processes of obtaining environmental information, fast, cheap, periodic, precise and systematic, and that is a resource for decision-making in the planning and organization of the territory, within the framework of compliance with the objectives of sustainable development.