Análisis de Alerta Temprana de la extinción de aves, usando Modelos de Machine Learning

15 pages•Published: May 26, 2023

Abstract

This work presents a model to realize early warning of the extinction of birds in Pichincha, considering critical factors as a climatic environmental intervention human and contamination by public and private organisms, opting for wildlife conservation plans. We have achieved a significant improvement in the methodology of trends and patterns of bird extinction with respect to manual projection processes. By using Machine Learning techniques, it is possible to obtain predictive results for early decision making. It describes the theoretical foundation used and the CRISP-DM methodology that was applied; analyze the data base to optimize the data base gradually and finally determine the model more optimally to carry out the prediction of the extinction of birds, validating the predictive model based on the red list of species of endangered birds of Ecuador emitted by the ministry of the environment; we present several conclusions.

Keyphrases: aprendizaje automático, crisp dm, machine learning, minería de datos, modelo de predicción, redes neuronales

In: Estevan Gomez and Nelson Ivan Herrera Herrera (editors). Proceedings of The 2022 International Conference on Digital Transformation and Innovation Technology, vol 15, pages 43-57.

Links:	https://easychair.org/publications/paper/Qbrm
	https://doi.org/10.29007/93gr