Estimación de áreas quemadas en incendios forestales utilizando redes neurales artificiales
Department of Management Information Systems, Faculty of Economics & Administrative Sciences.
email: mhcalp@ktu.edu.tr
Department of Computer Engineering, Faculty of Engineering, Suleyman Demirel University
email: utkukose@sdu.edu.tr
Introducción: Este artículo es el producto de la investigación "Desarrollo de un modelo basado en redes neuronales artificiales para estimar áreas quemadas en incendios forestales", desarrollado en la Universidad Técnica de Karadeniz en el año 2020.
Problema: los incendios forestales son un problema que afecta en gran medida la vida humana y el orden ecológico, dejando problemas a largo plazo. Debe estimarse porque no se sabe cuándo, dónde y cuánto será el incendio en el área.
Objetivo: El objetivo de la investigación es utilizar redes neuronales artificiales para estimar las áreas quemadas en incendios forestales.
Metodología: Se usó un modelo de red neuronal de propagación hacia atrás para estimar las áreas quemadas.
Resultados: Realizamos una evaluación de desempeño sobre el modelo propuesto considerando los valores de regresión, el error de porcentaje absoluto medio (MAPE) y el error de cuadrado medio (MSE). Los resultados muestran que el modelo es eficiente en términos de su estimación de áreas quemadas.
Conclusiones: El modelo de red neuronal artificial propuesto tiene una baja tasa de error y una alta precisión de estimación. Es más efectivo que los métodos tradicionales para estimar áreas quemadas en los bosques.
Originalidad: según nuestro conocimiento, esta es la primera vez que esta información real y única se ha utilizado para construir y probar las estimaciones del modelo y las mejoras que se han realizado para producir resultados más rápido y con mayor precisión que con los métodos tradicionales.
Limitaciones: Dado que existen diferencias regionales sobre las diferentes áreas forestales, es necesario analizar criterios efectivos con respecto a las regiones objetivo.
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