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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
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The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
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Updated: Jan 31, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Mapeo de activos de infraestructura de transporte en riesgo mediante métodos estadísticos y de aprendizaje automático

Rakesh Salunke1, Sadik Khan2

  • 1Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA. rakesh.salunke@jsums.edu.

Scientific reports
|January 29, 2026
PubMed
Resumen

El mapeo de terraplenes y taludes de carreteras (HWS) vulnerables es crucial para la infraestructura de transporte. Este estudio desarrolló un método de aprendizaje automático para identificar HWS en riesgo, mejorando la gestión de activos y previniendo deslizamientos de tierra.

Palabras clave:
Sistemas de Información Geográfica (SIG)Gestión de activos geotécnicosTaludes de carreterasInfraestructuraAprendizaje automáticoBosque aleatorioSusceptibilidad

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Área de la Ciencia:

  • Ingeniería geotécnica; Gestión de infraestructura de transporte; Sistemas de Información Geográfica (SIG)

Sus antecedentes:

  • Los terraplenes y taludes de carreteras (HWS) son activos de transporte vitales pero a menudo pasados por alto.; Los HWS son susceptibles a deslizamientos de tierra, exacerbados por eventos de lluvia extrema.; El mapeo preciso de HWS vulnerables es esencial para una gestión eficaz de la infraestructura.

Objetivo del estudio:

  • Desarrollar y evaluar modelos de aprendizaje automático para mapear terraplenes y taludes de carreteras en riesgo.; Crear un inventario confiable de HWS vulnerables para la gestión proactiva de activos.; Identificar los factores clave que influyen en las fallas de HWS.

Principales métodos:

  • Se utilizaron Modelos Digitales de Elevación (DEM) de datos de teledetección para derivar factores causales.; Se desarrollaron modelos supervisados de aprendizaje automático, incluido Random Forest.; Se entrenaron modelos utilizando datos geotécnicos, geomorfológicos e hidrológicos, con ubicaciones de fallas de HWS conocidas como verdad fundamental.; Se evaluó el rendimiento del modelo utilizando métricas AUC, F1-score y Precisión.

Principales resultados:

  • El modelo Random Forest logró puntuaciones perfectas (AUC, F1, Precisión = 1.0).; Se determinó un umbral de probabilidad óptimo de 0.75 para equilibrar la precisión de la predicción.; Los factores clave que influyen en las fallas de HWS identificados son la elevación, la distancia a los arroyos, el NDVI y la precipitación.

Conclusiones:

  • El método de aprendizaje automático basado en SIG desarrollado mapea eficazmente HWS vulnerables en grandes áreas.; Este enfoque permite intervenciones específicas y una utilización optimizada de los fondos para el mantenimiento de la infraestructura.; Las agencias de transporte pueden adoptar esta metodología para la gestión estratégica de activos geotécnicos.