Prediction of Landslide-Susceptible Areas Using Artificial Neural Network Mathematical Model in the Upper Pamplonita River Basin
DOI:
https://doi.org/10.24054/raaas.v15i2.3187Keywords:
Modeling, Artificial Neural Network, landslide, Pamplonita River basinAbstract
The research focused on predicting areas susceptible to landslides in the upper Pamplonita River basin using Artificial Neural Networks (RNA). Principal Component Analysis (PCA) was applied to reduce multicollinearity among variables, resulting in the selection of three principal components (PC1, PC2, and PC3) that captured 87% and 91% of the cumulative variance, respectively. The variables considered included geometric and environmental factors such as slope, curvature, drainage density, and various vegetation indices.
The RNA was implemented using the Keras library of TensorFlow, configured with five hidden layers (82, 5, 125, 126, and 58 neurons) and a dropout rate of 0.32837 to prevent overfitting. Optimization algorithms like Adam and the sigmoid activation function were utilized, with a learning rate set at 0.00012. The model was trained over 500 epochs, achieving an AUC value of 0.98, indicating high precision in predicting susceptibility areas.
The susceptibility evaluation showed that 50% of the study area has high or very high susceptibility to landslides, with areas classified as very high covering approximately 1173 ha and high susceptibility areas covering 716 ha. The moderate, low and very low susceptibility zones cover 15%, 16% and 19% of the area, respectively. This distribution highlights the need for specific mitigation approaches to reduce vulnerability in the most landslide-prone areas.
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