Landslide susceptibility was modelled using a binary artificial neural network (ANN) implemented in RStudio with the neuralnet package, employing resilient backpropagation with weight backtracking (rprop+) (Riedmiller, 1994). The final dataset comprised 24,317 landslide and 80,864 non-landslide samples (105,181 total), merged with DEM-derived predictors (slope, aspect, curvature, TWI, SPI, and land cover) and randomly split into training (70%; 76,017 samples) and testing (30%; 29,164 samples) subsets using spatial random selection (QGIS). A single hidden-layer architecture was adopted following the universal approximation theorem (Hornik et al., 1989; Rosales-Huamani et al., 2020). With 21 input variables, hidden neuron configurations ranging from 5 to 11 were evaluated (upper bound defined as 2n+1, while hyperparameters were kept fixed to minimize overfitting and maintain model stability under rpropbased optimization. The network used a logistic activation function, cross-entropy error, a convergence threshold of 0.01, a maximum of 1x10^8 iterations, random weight initialization, and a single training repetition. 3. RESULTS 3.1 Model Evaluation and Selection Model performance was evaluated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC), computed for both training and testing datasets using the ROCR package. To identify the optimal ANN configuration, multiple models with a single hidden layer and varying numbers of neurons were tested. Performance was assessed based on convergence behaviour, processing time, and AUC values for training and testing datasets. The comparative results are summarized in Table 1. Table 1 - Performance Comparison of Hidden Layers and Neurons Hidden Layers Neuron s Iterations Processin g Time (h) Min Threshold AUC % (Training) AUC % (Testing) 1 5 578,303 6.65 0.010067 087 97.37 95.55 (Best Model) 1 6 1,609,000 16.35 0.177181 674 Nonconvergen ce Nonconvergen ce 1 7 2,175,000 25.52 0.114354 682 Nonconvergen ce Nonconvergen ce 1 8 775,027 10.76 0.010162 271 97.85 92.87 1 9 673,800 9.67 0.010022 379 97.85 94.89 1 10 1,800,123 48.00 0.010140 01 97.82 91.04
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