## 5 Discussion  
The proposed hybrid model includes autoencoders and stacked classifiers in liquefaction identification with high perspective, imbalanced handling, and a geographical view of the geophysical data resulting from the Rif region. Indeed, most works on the issue of liquefaction detection adopt only basic approaches belonging either to a family of classification methods or to linear regression, whose shortcomings lie in handling an imbalances corresponding to minimal coverage of events such as liquefaction. While our model follows this route, it learns intricate relationships through feature extraction and uses ensemble methods to combine several learning algorithms' points of view. The proposed model extends previous studies on autoencoders for unsupervised feature learning and also aligns with the work of [PERSON] & [PERSON] [44], who investigated how autoencoders capture nonlinear relationships in geophysical data while improving feature extraction in seismic studies. Based on the work of [PERSON] & [PERSON] [45], batch normalization plays a key role in our framework, stabilizing training and avoiding overfitting to dominant geological features so that rare liquefaction events are not ignored. We integrated an ensemble classifier to enhance the predictive robustness of our approach. We combined nonlinear relationship modeling with MLP and handled class imbalance using XGBoost, following the work of [PERSON] & [PERSON] [46], who showed the efficiency of gradient boosting in classification tasks. However, most geophysical liquefaction models often suffer from missing environmental variables. Similarly, [PERSON], [PERSON] et al. [47] emphasized that local conditions, such as shear wave velocity and soil geometry, are critical in earthquake damage, even for sites very close to each other. Seismically induced soil deformations further influence damage outcomes. Therefore, it is useful to evaluate these factors in seismic risk mitigation, especially in cities, to enhance resilience in seismically active regions. Class imbalance is still a barrier in liquefaction modeling, since rare liquefaction events cannot compete against more frequent geological occurrences. This fact, as pointed out by [PERSON] et al. [48], leads to inflated overall accuracy at the expense of significantly worse performance regarding minority-class event detection. Our model, employing techniques  
Figure 21: Global sensitivity analysis using Sobol First-order indices. such as batch normalization and an ensemble-based stacking classifier, overcomes this issue by being sensitive to these rare events of liquefaction. Consequently, we can improve this model by including real-time seismic data or environmental inputs such as soil moisture changes caused by climate change. Although the autoencoder performs well in portraying raw data in compact form, much newer deep learning architectures like variation autoencoders and generative models might be considered in its place, apt for strengthening model learning of much subtler representations. This could extend the model further to include other seismically active regions where data and the distribution of influencing factors may vary, in addition to being helpful in bringing out its generalizability and robustness. While the proposed model shows good performance, it suffers from some limitations. First, the real-world sample is small and highly unbalanced, with only two liquefaction cases, which reduces the statistical strength so that the model may not fully capture the range of field conditions. Although synthetic data were used to supplement training and measure generalizability, synthetic situations cannot perfectly mimic the complexity of real earthquake settings. Additionally, the model's success may change when applied to different geographic regions or instrument setups. Future work should focus on increasing real-world data collection and proving the model across various regions and earthquake events.