## 1 Introduction
The information obtained from the testing of boreholes is detailed but of a limited extent, and extending this information to a larger area is extremely resource-consuming. Classic methods have been used to identify liquefaction, relying mainly on empirical correlations and probabilistic models such as Bayesian networks and Monte Carlo simulations based on geotechnical and seismic data. While powerful in developing the risk assessment process, these techniques were afflicted with assumptions about data distribution and how uncertainties are propagated [17]. Recent contributions to the subject include those of [PERSON], [PERSON], & [PERSON], who designed a hybrid ANN optimized with metaheuristic techniques for the probabilistic prediction of liquefaction. They proved that the used of artificial neural networks combined with advanced optimization approaches led to a large boost in predictive accuracy relative to standard models [18]. [PERSON] et al. studied machine learning-based liquefaction prediction using seismic features with geotechnical datasets with hybrid models integrating SVM and RF, reaching an overall accuracy of 97%. The results reflect an ever-increasing contribution of machine learning in predictive reliability advancement [19]. [PERSON] et al. developed continuous-discrete hybrid Bayesian network models and applied them to the the assessment of surface wave velocity (Vs) datasets for earthquake-induced liquefaction evaluation. This model was developed based on Bayesian inference methods that enhance the probabilistic evaluation, considering the uncertainty in soil and seismic properties discussed in [20]. [PERSON] and [PERSON] conducted a comprehensive analysis of machine learning applications in liquefaction research, outlining the evolution of hybrid machine learning methodologies from 1994 to 2021. Their analysis highlighted the necessity of integrating multiple ML algorithms into a single framework to enhance predictive robustness [21]. The most recent contributions were made by [PERSON] et al., who proposed an ANN model whose hyperparameters are optimized for liquefaction susceptibility prediction. Their works underlined how relevant hyperparameter tuning is in improving model accuracy when applied to seismic and soil datasets [22]. On the other hand, [PERSON] et al. explored the application of Extreme Learning Machines (ELMs) with dingo optimization algorithms and concluded that optimization-based hybrid models may enhance liquefaction prediction [23]. The role of ensemble learning frameworks was reinforced by [24]. Their study developed ensemble models that employed different ML methods to enhance the generalization and robustness of model performance predictions. [PERSON] et al. also presented a hybrid ML-based liquefaction assessment framework using cone penetration test data, which showed that deep learning combined with hybrid learning significantly improved the prediction accuracy for liquefaction-prone areas [25]. Probabilistic approaches still play a vital role in liquefaction risk assessment. [PERSON] et al. proposed a probabilistic energy-based machine learning model considering uncertainty quantification to improve reliability [26]. [PERSON] and [PERSON]. developed a hybrid computational framework using fuzzy logic and ML in liquefaction risk assessment [27]. From empirical and probabilistic models, the development of liquefaction identification methodologies has progressed to advanced machine learning and hybrid computational approaches, enhancing prediction capabilities considerably. However, these traditional methods have serious limitations. Therefore, to cope with these challenges, the approach presented in this paper leverages a deep learning-based method [28] for liquefaction identification in the Rif region of Morocco. It has a key novelty by introducing an autoencoder that learns a compact 16D latent representation of input data. The proposed autoencoder model captures complex nonlinear relationships in liquefaction potential.