## 1 Introduction  
There is a wide variety of phenomena that can trigger slope instabilities ( landslides or rock falls), such as intense rainfall, rapid snowmelt, human-induced activities, or seismic events. These phenomena affect many areas of the world and are highly newsworthy, particularly those triggered by the seismic action of earthquakes, due to their great destructive potential. At present, the losses due to earthquake damage are difficult to approximate. Traditional methodologies estimate such damage costs based on building repair/reconstruction, without valuing economic losses due to the loss of economic activity and human lives [1]. Therefore, minimizing the effects and phenomena associated with strong ground motions is needed to characterize seismic hazards in inhabited regions. This is performed based on the study of past events and the geological and geotechnical conditions of areas that are likely to experience a seismic event, so that the inherent seismic hazard can be estimated. Co-seismic landslides, as secondary effects of earthquakes [2], play a crucial role in identifying past seismic events and in improving seismic hazard predictions. These landslides provide valuable real-time geological evidence, allowing scientists to reconstruct the seismic catalog of a region and gain a better understanding of past seismic activity [3]. By expanding the dataset available for seismic analysis, greater accuracy in seismic hazard predictions can be achieved, which in turn contributes to strengthening community resilience against future seismic events. The 2011 Lorca earthquake occurred in the Murcia Region (SE Spain) with a moment magnitude (M\({}_{\text{w}}\)) of 5.1 [4]. This moderate seismic event caused numerous material losses and injuries, as well as many slope instabilities around the Lorca Basin, compared to other earthquakes with instrumental evidence [5]. Detailed slope instability inventories were carried out to obtain the relationships with different conditioning factors [6; 7]. Such instability inventories allow for the development of landslide hazard maps that reflect the likelihood of a threat. This is essential for the development of contingency plans by response organizations and is also a useful tool in planning the vulnerability of the threat [8]. Numerous research studies were conducted on regional landslide susceptibility mapping (e.g., [7; 9; 10; 11; 12]), as well as investigations focused on landslide susceptibility analysis using machine learning techniques (e.g., [13; 14; 15; 16]). However, there are not many studies that validate the results obtained using physical-based methodologies, such as the Newmark method [17] and limit equilibrium methods. In this context, the present paper shows and contrasts different methods to ensure their reliability and objectivity in the evaluation of co-seismic landslide hazard maps. A comparative methodology between advanced statistical tools and physical methods was carried out. To do this, co-seismic landslides triggered by the Lorca earthquake were used since the data available are abundant. This highlighted the true usefulness and reliability of these methods in improving the evaluation of co-seismic landslide hazard maps. It should be noted that, although the Lorca earthquake has been deeply studied [11; 18], there is currently no similar research on the same area and with the same objective. Four different machine learning models were applied as follows: logistic regression, random forest, artificial neural network, and support vector machine. Logistic regression (LG) achieved very satisfactory results in the susceptibility analysis at regional and local scales. The LG technique was applied [19] to landslide inventory data from El Salvador to obtain a landslide susceptibility map. This demonstrated that the model fit the data efficiently, and it was found that the terrain roughness and lithology were the best factors for estimating landslide susceptibility in the area studied.