Slope stability analysis is a key branch of geotechnical engineering, a necessary tool for hazard prediction . It is essential, along with other related knowledge areas such as debris flow , to mitigate landslide risks that cause billions of dollars in economic damage annually and threaten lives and infrastructure. Traditional limit equilibrium methods (LEMs), such as Bishop's Simplified Method and Morgenstern-Price , and numerical techniques such as the Finite Element Method (FEM) and Strength Reduction Method (SRM), rely on assumptions about slip surfaces, soil homogeneity, and force equilibrium that struggle with uncertainties in soil properties (cohesion c), friction angle (ϕ), unit weight (γ), geometry (height H), angle (β), pore pressure (ru), and external factors (seismicity, anthropic actions). c, ϕ, and ru exhibit significant spatial-time variations , making it challenging to choose typical values for analysis, although they are related to the soil formations of the site and cannot be considered purely random. Several works recognize the importance of spatial correlation of geological properties . Furthermore, data scattering is linked to the volume of soil mobilized in a test and the smaller the specimen, the greater the variability . Probabilistic methods such as Monte Carlo simulation and the first order second moment (FOSM) aim to mitigate some of these deficiencies, representing the variability and uncertainty inherent to natural geological structures, despite facing challenges in modeling the complex non-linear interactions that are inherent of the soil/rock masses . Furthermore, the interactions between soil and environment, anthropic factors, and the scale of the simulation have a significant impact on the probability of slope instability events .

Data-driven based methods, including machine learning (ML), deep learning (DL), and soft computing techniques can leverage simulations, field data, and literature to model slope stability. By predicting the Factor of Safety (FoS) or classifying stability status (e.g., stable, unstable), these approaches offer a powerful, data-centric solution for municipal decision-making in risk-prone areas, particularly when handling large, complex databases. Although many of the published papers deal with ML for FoS prediction , ML techniques have proven applicable in a wide range of geotechnical problems, including landslide susceptibility mapping , illustrating the versatility of ML and identifying opportunities for transference of cross-disciplinary knowledge and enhancement of slope stability analysis in large areas. In this regard, the ML models Random Forest and XGBoost are particularly valuable as they combine multiple learners, reducing overfitting while enhancing generalization in cases with substantial data uncertainty .

However, geohazard research on landslide prediction spans four distinct but often conflated categories that differ fundamentally in their temporal orientation and operational intent, and whose distinctions are essential for accurately positioning the present contribution. Landslide susceptibility mapping estimates the spatial propensity of terrain to experience slope failure based on time-invariant intrinsic factors, without reference to any specific triggering event or time period , and while ML has advanced this field considerably , susceptibility maps carry no temporal probability statement and cannot support time-sensitive warnings. In the Brazilian context, susceptibility mapping remains geographically concentrated in southeastern states, with northeastern Brazil, including Bahia, comparatively underrepresented in the published literature

Retrospective event classification incorporates time implicitly by pairing historical landslide inventory records with the environmental conditions that prevailed at the time and location of each event, training models to discriminate recorded failure events from stable reference cases ; while methodologically informative, such frameworks yield optimistic performance estimates when spatial autocorrelation between training and test samples is not controlled , and do not demonstrate skill on conditions genuinely unknown at the time of prediction. Nowcasting estimates current or imminent landslide activity in near-real-time by combining static terrain and geotechnical susceptibility information with dynamic observed forcing to produce spatially distributed hazard intensity estimates with a temporal horizon of hours to a few days ; rainfall intensity-duration thresholds represent the most widely deployed nowcasting approach , and integrating terrain susceptibility into spatially variable threshold derivation has been shown to improve nowcasting performance . In the Brazilian context, evaluated the operational performance of the Rio de Janeiro municipal early warning system over four years, providing the most directly relevant precedent for a city-scale nowcasting framework and establishing performance benchmarks against which the present system should ultimately be measured. Operational forecasting extends nowcasting by coupling landslide models with numerical weather prediction outputs to issue probabilistic hazard forecasts with lead times of one to several days, enabling proactive rather than reactive emergency response ; in Brazil, CEMADEN (National Center for Monitoring and Early Warning of Natural Disasters) operates a national early warning framework integrating satellite rainfall monitoring with susceptibility zonation across thousands of municipalities, representing the operational infrastructure within which city-scale systems such as the one developed here must ultimately be situated.

Although ML has experienced an impressive recent boost, only a few cases have applied ML to real-world instability events in an operational nowcasting context. In this paper, a data bank of hydromechanical properties of soils is used in conjunction with a digital terrain model (DTM) and different georeferenced information, including rainfall, vegetation coverage, geological structures, sewage collection/treatment status, and residential density, to predict calls from the population living in risk areas to the municipality reporting soil mass movements or the perception of risk indicators. This paper predicts landslide hazards in Salvador, Bahia, Brazil, using machine learning (ML) on georeferenced data, which could be categorized as “Nowcasting” of landslides. The model is trained using a novel data source: emergency calls from residents in areas at risk in Salvador, Bahia, Brazil, one of the few cities in Brazil with a consistent and reliable data set in this regard. Results from shear strength and permeability tests conducted by the geotechnical laboratories of the Federal University of Bahia over the past few decades are also used, along with the city's digital terrain model (DTM), to calculate FoS, the probability of failure, and the reliability index across the entire urban area. The cumulative rainfall over periods ranging from 1 to 30 d prior to each event was estimated using data from 40 weather stations. This was used in conjunction with the mapped geological structures, including faults, foliations, and dikes, as variables influencing slope stability. As anthropic input layers, the presence of residences at both the top and the foot of the slope, as well as the quality of the sewage service around the slope, were evaluated. The developed framework can be helpful for municipalities in risk management activities and can provide a tool for online risk monitoring using rain data from city weather stations. Furthermore, the nature of ML enables frequent updates to the system, changing the DTM, residence, and sewage layers over time, and adding new emergency calls and rainfall data to the machine learning database. This strong result underscores the viability of machine learning as a powerful tool for this kind of problem, particularly within municipal warning systems.

Figure  summarizes all the obtained results in the machine learning attempts performed. As observed, in all the scenarios, ML was able to deliver a very good and similar performance for the testing and validation Databanks, with values of R2>0.94.

= Table  summarizes the best results obtained for each Ri presented in Fig. , and Figs.  and  illustrate how the slope's radius of influence and RFU impact the ML performance. As observed, Ri had the greatest impact on ML predictive ability, with the highest R2 values obtained for Ri=300 m. Furthermore, the influence of RFU and Ti, indicated by the dispersion bars, decreases with Ri. However, using such a large radius of influence may capture emergency calls from adjacent areas and relatively distant structural aspects of the bedrock, as illustrated in Fig. , where circles with Ri of 150 and 300 m were traced in a typical area of risk in the city. To further analyze the relative size of the employed radii, they were compared with the distances between the centroids of nearest slope areas.

Figure  illustrates the obtained results. As observed, only 24 % of adjacent areas have distances less than 75 m, with 43.1 % of the distance between the nearest areas being less than 100 m.

Using a RFU =20 led to slightly better results for all the Ri. Using a time interval of 4 d, in most cases, resulted in a better performance, although the Ti influence was considered negligible. Figure a presents the ML performance for a Ri=75 m with varying Ti and RFU, chosen as the best option in terms of Ri considering the individuality of each slope area.

Figure  shows the ML performance for a Ri=75 m, Ti=3 d and RFU =20. Although the presence of relatively few scattering points, the ML model presents a similar and excellent performance in both the validation and test phases. In XGBoost, “weight” and “gain” are key concepts related to feature importance and the tree-building process. The weight refers to the number of times a feature is used to split the data across all trees in a given XGBoost model, whereas the gain is linked to the reduction in training loss achieved when using a feature for splitting. The gain quantifies the improvement in the model's performance (reduction in the objective function) achieved by using a particular feature to make splits throughout the ensemble of trees. As observed in Fig. a, the variables related to the slope stability analysis, both in natural and saturated states (probability of failure and FoS), and the different periods of cumulative rainfall, had the highest weight values. The density of residences at the slope toe, the quality of the sewage service, and proximity to the rock's faults also had a significant influence on the obtained results, followed by the density of residences at the slope crest. Figure  compares the feature's weight for different values of Ri. Although the list of parameters with higher weights remains almost unchanged, the relative weight of the sewage service increases with Ri, going from 14th to 8th place when Ri increases from 75 to 300 m. According to Fig. , the input variables for different periods of rainfall presented high values of both weight and gain, meaning that they are often used in splitting data and are effective in improving the model performance. The proximity to the faults in the bedrock, the quality of the sewage service, and the soil permeability tend to form a second tier in terms of variable gain.

Figure  compares the feature's gain for different values of Ri (RFU =20, and Ti=4 d). The cumulative rainfall inputs still tend to present the highest gains. However, the soil permeability and the faults in the bedrock play a first-level role in this case. The faults in the bedrock lose gain as Ri increases, reaching the 2nd place for Ri=75 m and the fourth position for Ri=100 m. The same pattern is observed for the soil permeability, whereas the quality of the sewage service tends to preserve an intermediate ranking. These findings also suggest that using Ri=75 m is a wise choice because it tends to ensure greater effectiveness for variables other than those linked to rainfall. In gradient boosting, variables that have the highest “weight” (the number of times used in a split) may present smaller “gains” (reductions in loss) in later trees because earlier trees have already captured the most significant predictive power of that feature. Gradient boosting is an iterative process where each new tree is built to correct the errors (residuals) of the previous ones. It must be pointed however, that feature importance metrics based on XGBoost weight and gain should be interpreted with caution in the present study, which involves multiple cumulative rainfall as well FoS, probability, and reliability as model variables, with a clear interdependence relationship, which makes accurately accounting for their individual contribution to model performance much complex.

This research paper presents a robust and effective machine learning (ML) framework for the prediction of slope instabilities in the city of Salvador, Bahia, Brazil. The primary purpose was to address the significant challenge of managing slope stability issues in urban areas with complex terrain by developing a data-driven model capable of providing accurate, real-time predictions of soil mass movements. This model was designed to serve as a critical decision-support tool for municipal authorities, enabling proactive risk management so as to ensure public safety. The study's approach involved integrating a comprehensive dataset comprising 13 522 emergency calls (about both confirmed and unconfirmed mass movements) with a digital terrain model (DTM) and extensive geotechnical data. A suite of georeferenced variables was used as input for the ML model, including cumulative rainfall over various time intervals, vegetation cover, key geological structures such as faults and foliations, the status of the sewage collection system, and residential density, both at the slopes's foot and crest. The XGBoost algorithm, a powerful gradient boosting technique, was used to build the predictive model, which was rigorously trained and validated. The results of this study are highly compelling and demonstrate the significant potential of ML in geotechnical hazard prediction. The model achieved an outstanding performance, with an R2 value consistently exceeding 0.94 across various scenarios and reaching a peak of approximately 0.98 during both the validation and testing phases. This exceptional accuracy underscores the model's reliability in forecasting slope instability events. A detailed analysis of feature importance revealed that variables directly related to slope stability, such as the Factor of Safety (FoS) and probability of failure, along with cumulative rainfall and the quality of the sewage service, were the most influential predictors. The model's performance was found to be sensitive to parameters like the slope's radius of influence (Ri) and the weighting of confirmed versus unconfirmed emergency calls (RFU), with an Ri of 75 m and an RFU of 20 yielding physically consistent optimal results. The practical implications of this research, particularly for evacuation alarming in high-risk situations, are profound. The high predictive accuracy of the model provides a solid foundation for developing an effective early warning system. By continuously processing real-time data on rainfall and other dynamic factors, the system can identify areas where the risk of slope failure has surpassed critical thresholds. This enables municipal authorities to issue timely and targeted evacuation orders, thereby minimizing the risk to human life and property. The model's ability to be frequently updated with new emergency call data and weather information ensures its dynamic nature, allowing it to adapt to changing conditions and improve its predictive power over time. Ultimately, this work provides a scalable and efficient tool that can be integrated into existing municipal warning systems, transforming reactive responses into proactive risk mitigation strategies and significantly enhancing the resilience of communities vulnerable to slope instability hazards. Although in its present form, the proposed framework demonstrated a strong ability to reproduce retrospective events in time series, this good performance is usually not sufficient to ensure strong predictive performance. Temporal holdout validation and an interactive implementation of this model are under development, aiming to gradually replace the municipality's current warning and emergency declaration criteria. All the new data produced during this period will help with the model implementation task, using walk-forward validation with Sliding Windows to Test Memory Depth, since most variables tend to evolve (e.g., the quality of the sewage collection system and the geometry of the slopes). Different (at least two) thresholds must be defined in this stage, considering the predicted and experimental values of the output variable: a warning and an emergency state. This is a very delicate phase, because declaring an emergency without confirmed events (false alarms) can jeopardize the entire system, leading the population to gradually discredit it and expose them to risk in future events.