Understanding Landslide Susceptibility in Northern Chilean Patagonia: A Basin-Scale Study Using Machine Learning and Field Data

Abstract

The interaction of geological processes and climate changes has resulted in growing landslide activity that has impacted communities and ecosystems in [d=EngRev]northernnorth of Chilean Patagonia. On 17 December 2017, a catastrophic flood of 7 ×106 m3 almost destroyed Villa Santa Lucía and approximately 3 km of the southern highway (Route 7), the only land route in Chilean Patagonia that connects this vast region from north to south, exposing the vulnerability of the population and critical infrastructure to these natural hazards. The 2017 flood produced a paradigm shift on the [d=EngRev]analysis scalescale of analysis to understand the danger to which communities and their infrastructure are exposed. [d=EngRev]ThusFor this reason, in this study, we [d=EngRev]soughtseek to evaluate the susceptibility of landslides in the Yelcho and Rio Frio basins, whose intersection represents the origin of this great flood. For this, we used two approaches, (1) geospatial data in combination with machine learning methods using different training configurations and (2) a qualitative analysis of the landscape considering the geological and geomorphological conditions through fieldwork. For statistical modeling, we used an inventory of landslides that occurred between 2008 and 2017 and a total of 17 predictive variables, which are geoenvironmental, climatological and environmental [d=R1]triggersdisturbances derived from volcanic and seismic activity. Our results indicate that soil moisture significantly impacted spatial susceptibility, followed by lithology, drainage density and seismic activity. Additionally, we observed that the inclusion of climatic predictors and environmental [d=R1]triggersdisturbances increased the average performance score of the models by up to 3–5%. Based on our results, we believe that the wide distribution of volcanic–sedimentary rocks hydrothermally altered with zeolites in the western mountains of the Yelcho and Rio Frio basin are highly susceptible to generating large-scale landslides. Therefore, the town of Villa Santa Lucia and the “Carretera Austral” (Route 7) are susceptible to new landslides coming mainly from the western slope[d=EngRev]. This a, which requires the timely implementation of measures to [d=EngRev]mitigatereduce the impact on the population and critical infrastructure.

Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile

“…In [99], they use GAM models for the calculation of susceptibility in areas near roads, and here, they note the importance of curvature, like in our study, as an important factor in the calculation of susceptibility. In [100], they also found relevance in the curvature. Finally, in [101], susceptibility maps are used using only the DEM of the zone, holding the results obtained in this work, which also uses satellite imagery.…”

A Novel Approach to Swell Mitigation: Machine-Learning-Powered Optimal Unit Weight and Stress Prediction in Expansive Soils

“…This method entails an iterative exploration of a predetermined grid of hyperparameter values, conducting cross-validation for each combination to identify the optimal set that maximizes model performance. Given the smaller and more manageable hyperparameter spaces of these models, a comprehensive search proves both practical and advantageous [74,84,85]. The study acknowledges that both the GBR and ANN models feature larger and more complex hyperparameter spaces, necessitating more time for optimization.…”

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

“…This approach involves systematically exploring a predefined grid of hyperparameter values, coupled with cross-validation for each combination, to identify the most effective set that enhances model performance. Considering the more compact and manageable hyperparameter spaces of these models, a comprehensive exploration proves both feasible and advantageous [99][100][101]. While acknowledging the larger and intricate hyperparameter spaces of the GBR and ANN models, requiring additional optimization time, the study opts for GridSearchCV for consistency.…”