Seismic and geomorphic assessment for coseismic landslides zonation in tropical volcanic contexts

Abstract

The Poás volcano is an active volcano of Costa Rica with intense tectonic activity in its flanks. Historically, the volcano has presented strong, surficial earthquakes provoking many landslides with associated casualties and immense economic impacts. One example is the Cinchona earthquake in 2009 (Mw 6.2 and 4.6 km depth). We aim to determine a landslide zonation according to seismic data and a geomorphic assessment in the NW sector of the Poás volcano based on a combination of qualitative methods and morphometric parameters. The results estimate the possible outcome of a 6.8 Mw earthquake. The susceptibility mapping and models showed a positive relationship between this methodology and others previously developed for the area that surrounds the Poás volcano as well as a correspondence with the landslides caused by the Cinchona earthquake in 2009. The final coseismic landslides susceptibility zoning indicated that 52% of the area is prone to landslides. Furthermore, there is a relationship between the areas with high exposure to landslides with conical volcanic geomorphologies, active faulting structural and/or tectonic geomorphological units with slopes greater than 15°. The proposed zoning can be useful for land use planning and by civil protection entities to orientate quick response and reduce the impact of future landslides. Moreover, this method can be applied in active tectonic and volcanic areas worldwide.

Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

“…The effectiveness and efficiency of an ML solution are determined by the quality and type of data as well as the performance of the learning algorithms [66]. Worldwide, researchers employed methods such as the AHP [14,15], F-AHP [15], fuzzy logic [16], frequency ratio [17], analytical network process [18], FROC [19], and Mora-Vahrson-Mora (MVM) [20,21] for the mapping of landslide-susceptible zones. Researchers also applied artificial intelligence (AI)/machine learning (ML) models such as support vector machine (SVM) [22][23][24], Naïve Bayes (NB) [25][26][27], decision tree [28][29][30], K-nearest neighbor [31,32], random forest (RF) [33,34], adaptive neuro-fuzzy inference system (ANFIS) [35], convolutional neural network (CNN) [36][37][38], artificial neural network [39,40], logistic regression [41][42][43], support vector regression [44,45], recurrent neural network [36,37], Adaptive Boosting [46], extreme gradient boosting [35], Random Subspace (RSS) [47], Reduced Error Pruning Tree (REPTree) [48], etc., for landslide susceptibility modeling.…”