Landslide susceptibility assessment in expansion areas of the rapidly growing city of Cuenca (Ecuador)

Napoli, Mariano Di; Corona, Mariagiulia Annibali; Guerriero, Luigi; Miele, Pietro; Sellers, Chester; Martire, Diego Di

doi:10.3301/rol.2022.07

Cited by 1 publication

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of geology, the area of Cuenca city corresponds to an intramountainous Andean basin. Materials consist of a volcano-sedimentary series, with deposits of detrital materials, such as conglomerates, sands, silts, and clays, of Miocene-Pliocene age, interspersed with volcanic materials, such as andesitic and rhyolitic tuffs [40,58,60,61]. An important detail is that, in the study area, more than 75% of the landslides recorded in the inventory were rotational [55,58,59].…”

Section: Study Areamentioning

confidence: 99%

“…Model performance should be evaluated using training and test datasets [35,76]. In this research, the dataset was divided into 70% training and 30% test, which is a common ratio [18] evidenced in several studies [28,29,32,40,47]. The main consideration for generating datasets in this research was to obtain points by random sampling in areas without landslides (labeled as 0) and areas with landslides (labeled as 1), based on rotational landslides and areas without landslides.…”

Section: Extraction Of Training and Test Datasetsmentioning

confidence: 99%

“…In addition to the aforementioned algorithms, other methods have been applied, such as maximum entropy (MaxEnt), which is a multivariate model useful for generating predictions from incomplete information [40]. In that sense, several studies related to natural risk management have been published; for example, Lin et al [41] applied MaxEnt to predict landslide-prone urban areas in Shenzhen (China), ensuring that there was no sampling bias.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador

et al. 2022

Self Cite

View full text Add to dashboard Cite

Natural hazards generate disasters and huge losses in several aspects, with landslides being one of the natural risks that have caused great impacts worldwide. The aim of this research was to explore a method based on machine learning to evaluate susceptibility to rotational landslides in an area near Cuenca city, Ecuador, which has a high incidence of these phenomena, mainly due to its environmental conditions, and in which, however, such studies are scarce. The implemented method consisted of an artificial neural network multilayer perceptron (ANN MLP), generated with the neuralnet R package, with which, by means of different backpropagation algorithms (RPROP+, RPROP−, SLR, SAG, and Backprop), five landslide susceptibility maps (LSMs) were generated for the study area. A landslide inventory updated to 2019 and 10 conditioning factors, mainly topographical, geological, land cover, and hydrological, were considered. The results obtained, which were validated through the AUC-ROC value and statistical parameters of precision, recall, accuracy, and F-Score, showed a good degree of adjustment and an acceptable predictive capacity. The resulting maps showed that the area has mostly sectors of moderate, high, and very high susceptibility, whose landslide occurrence percentages vary between approximately 63% and 80%. In this research, different variants of the backpropagation algorithm were implemented to verify which one gave the best results. With the implementation of additional methodologies and correct zoning, future analyses could be developed, contributing to adequate territorial planning and better disaster risk management in the area.

show abstract