2022
DOI: 10.3390/rs14143495
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: 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 n… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
28
1

Year Published

2022
2022
2024
2024

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 25 publications
(30 citation statements)
references
References 86 publications
1
28
1
Order By: Relevance
“…Similarly, update bias and its weight for input i = 1, 2, ..., n and hidden layer Z j where j = 1, 2, ..., p and k = 1, 2, ..., m using Equation (17).…”
Section: Methodology a The Procedures Of Backpropagation Ann Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…Similarly, update bias and its weight for input i = 1, 2, ..., n and hidden layer Z j where j = 1, 2, ..., p and k = 1, 2, ..., m using Equation (17).…”
Section: Methodology a The Procedures Of Backpropagation Ann Algorithmmentioning
confidence: 99%
“…Hossain et al [16] utilized ANN and developed an intelligent model-based backpropagation to recognize predefined Latin characters subsequently. Bravo-López et al [17] explored an ANN model for landslide susceptibility evaluation at a high land in Ecuador. They implemented a technique consisting of an ANN and multilayer perceptron and generated them with one of the R packages.…”
Section: Related Workmentioning
confidence: 99%
“…This research is the continuation of a study previously carried out [46] whose objective is to analyze different ML techniques to determine the one with the best results in terms of susceptibility in the study area (a sector around the urban area of Cuenca, Ecuador), which has no previous studies of landslide susceptibility analysis considering the proposed methods. In this study, a methodology was applied to evaluate which are the most important conditioning factors from a set of 15 factors, based on the implementation of feature selection algorithms (CART, RFE, RF, and Boruta), which have rarely been applied so far to determine relevant factors in LSM generation, and multicollinearity methods were traditionally applied (Variance Inflation Factor VIF and correlation analysis).…”
Section: Introductionmentioning
confidence: 99%
“…Out of these, models based on machine learning (ML) are recently wellknown as the highest advanced statistical based models for better modeling and mapping of landslide susceptibility [6]. Popular ML models used for landslide susceptibility modeling are Support Vector Machines [7,8], Artificial Neural Networks -ANN [9,10], Decision Trees [11,12], and Random Forests -RF [13,14]. More recently, hybrid/ensemble models which are combinations of single ML models and different optimization techniques are considered as better tools compared with single ML models for landslide susceptibility modeling [15].…”
Section: Introductionmentioning
confidence: 99%