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 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.

Applying Artificial Neural Network Based on Backpropagation Method for Indonesian Sign Language Recognition

“…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).…”

“…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.…”

Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods

“…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).…”

Landslide susceptibility modeling and mapping at Dien Bien province, Vietnam using Bagging based MLP neural network

“…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].…”