Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

TLDR: In this article, the authors compared the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran.

Abstract: Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.

Full text

Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural
network model: a comparison between multilayer perceptron (MLP) and radial basic
function (RBF) algorithms
ABSTRACT
Landslide susceptibility and hazard assessments are the most important steps in landslide risk
mapping. The main objective of this study was to investigate and compare the results of two
artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial
basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran.
At first, landslide locations were identified by aerial photographs and field surveys, and a
total of 136 landside locations were constructed from various sources. Then the landslide
inventory map was randomly split into a training dataset 70 % (95 landslide locations) for
training the ANN model and the remaining 30 % (41 landslides locations) was used for
validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude,
land use, lithology, distance from rivers, distance from roads, distance from faults, and
rainfall were constructed in geographical information system. In this study, both MLP and
RBF algorithms were used in artificial neural network model. The results showed that MLP
with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in
landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps
were validated using the validation data (i.e., 30 % landslide location data that was not used
during the model construction) using area under the curve (AUC) method. The success rate
curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and
0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area
under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %),
respectively. The results of this study showed that landslide susceptibility mapping in the Vaz
Watershed of Iran using the ANN approach is viable and can be used for land use planning.
Keyword:
Landslide, Susceptibility, Artificial neural networks, Geographic Information
Systems (GIS), Vaz Watershed, Iran

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Landslide susceptibility mapping at vaz watershed (iran) using an artificial neural network model: a comparison between multilayer perceptron (mlp) and radial basic function (rbf) algorithms abstract landslide susceptibility and hazard assessments are the most important steps in landslide risk" ?

The main objective of this study was to investigate and compare the results of two artificial neural network ( ANN ) algorithms, i. e., multilayer perceptron ( MLP ) and radial basic function ( RBF ) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.