@article{ref1,
title="Assessment of landslide susceptibility using integrated ensemble fractal dimension  with kernel logistic regression model",
journal="Entropy (Basel, Switzerland)",
year="2019",
author="Zhang, Tingyu and Han, Ling and Han, Jichang and Li, Xian and Zhang, Heng and Wang, Hao",
volume="21",
number="2",
pages="e218-e218",
abstract="The main aim of this study was to compare and evaluate the performance of fractal  dimension as input data in the landslide susceptibility mapping of the Baota  District, Yan'an City, China. First, a total of 632 points, including 316 landslide  points and 316 non-landslide points, were located in the landslide inventory map. All points were divided into two parts according to the ratio of 70%:30%, with 70%  (442) of the points used as the training dataset to train the models, and the  remaining, namely the validation dataset, applied for validation. Second, 13  predisposing factors, including slope aspect, slope angle, altitude, lithology, mean  annual precipitation (MAP), distance to rivers, distance to faults, distance to  roads, normalized differential vegetation index (NDVI), topographic wetness index  (TWI), plan curvature, profile curvature, and terrain roughness index (TRI), were  selected. Then, the original numerical data, box-counting dimension, and correlation  dimension corresponding to each predisposing factor were calculated to generate the  input data and build three classification models, namely the kernel logistic  regression model (KLR), kernel logistic regression based on box-counting dimension  model (KLR(box-counting)), and the kernel logistic regression based on correlation  dimension model (KLR(correlation)). Next, the statistical indexes and the receiver  operating characteristic (ROC) curve were employed to evaluate the models'  performance. Finally, the KLR(correlation) model had the highest area under the  curve (AUC) values of 0.8984 and 0.9224, obtained by the training and validation  datasets, respectively, indicating that the fractal dimension can be used as the  input data for landslide susceptibility mapping with a better effect.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e21020218",
url="http://dx.doi.org/10.3390/e21020218"
}