AUTHOR=Zhang Sikui , Bai Lin , Li Yuanwei , Li Weile , Xie Mingli 

TITLE=Comparing Convolutional Neural Network and Machine Learning Models in Landslide Susceptibility Mapping: A Case Study in Wenchuan County

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.886841

DOI=10.3389/fenvs.2022.886841

ISSN=2296-665X

ABSTRACT=Landslides are one of the most widespread disasters and threaten people's lives and properties in many areas worldwide. Landslide susceptibility mapping (LSM) plays the crucial role in the evaluation and extenuation of risk. To date, large quantities of machine learning approaches have been employed to LSM. Lately, a high-level convolutional neural network (CNN) has been applied for the intention of raise the forecast precision of LSM. The primary contribution of the research was to present a model which was based on the CNN for LSM and methodically compare its capability with the traditional machine learning approaches, namely, support vector machine (SVM),  logistic regression (LR), and random forest (RF). Subsequently, we employed  this model in the Wenchuan region, where a catastrophic earthquake happened on 12 May 2008 in China. There were 405 valuable landslides in the landslide inventory, which were separated into a training set (283 landslides) and  validation set (122 landslides). 
Furthermore,11 landslide causative factors were selected as the model's input, and each model's output was reclassified into 5 intervals according to the sensitivity. We also evaluated the model's performance by the receiver operating characteristic (ROC) curve and several statistical metrics, such as precision, recall, F1-score and other measures. Nevertheless, the CNN-based methods achieved the best performance, with the success-rate curve (SRC) and prediction-rate curve (PRC) approaches reaching 93.14% and 91.81%, respectively. The current research indicated that the approach  based on CNN for LSM had both outstanding goodness-of-fit and excellent forecast capacity. Generally, the LSM in our research is capable of advancing the ability to assess landslide susceptibility.