Data and codes for article "A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards"

<b>Authors: Jianhua Chen</b>^{<strong>1</strong>}<b>, Kaihang Xu</b>^{<strong>1, 2</strong>}<b>, Zheng Zhao</b>^{<strong>3</strong>}<b>, Xianxia Gan</b>^{<strong>1</strong>}<b>, Huawei Xie</b>^{<strong>1</strong>}(1 College of Geophysics, Chengdu University of Technology, Chengdu, China; 2 Sichuan Engineering Technology Research Center of Geological Disaster Prevention, Chengdu, China; 3 Institute of Geographic Sciences and Natural Resources Research, China Academy of Sciences, Beijing, China)<b>Instuction: </b>Data and codes for article "A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards". Data and codes for each model and the related instructions are in their respective folders. CNN-1D is for the static evaluation of landslide hazards.SCBR-CA is for predicting future regional landslide hazard scenarios.CNN-CA is a control group of SCBR-CA.<b>Abstract</b>:Predicting landslide hazards benefits geological disaster prevention and control. A novel cellular automaton (CA) integrating spatial case-based reasoning (SCBR), namely SCBR-CA, is proposed in this paper to predict landslide hazards at a local scale. The proposed model not only extracts spatial scene features for computations but also achieves dynamic prediction, which means that only one input is needed to obtain continuous predictions. Experiments were performed in Lushan, Sichuan, China. After using a convolutional neural network (CNN) to obtain the initial static landslide hazard zoning results, the landslide hazard zoning results for 2016-2025 were predicted with the SCBR-CA model. For comparison, a CA combined with a CNN (CNN-CA), was introduced. The area under the curve (AUC) of the receiver operating characteristic curve and Moran's I index were used to assess the performance of the model. The experimental results showed that SCBR-CA yields slightly better AUC and Moran’s I index values than CNN-CA, and the dynamically predicted landslide hazard zoning results are equivalent or superior to those of static zoning, which indicates that the SCBR-CA model effectively predict local landslide hazards.