WeChat Mini Program
Old Version Features

    • Landslide Spatial Prediction Using Data-Driven Based Statistical and Hybrid Computational Intelligence Algorithms

    Xia Zhao,Wei Chen,Paraskevas Tsangaratos,Ioanna Ilia, Qingfeng He

    Geocarto International（2025）

    College of Geology and Environment

    Cited 0|Views1
    Abstract
    Optimization of landslide susceptibility model driven by geological environment: a key challenge for disaster reduction in mountainous areas. Xiaojin County in China has complex geology and active hazards, posing a threat to human and economic security. This study evaluated landslide susceptibility in Xiaojin County with high terrain heterogeneity by comparing three data-driven models - WoE, optimized RF, and RBFNetworks. The spatial correlation between 12 conditioning factors and landslides was analyzed, and the multicollinearity between factors was determined, and the significance of the factors was quantified by MDA and MDG, especially for elevation, soil, and distance to roads. The WoE model exhibits exceptional performance (AUC: 0.899 for training, 0.892 for validation), outperforming RF (AUC: 0.880 for training, 0.874 for validation) and RBFNetwork (AUC: 0.866 for training, 0.863 for validation). The results have significant implications for land development and management in Xiaojin County, while also challenging the machine learning paradigm.
    More
    Translated text
    Key words
    Landslide susceptibility modeling,weights-of-evidence,random forest,radial basis function network,GIS
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined