Balancing Method for Landslide Monitoring Samples and Construction of an Early Warning System

Balancing Method for Landslide Monitoring Samples and Construction of an Early Warning System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Balancing Method for Landslide Monitoring Samples and Construction of an Early Warning System Dunlong Liu, Zhaoyang Xie, Dan Tang, Xuejia Sang, Shaojie Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4559186/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract Given that machine learning is adept at uncovering implicit patterns from heterogeneous data sources, it is well suited for predicting landslide deformation with multi-factor monitoring. The sample dataset forms the foundation for training the models, and the quality and quantity of the dataset directly affect its accuracy and generalization ability. However, significant deformation in landslide bodies is relatively rare, leading to an imbalance in the collected sample dataset. To address this issue, this study proposed the genetic algorithm improved multi-classification-genetic-synthetic minority oversampling technique (SMOTE)-algorithm (GAMCGSA). Building on the multi-classification-genetic-SMOTE-algorithm (MCGSA), it integrated genetic algorithms to determine the optimal sampling rate. Based on this rate, new samples were generated, avoiding the creation of a large number of synthetic samples and effectively addressing the issue of sample imbalance. Subsequently, a convolutional neural network (CNN) was employed to process non-image data from multiple sources, resulting in the development of an intelligent landslide warning model. According to the test results, the F1 score of this model reached 84.2% with an accuracy of 90.8%, it possesses strong classification capabilities for both majority and minority classes, especially outperforming many current models (such as TabNet and RF) in classifying minority classes. This indicates that the CNN model has a superior ability to identify large-scale landslides. Based on the developed warning model and utilizing popular development frameworks, geographic information systems, and database technologies, an intelligent landslide monitoring warning system was constructed. This system integrates intelligent landslide monitoring and warning services, and provides scientific and reliable technical support for landslide disaster prevention and reduction. Landslide monitoring Multi-source data fusion Sample equilibrium Artificial intelligence Early warning system Full Text Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Natural Hazards → Version 1 posted Editorial decision: Minor revisions 19 Nov, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers invited by journal 22 Jul, 2024 Editor assigned by journal 11 Jun, 2024 First submitted to journal 10 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4559186","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330327805,"identity":"c553d63e-5d1c-4850-96a7-2e0db40716f0","order_by":0,"name":"Dunlong 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