Risk Mapping of Groundwater Level in Peatland Area Utilizing a Spatio- Temporal Model with Weight Constructed Based on Minimum Spanning Tree

Risk Mapping of Groundwater Level in Peatland Area Utilizing a Spatio- Temporal Model with Weight Constructed Based on Minimum Spanning Tree | 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 Risk Mapping of Groundwater Level in Peatland Area Utilizing a Spatio- Temporal Model with Weight Constructed Based on Minimum Spanning Tree Utriweni Mukhaiyar, Adilan Widyawan Mahdiyasa, Bagas Caesar Suherlan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4119220/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Space-time extrapolation models are usually constrained to a limited number of observed locations and lack the ability to provide information about the values at unobserved locations. However, integrating these models with spatial interpolation techniques, it is possible to obtain more informative visual representations. The Generalized Space-Time Autoregressive (GSTAR) model, as a multivariate space-time extrapolation model, is often used due to its simplicity. Within the framework of the GSTAR model, a crucial component is the spatial weight matrix, which facilitates the establishment of spatial relationships among different locations. This matrix can be constructed by employing graph theory, particularly Minimum Spanning Tree (MST), as an extension of the model. Additionally, spatial interpolation can be achieved through the utilization of kriging methods, by gridding the observed spatial locations. Although the amalgamation of these two models does not exhibit superior performance compared to univariate time series models in risk mapping, particularly in the context of groundwater level observed in peatland areas within Riau Province, Indonesia, the model can provide more robust conclusions. GSTAR MST kriging prediction interpolation weight matrix Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4119220","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283260112,"identity":"c07ad1d5-c13e-43ae-99ba-e8c2a4d3fbb6","order_by":0,"name":"Utriweni 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