Innovative AI Approaches to Enhance Land Use Prediction and Management

Innovative AI Approaches to Enhance Land Use Prediction and Management | 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 Innovative AI Approaches to Enhance Land Use Prediction and Management R. Tamilkodi, N. Leelavathy, B. Sujatha, K. Praveen Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6352625/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 In the context of rapid urbanization and growing environmental challenges, land use prediction has become a critical area of study for sustainable development and urban planning. Traditional methods often struggle to handle the complexity and dynamic nature of land use changes. This article explores innovative approaches to enhance land use prediction and management using Artificial Intelligence (AI). It discusses various AI techniques, including machine learning models, deep learning approaches, reinforcement learning, Geospatial AI (GeoAI), hybrid models, and spatial-temporal models, highlighting their potential to improve prediction accuracy. The applications and benefits of AI-driven land use prediction are examined, demonstrating its impact on urban planning, agricultural optimization, environmental conservation, and disaster management. Despite the significant advancements, challenges such as data quality, computational requirements, model interpretability, and integration with policy are identified. Future directions for research and implementation are also discussed, emphasizing the need for high-quality data, improved model interpretability, and effective collaboration between researchers, policymakers, and stakeholders. The article concludes that AI offers promising solutions for enhancing land use prediction and management, contributing to more sustainable and efficient land resource utilization. Land Use Prediction Urban Planning Environmental Conservation Sustainable Development Predictive Analytics Resource Management Data Quality Model Interpretability 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6352625","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451732060,"identity":"376c69af-920a-4cde-bc9e-4d0f78fe81dc","order_by":0,"name":"R. 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