Simulation of land use change by using machine learning based Multilayer Perceptron--Markov Chain model for Mashhad Metropolitan Area, Iran 1990-2030

Simulation of land use change by using machine learning based Multilayer Perceptron--Markov Chain model for Mashhad Metropolitan Area, Iran 1990-2030 | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 January 2025 V1 Latest version Share on Simulation of land use change by using machine learning based Multilayer Perceptron--Markov Chain model for Mashhad Metropolitan Area, Iran 1990-2030 Authors : Sajedeh Baghban Khiabani , Mohammad Rahim Rahnama 0000-0002-4851-6327 [email protected] , and Mohammad Ajza Shokouhi Authors Info & Affiliations https://doi.org/10.22541/au.173768402.25360681/v1 237 views 130 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study utilizes integrated innovative approaches in machine learning modeling to analyze the Earth’s surface and examine urban expansion resulting from human activity, focusing on land use/land cover (LULC) changes in the Mashhad Metropolitan Area (MMA) from 1990 to 2020, with predictions for 2030. Advanced algorithms, including support vector machines (SVMs), Multilayer Perceptron (MLP) neural network, and Markov chains, were employed in the analysis. The results indicate that from 1990 to 2010, built-up areas increased by 51.70%, green spaces by 26.40%, while sand-covered areas decreased by 36.49%. From 2010 to 2020, the growth rate for built-up areas and green spaces slowed, with built-up areas growing by 13.27% and green spaces by 12.29%. The findings project a 15.59% increase in built-up areas and a 6.44% decrease in green spaces by 2030. Model validation was conducted using the Area Under the Curve (AUC) of 0.767 and Kappa coefficient of 0.7589, indicating strong model validity. Furthermore, this research identifies critical factors influencing LULC changes, revealing that the distance from green spaces and proximity to built-up areas are the most significant determinants. By addressing a significant gap in understanding the impact of human activities on urban dynamics and their ecological implications in developing regions such as the MMA, this study contributes meaningfully to urban planning. Supplementary Material File (manuscript.docx) Download 2.07 MB Information & Authors Information Version history V1 Version 1 24 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords land use markov chain model mashhad metropolitan area multilayer perceptron support vector machine Authors Affiliations Sajedeh Baghban Khiabani Ferdowsi University of Mashhad Department of Geography View all articles by this author Mohammad Rahim Rahnama 0000-0002-4851-6327 [email protected] Ferdowsi University of Mashhad Department of Geography View all articles by this author Mohammad Ajza Shokouhi Ferdowsi University of Mashhad Department of Geography View all articles by this author Metrics & Citations Metrics Article Usage 237 views 130 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sajedeh Baghban Khiabani, Mohammad Rahim Rahnama, Mohammad Ajza Shokouhi. Simulation of land use change by using machine learning based Multilayer Perceptron--Markov Chain model for Mashhad Metropolitan Area, Iran 1990-2030. Authorea . 24 January 2025. DOI: https://doi.org/10.22541/au.173768402.25360681/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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