Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

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dc.contributor.authorRani, Ankush
dc.contributor.authorGupta, Saurabh Kumar
dc.contributor.authorSingh, Suraj Kumar
dc.contributor.authorMeraj, Gowhar
dc.contributor.authorKumar, Pankaj
dc.contributor.authorKanga, Shruti
dc.contributor.author?urin, Bojan
dc.contributor.authorDogan?i?, Dragana
dc.date.accessioned2024-01-21T10:51:55Z
dc.date.accessioned2024-08-14T06:47:35Z
dc.date.available2024-01-21T10:51:55Z
dc.date.available2024-08-14T06:47:35Z
dc.date.issued2023-09-18T00:00:00
dc.description.abstractThe main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton�Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study�s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond. � 2023 by the authors.en_US
dc.identifier.doi10.3390/earth4030039
dc.identifier.issn26734834
dc.identifier.urihttp://10.2.3.109/handle/32116/4114
dc.identifier.urlhttps://www.mdpi.com/2673-4834/4/3/39
dc.language.isoen_USen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.subjectCA�Markov modelen_US
dc.subjectfuture forecastingen_US
dc.subjectkappa coefficienten_US
dc.subjectland coveren_US
dc.subjectland useen_US
dc.subjectsustainable developmenten_US
dc.titlePredicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Modelsen_US
dc.title.journalEarth (Switzerland)en_US
dc.typeArticleen_US
dc.type.accesstypeOpen Accessen_US

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