Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models
The 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 n...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Online Access: | https://www.mdpi.com/2673-4834/4/3/39 |
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| author | Ankush Rani Saurabh Kumar Gupta Suraj Kumar Singh Gowhar Meraj Pankaj Kumar Shruti Kanga Bojan Đurin Dragana Dogančić |
| author_facet | Ankush Rani Saurabh Kumar Gupta Suraj Kumar Singh Gowhar Meraj Pankaj Kumar Shruti Kanga Bojan Đurin Dragana Dogančić |
| author_sort | Ankush Rani |
| collection | DOAJ |
| description | The 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 km<sup>2</sup> in 1990 to 5.6 km<sup>2</sup> in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km<sup>2</sup> in 1990 to 205.6 km<sup>2</sup> in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km<sup>2</sup> in 1990 to 2859.6 km<sup>2</sup> in 2020 before stabilizing at 2898.4 km<sup>2</sup> in 2050. Water landscapes are expected to shrink from 13.4 km<sup>2</sup> in 1990 to 5.6 km<sup>2</sup> 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. |
| first_indexed | 2024-03-10T22:51:29Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2673-4834 |
| language | English |
| last_indexed | 2024-03-10T22:51:29Z |
| publishDate | 2023-09-01 |
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| spelling | doaj.art-b3ad1fa65ab54378be5e2b669e1223e82023-11-19T10:17:51ZengMDPI AGEarth2673-48342023-09-014372875110.3390/earth4030039Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain ModelsAnkush Rani0Saurabh Kumar Gupta1Suraj Kumar Singh2Gowhar Meraj3Pankaj Kumar4Shruti Kanga5Bojan Đurin6Dragana Dogančić7Centre for Climate Change & Water Research, Suresh Gyan Vihar University, Jaipur 302017, IndiaCentre for Climate Change & Water Research, Suresh Gyan Vihar University, Jaipur 302017, IndiaCentre for Sustainable Development, Suresh Gyan Vihar University, Jaipur 302017, IndiaDepartment of Ecosystem Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8654, JapanInstitute for Global Environmental Strategies, Hayama 240-0115, JapanDepartment of Geography, School of Environment and Earth Sciences, Central University of Punjab, VPO-Ghudda, Bathinda 151401, IndiaDepartment of Civil Engineering, University North, 42000 Varaždin, CroatiaFaculty of Geotechnical Engineering, University of Zagreb, 42000 Varaždin, CroatiaThe 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 km<sup>2</sup> in 1990 to 5.6 km<sup>2</sup> in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km<sup>2</sup> in 1990 to 205.6 km<sup>2</sup> in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km<sup>2</sup> in 1990 to 2859.6 km<sup>2</sup> in 2020 before stabilizing at 2898.4 km<sup>2</sup> in 2050. Water landscapes are expected to shrink from 13.4 km<sup>2</sup> in 1990 to 5.6 km<sup>2</sup> 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.https://www.mdpi.com/2673-4834/4/3/39land useland coversustainable developmentCA–Markov modelkappa coefficientfuture forecasting |
| spellingShingle | Ankush Rani Saurabh Kumar Gupta Suraj Kumar Singh Gowhar Meraj Pankaj Kumar Shruti Kanga Bojan Đurin Dragana Dogančić Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models Earth land use land cover sustainable development CA–Markov model kappa coefficient future forecasting |
| title | Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models |
| title_full | Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models |
| title_fullStr | Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models |
| title_full_unstemmed | Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models |
| title_short | Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models |
| title_sort | predicting future land use utilizing economic and land surface parameters with ann and markov chain models |
| topic | land use land cover sustainable development CA–Markov model kappa coefficient future forecasting |
| url | https://www.mdpi.com/2673-4834/4/3/39 |
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