Land Use and Land Cover Analysis and Prediction Using Machine Learning Approach: A Case Study of Gaibandha District, Bangladesh

Land Use and Land Cover Analysis and Prediction Using Machine Learning Approach: A Case Study of Gaibandha District, Bangladesh | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 15 December 2025 V1 Latest version Share on Land Use and Land Cover Analysis and Prediction Using Machine Learning Approach: A Case Study of Gaibandha District, Bangladesh Authors : Sujoy Dey 0009-0008-5281-4467 [email protected] , S. M. Tasin Zahid , and Pranto Kumar Sarker Authors Info & Affiliations https://doi.org/10.22541/au.176583194.47212572/v1 166 views 114 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Land use and land cover (LULC) change analysis is crucial for sustainable environmental management and policy formulation in Bangladesh's rapidly changing landscape. This study employs Google Earth Engine and machine learning techniques to analyze LULC dynamics and predict future changes in Gaibandha district, Bangladesh, using ESRI Global Land Cover data from 2019 and 2022, with predictions extending to 2025. A Random Forest classifier was developed using multi-temporal satellite imagery, incorporating elevation data from SRTM and temporal variables to model land cover transitions. Nine LULC classes were identified: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, and rangeland. The model achieved high accuracy (>90%) and kappa coefficient (>0.9), validated through hyperparameter tuning and cross-validation across different random seeds. Results reveal significant landscape transformations between 2019 and 2022, with notable transitions from agricultural to built-up areas and changes in vegetation cover. The Shannon diversity index analysis indicates fluctuating landscape heterogeneity over the study period. Transition matrix analysis identified crop-to-built area conversion as a dominant change pattern, reflecting rapid urbanization pressures. The 2025 predictions suggest continued urban expansion and agricultural land conversion, highlighting potential environmental challenges. Model stability over seeds and overfitting analysis indicate the robustness and reliability of the machine learning framework. Feature importance analysis revealed that historical land cover patterns and elevation are primary drivers of change. The methodology demonstrates the effectiveness of cloud-based remote sensing platforms for large-scale LULC monitoring and prediction, supporting evidence-based decision-making for regional development and climate adaptation planning. View paper Published: 06 November 2025 by MDPI in The 9th International Electronic Conference on Water Sciences session Remote Sensing, Artificial Intelligence and New Technologies in Water Sciences Supplementary Material File (water.pdf) Download 2.07 MB Information & Authors Information Version history V1 Version 1 15 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords bangladesh google earth engine land use change prediction modeling random forest remote sensing Authors Affiliations Sujoy Dey 0009-0008-5281-4467 [email protected] Department of Water Resources Engineering, Bangladesh University of Engineering and Technology View all articles by this author S. M. Tasin Zahid Department of Water Resources Engineering, Bangladesh University of Engineering and Technology View all articles by this author Pranto Kumar Sarker Department of Water Resources Engineering, Bangladesh University of Engineering and Technology View all articles by this author Metrics & Citations Metrics Article Usage 166 views 114 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sujoy Dey, S. M. Tasin Zahid, Pranto Kumar Sarker. Land Use and Land Cover Analysis and Prediction Using Machine Learning Approach: A Case Study of Gaibandha District, Bangladesh. Authorea . 15 December 2025. DOI: https://doi.org/10.22541/au.176583194.47212572/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 . 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