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10.1038/s41598-025-31791-8 http://scihub22266oqcxt.onion/10.1038/s41598-025-31791-8 41354750!?!41354750 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41354750&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Sci+Rep 2025 ; ? (?): ? Nephropedia Template TP {{tp|p=41354750|t=2025. Mapping coastal transformations with a novel Cellular Automata-Markov-Random forest framework for land use change modeling |pdf=|usr=}}{{41354750}} gab.com Text Mapping coastal transformations with a novel Cellular Automata-Markov-Random forest framework for land use change modeling JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=41354750 #FOAvip Coastal areas are dynamic, shaped by natural processes and human activities, making accurate prediction of shoreline and land use changes crucial for sustainable management. This study presents a hybrid modeling framework that combines CA-Markov and machine learning to enhance land use/land cover (LULC) and shoreline change projections in Oman's vulnerable coastal regions. Coastlines were delineated using multi-temporal Landsat images (1997-2006-2015-2024) and the Normalized Difference Water Index, while erosion and accretion rates were quantified using End Point Rate and Linear Regression Rate analyses. Results from 1997 to 2024 show substantial spatial variability, with urban localities such as Rakhyut experiencing significant erosion (-1.81 m/year) and areas like Bawshar showing accretion (1.41 m/year). Coastal LULC changes reveal rapid urban expansion, as seen in Muscat's built-up area, which increased from 10.31 km(2) in 1997 to 116.41 km(2) in 2015. Four models-CA-Markov, CA-Markov + XGBoost, CA-Markov + CART, and CA-Markov + RF-were evaluated for future LULC prediction. The hybrid CA-Markov + RF model achieved the highest predictive performance, increasing overall accuracy from 0.905 (CA-Markov) to 0.935 (CA-Markov + RF) on the test dataset, highlighting the capability of machine learning models. Projections for 2033 indicate continued urban growth, particularly in Salalah and Sohar, alongside reductions in vegetation in arid regions. Twit Text FOAVip Mapping coastal transformations with a novel Cellular Automata-Markov-Random forest framework for land use change modeling ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=41354750 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41354750 #FOAvip English Wikipedia <ref>{{Cite pmid|41354750}}</ref> Mapping coastal transformations with a novel Cellular Automata-Markov-Random forest framework for land use change modeling #MMPMID41354750 Nikoo MR; Zarei E; Al-Wardy M Sci Rep 2025[Dec]; ? (?): ? PMID41354750show ga Coastal areas are dynamic, shaped by natural processes and human activities, making accurate prediction of shoreline and land use changes crucial for sustainable management. This study presents a hybrid modeling framework that combines CA-Markov and machine learning to enhance land use/land cover (LULC) and shoreline change projections in Oman's vulnerable coastal regions. Coastlines were delineated using multi-temporal Landsat images (1997-2006-2015-2024) and the Normalized Difference Water Index, while erosion and accretion rates were quantified using End Point Rate and Linear Regression Rate analyses. Results from 1997 to 2024 show substantial spatial variability, with urban localities such as Rakhyut experiencing significant erosion (-1.81 m/year) and areas like Bawshar showing accretion (1.41 m/year). Coastal LULC changes reveal rapid urban expansion, as seen in Muscat's built-up area, which increased from 10.31 km(2) in 1997 to 116.41 km(2) in 2015. Four models-CA-Markov, CA-Markov + XGBoost, CA-Markov + CART, and CA-Markov + RF-were evaluated for future LULC prediction. The hybrid CA-Markov + RF model achieved the highest predictive performance, increasing overall accuracy from 0.905 (CA-Markov) to 0.935 (CA-Markov + RF) on the test dataset, highlighting the capability of machine learning models. Projections for 2033 indicate continued urban growth, particularly in Salalah and Sohar, alongside reductions in vegetation in arid regions. ?Mapping coastal transformations with a novel Cellular Automata-Markov-(epmc).pdf DeepDyve Pubget Overpricing