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Therefore, the study concluded that the RF, CART, and GTB are machine learning modeling algorithms that were proposed to be applied and to produce a land degradation map in the study area. The overall accuracy of the results of the comparison and evaluation of machine learning modeling on the SVM, MD, CART, GTB, NB, and RF algorithms in the study area are 52.8, 34.5, 81.2, 85.8, 36.3, and 86.2%, respectively. The performance of the model was compared using three accuracy assessment processes (producer accuracy, user accuracy, and overall accuracy). During the modeling phase, 70% of the reference data were divided into training and 30% into validation. The prediction results from these modeling algorithms can be compared and evaluated to get the most optimal performance and accuracy. Support Vector Machine (SVM), Minimum Distance (MD), Classification and Regression Trees (CART), Gradient Tree Boost (GTB), Naïve Bayes (NB), Random Forest (RF) are machine learning modeling algorithms used to predic and map land degradation in the study area. This study’s novelty model is that it is the first to integrate the six main variables of multi-source geospatial data-topographical, biophysical, bioclimatic, geo-environmental, global human modification, and accessibility-in predicting and mapping the potential of land degradation in Indonesia’s tropical environment. The existence of environmental problems in the study area can be identified based on land degradation. The study area is located in the Batanghari watershed, Sumatra, Indonesia, which is a tropical environment. In the present study, the Geospatial Artificial Intelligence (Geo-AI) is proposed to overcome challenges and phenomena related to land degradation identification in the field by integrating multi-source geospatial data and machine learning modeling techniques.
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String found in binary or memory: erts.digic ert.com/Di giCertTrus tedG4RSA40 96SHA256Ti meStamping CA.crt0 String found in binary or memory: erts.digic ert.com/Di giCertTrus tedG4CodeS igningRSA4 096SHA3842 021CA1.crt 0 String found in binary or memory: erts.digic ert.com/Di giCertAssu redIDRootC A.crt0E 5.dr, Unc onfirmed 5 96909.crdo wnload.5.d r String found in binary or memory: syndicatio n.org/2006 /appsynapp licationap uputil.cpp upgradeexc lusivetrue enclosured igestalgor URLs found in memory or binary data Source: ca6a626c-a a28-414b-9 1e0-d65571 f3f31f.tmp. It is a reliable application with a wide range of creative tools and provides support built-in with a music player, where users can play the converted file with ease. You can also download 4K YouTube to MP3 2020.ĤK YouTube to MP3 2021 is a perfect solution using which users can convert their video into audio with the same high quality without facing any issues. 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This is understandable considering the fact that your friends on Facebook probably add thousands of posts on a daily basis. While in the future the video may still be available on Facebook, locating it can be tough. Saving a video from Facebook to iPhone or iPad will give you the ability to view the videos anytime you feel like viewing them. How to Save a Video from Facebook to iPhone? We will show you the top solutions you can use to save interesting videos to your iOS device. If you read on, you will know how to save a video from Facebook to iPhone. Irrespective of the reason why downloading Facebook videos is impossible for you, you will want a solution to “how to save a video from Facebook to my iPhone”. Has updated their system to keep users from downloading videos. Your iPhone is interfering with the Facebook downloader installed on your Updated your Facebook downloader for a long time. 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