首页|Findings on Machine Learning Reported by Investigators at Indian Institute of Te chnology (IIT) Indore [A Comparative Study of Empirical and M achine Learning Approaches for Soil Thickness Mapping In the Joshimath Region (I ndia)]
Findings on Machine Learning Reported by Investigators at Indian Institute of Te chnology (IIT) Indore [A Comparative Study of Empirical and M achine Learning Approaches for Soil Thickness Mapping In the Joshimath Region (I ndia)]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Madhya Pradesh, India, by NewsRx correspondents, research stated, "Precisely determining the thickness of soil, w hich is an essential parameter in environmental modelling, presents difficulties when applied to heterogenic large-scale areas. Current prediction models primar ily concentrate on shallow soil depths and lack comprehensive spatial coverage." Financial support for this research came from Department of Science & Technology (India). Our news journalists obtained a quote from the research from the Indian Institut e of Technology (IIT) Indore, "This study addresses this limitation by presentin g the results of soil thickness assessment along three important roads in the Jo shimath region (Indian Himalaya). Three different methods were examined incorpor ating geological and geomorphological data as input to obtain soil thickness map s: (1) a customized version of the conventional geomorphologically indexed soil thickness (GIST) model, modified specifically for the peculiarities of the resea rch area, (2) the GIST model enhanced by Monte Carlo simulations (GIST-MCS), and (3) the random forest (RF) algorithm integrated with the GIST model (GIST-RF). By quantifying their errors and conducting validation using geophysical tests, t he effectiveness of the models was assessed. Moreover, a critical comparison of the results provided useful insights to understand the peculiarities of the test site and how to adapt the site-specific customization of the models to the loca l features. The results indicate that the GIST model inadequately accounted for the substantial spatial variations in soil thickness observed across the study a rea."
Madhya PradeshIndiaAsiaCyborgsEm erging TechnologiesMachine LearningIndian Institute of Technology (IIT) Indo re
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