首页|Research Results from University of Munster Update Knowledge of Machine Learning (High-resolution soil temperature and soil moisture patterns in space, depth an d time: An interpretable machine learning modelling approach)
Research Results from University of Munster Update Knowledge of Machine Learning (High-resolution soil temperature and soil moisture patterns in space, depth an d time: An interpretable machine learning modelling approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New study results on artificial intell igence have been published. According to newsreporting originating from Munster , Germany, by NewsRx correspondents, research stated, "Soil temperatureand soil moisture are key drivers of various soil ecological processes, which implies a significantimportance of datasets including their variations in space, depth an d time (4D)."Funders for this research include German Research Foundation.The news reporters obtained a quote from the research from University of Munster : "Current griddedproducts typically have a low resolution, either spatially or temporally. Here, we aim at modelling andexplaining high-resolution soil tempe rature and soil moisture patterns in 4D for a 400 km2 study area in aheterogene ous landscape. Our target resolution of 10 m in space, 10 cm in depth, and 1 h i n time allowscapturing small-scale variations as well as short-term dynamics. W e used multi-depth soil temperature andsoil moisture measurements from 212 loca tions and linked them to 45 potential predictors, representingmeteorological co nditions, soil parameters, vegetation coverage, and landscape relief. We trained randomforest models that were able to predict soil temperature with a mean abs olute error of 0.93 °C and soilmoisture with a mean absolute error of 4.64 % volumetric water content. Continuous model predictionsenabled a comprehensive a nalysis of 4D patterns and confirmed that the selected resolution is essential to capture soil temperature and soil moisture variations at the landscape scale."
University of MunsterMunsterGermanyEuropeCyborgsEmerging TechnologiesMachine Learning
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