Digital Mapping of Soil Properties Using Ensemble Machine Learning Approaches in an Agricultural Lowland Area of Lombardy, Italy
Odunayo David Adeniyi; Alexander Brenning; Alice Bernini; Stefano Brenna; Michael Maerker · 2023 · Land
WASTE classifies this as Methods Dead-End · AI classification, approximate
A method or design hit a limitation — check whether the same constraint applies to your setup.
Abstract
Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear and nonlinear ML models in predicting and mapping soil properties in an agricultural lowland landscape of Lombardy region, Italy. We further evaluated the ability of an ensemble learning model, based on a stacking approach, to predict the spatial variation of soil properties, such as sand, sil
Abstract by Odunayo David Adeniyi; Alexander Brenning; Alice Bernini; Stefano Brenna; Michael Maerker, Land (2023) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Is CO 2 an Indoor Pollutant? Direct Effects of Low-to-Moderate CO 2 Concentrations on Human Decision-Making Performance
Negative / Null Result ReportIngestion of Nanoplastics and Microplastics by Pacific Oyster Larvae
Negative / Null Result ReportHealth Effects of Chronic Arsenic Exposure
Negative / Null Result ReportSevere Air Pollution and Labor Productivity: Evidence from Industrial Towns in China
Negative / Null Result ReportLand-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
Negative / Null Result ReportTraffic-Related Air Pollution and Cognitive Function in a Cohort of Older Men
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.3390/land12020494