Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 6, 2025

Improper management of soil resources leads to the destruction organic carbon (SOC) stock and, as a result, reduction quality, well accelerating process climate change through release SOC into atmosphere. This study was conducted evaluate potential different simulation models map spatial variability affected by land use in area Qarasu watershed Kermanshah province, west Iran. Map sampling points prepared using Latin hypercube method. A total 168 observation were selected and profile dug described these points. The samples taken horizon determine content laboratory. mapped kriging geostatistical method area. changes simulated multivariate analysis machine learning methods including generalized linear model (GLM), additive (LAM), cubist, random forest (RF), support vector (SVM) models. Comprehensive measurement data is utilized develop validate predictive Predictor variables included 16 topographic two vegetation, six parent material, four climatic variables. In-depth statistical analyses are proposed performance. results showed that ranged from 0.19 8.44 percent uses. spherical variogram with MAE = 0.41 best fits interpolate ordinary LAM estimated wider range (SOC 0.18–4.82%) among model. However, RF (R2 0.64 RMSE 0.58%) most accurate predicting quantity comparing other It can be used reliable predict similar semiarid regions West Asia Among predictor variables, material's intrinsic properties topography had greatest effect variability.

Язык: Английский

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Variability analysis of soil organic carbon content across land use types and its digital mapping using machine learning and deep learning algorithms

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(5)

Опубликована: Апрель 10, 2025

Язык: Английский

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A Survey of Data Collaborative Sensing Methods for Smart Agriculture

Internet of Things, Год журнала: 2024, Номер 28, С. 101354 - 101354

Опубликована: Авг. 28, 2024

Язык: Английский

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Insights of Machine Learning Approach for Soil Fertility Assessment and Management Strategy

Communications in Soil Science and Plant Analysis, Год журнала: 2024, Номер unknown, С. 1 - 22

Опубликована: Окт. 20, 2024

Язык: Английский

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Aplicaciones de la inteligencia artificial en el monitoreo y conservación ambiental: una revisión exploratoria

REVISTA AMBIENTAL AGUA AIRE Y SUELO, Год журнала: 2024, Номер 15(2), С. 48 - 68

Опубликована: Сен. 27, 2024

Este artículo explora el uso de la inteligencia artificial en vigilancia y preservación del agua, aire suelo. El análisis examinó estudios revisador por pares publicados entre 2020 2024, con un enfoque específico contribución a mejora las técnicas gestión ambiental. procedimiento selección se limitó treinta tres investigaciones pertinentes, que clasificaron dominios principales, calidad suelo, contaminación monitoreo ambiental, aplicaciones IA. Las artificial, incluido aprendizaje automático profundo, muestran gran potencial para mejorar precisión predicciones optimizar asignación recursos varios campos ambientales. Los usos principales esta tecnología son evaluar predecir los niveles gestionar hídricos. La integración IA métodos convencionales eficacia Sin embargo, existen dificultades continuas garantizar confiabilidad datos, capacidad modelos aplicarse diferentes escenarios exitosa estos diversas situaciones. ha demostrado su generar cambios significativos conservación medio ambiente. posteriores deberían dar prioridad ampliación conjuntos incorporación tecnologías desarrollo resolución consecuencias socioeconómicas, fin aprovechar al máximo abordar cuestiones ambientales complejas.

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