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Soil

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Clivia biosensor: Soil moisture identification based on electrophysiology signals with deep learning.

Biosensors & bioelectronics
Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests tha...

Machine learning in soil nutrient dynamics of alpine grasslands.

The Science of the total environment
As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional ...

Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach.

Chemosphere
The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil sp...

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) prediction model based on limited peat samples using an evolved artificial neural network.

Chemosphere
Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high ...

A hybrid SWAT-ANN model approach for analysis of climate change impacts on sediment yield in an Eastern Himalayan sub-watershed of Brahmaputra.

Journal of environmental management
The current study focuses on analyzing the impacts of climate change and land use/land cover (LULC) changes on sediment yield in the Puthimari basin, an Eastern Himalayan sub-watershed of the Brahmaputra, using a hybrid SWAT-ANN model approach. The a...

Soil data recency: The foundation for harmonizing soil data across time.

Journal of environmental management
Sustainable soil resource management depends on reliable soil information, often derived from 'legacy soil data' or a combination of old and new soil data. However, the task of harmonizing soil data collected at different times remains a largely unex...

Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus.

Environmental pollution (Barking, Essex : 1987)
This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soil following the application of livestock manure in Chinese farmlands by integrating machine learning...

Machine learning prediction on wetland succession and the impact of artificial structures from a decade of field data.

The Science of the total environment
The artificial structures can influence wetland topology and sediment properties, thereby shaping plant distribution and composition. Macrobenthos composition was correlated with plant cover. Previous studies on the impact of artificial structures on...

Path to autonomous soil sampling and analysis by ground-based robots.

Journal of environmental management
Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing...

Development and application of machine learning models for prediction of soil available cadmium based on soil properties and climate features.

Environmental pollution (Barking, Essex : 1987)
Identifying the key influencing factors in soil available cadmium (Cd) is crucial for preventing the Cd accumulation in the food chain. However, current experimental methods and traditional prediction models for assessing available Cd are time-consum...