AIMC Topic: Soil

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Fuzzy logic-based assessment for mapping potential infiltration areas in low-gradient watersheds.

Journal of environmental management
This paper gives an account of the design a logic-based approach for identifying potential infiltration areas in low-gradient watersheds based on remote sensing data. This methodological framework is applied in a sector of the Pampa Plain, Argentina,...

Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks.

Computational intelligence and neuroscience
In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various t...

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil.

Sensors (Basel, Switzerland)
Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key...

Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of "hot" particles.

The Science of the total environment
The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can pre...

Soil and litter emission sources as important contributors to ozone production from volatile organic compounds in island tropical forests.

Environmental research
While studies have confirmed that volatile organic compounds (VOCs) emitted directly by tropical island forest vegetation significantly influence ozone (O) production and climate change through atmospheric oxidation processes, the environmental effec...

Identifying factors influencing trace metal concentrations in urban residential soil using an optimal parameter-based geographical detector model.

Environmental research
Australia's national citizen science program VegeSafe has collected and analysed over 26,000 residential garden soil samples for their trace metal concentrations, enabling a more comprehensive understanding of the factors influencing contamination. H...

Insights into the comparison of machine learning models on rice grain arsenic prediction: Interplay of rice cultivation systems and soil environmental factors.

Environmental pollution (Barking, Essex : 1987)
Arsenic (As) exposure to rice threatens food safety while transferring As to rice from paddy soils significantly impacts increasing As levels in rice. This study explores establishing an efficient model for predicting As accumulation in rice grain us...

Diffusive gradient in thin films combined with machine learning to discern the accumulation characteristics and driving factors of Cd and Cu in soil-rice systems.

Journal of hazardous materials
The dietary exposure risk of cadmium (Cd) in rice is significantly higher than that of copper (Cu), while the co-migration of Cd and Cu in the soil-crop system may enhance the bioavailability of pollution, thus making rapid and accurate prediction of...

Machine learning-driven optimization of arsenic phytoextraction using amendments.

Ecotoxicology and environmental safety
Exogenous amendments are crucial for enhancing the remediation efficiency of arsenic-contaminated soils by Pteris vittata. However, their effectiveness is unstable due to various factors, and neglecting their economic costs hinder broader application...

Predicting the impact of climate warming on soil quality using bacteria and machine learning.

Journal of environmental management
In the context of global warming, a substantial portion of global soil is in a state of degradation, which poses a significant threat to biodiversity and food production worldwide. Moreover, monitoring soil quality typically requires measuring numero...