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...

A critical review on occurrence, speciation, mobilization, and toxicity of per- and polyfluoroalkyl substances in the soil-microbe-plant system and bioremediation strategies.

Journal of hazardous materials
Per- and polyfluoroalkyl substances (PFAS) are a group of recalcitrant anthropogenic compounds that are extensively utilized for numerous industrial applications globally. Despite such vast utilization, PFAS accumulation in the soils and sediments wi...

Total nitrogen levels as a key constraint on soil organic carbon stocks across Australian agricultural soils.

Environmental research
Understanding how pedoclimatic drivers regulate soil organic carbon (SOC) stock is crucial for gaining insights into terrestrial carbon-climate feedback and thus adaptation to climate change. However, current data-driven SOC predictive models often n...

Heuristic Topological Graph Convolutional Network for Risk Prediction of Potentially Toxic Elements in Cultivated Soils.

Environmental science & technology
Contamination of cultivated soils with potentially toxic elements (PTEs) poses a growing threat to global food security. Although existing risk assessments have examined the accumulation and toxicity of PTEs, their dynamic interplay with multidimensi...

Afforestation Surpasses Abandonment in the Recovery of Post-Agricultural Soil Organic Carbon in China as Estimated by Machine Learning Models.

Global change biology
The surface soil organic carbon (SOC) dynamics typically follow a trend of initial loss followed by subsequent accumulation after cropland abandonment. However, the timing of SOC stock increase (referred to as the threshold in this study) remains ins...

Unraveling soil salinity on potentially toxic element accumulation in coastal Phragmites australis: A novel integration of multivariate and interpretable machine-learning models.

Marine pollution bulletin
Revealing the key mechanisms influencing the behavior of potentially toxic elements (PTEs) in soil-plant systems is of great significance for environmental protection and grassland development in coastal areas. This study utilized redundancy analysis...

Data-Driven Estimates of High-Resolution Soil HONO Emissions in China from 2000 to 2020.

Environmental science & technology
Soil nitrous acid (HONO) emissions influence air quality by affecting atmospheric oxidizing capacity and secondary pollutant formation. However, estimating soil HONO emissions remains uncertain due to complex factors and limited data. Here, we presen...