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Soil

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High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PloS one
Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effecti...

Analysis of the Importance of Oxides and Clays in Cd, Cr, Cu, Ni, Pb and Zn Adsorption and Retention with Regression Trees.

PloS one
This study determines the influence of the different soil components and of the cation-exchange capacity on the adsorption and retention of different heavy metals: cadmium, chromium, copper, nickel, lead and zinc. In order to do so, regression models...

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.

Chemosphere
The soil sorption partition coefficient logK is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in ...

Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

Chemosphere
The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the p...

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...

Dominant Edaphic Controls on Particulate Organic Carbon in Global Soils.

Global change biology
The current soil carbon paradigm puts particulate organic carbon (POC) as one of the major components of soil organic carbon worldwide, highlighting its pivotal role in carbon mitigation. In this study, we compiled a global dataset of 3418 data point...

[Soil Cadmium Prediction and Health Risk Assessment of an Oasis on the Eastern Edge of the Tarim Basin Based on Feature Optimization and Machine Learning].

Huan jing ke xue= Huanjing kexue
Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple lin...