KuanHuang/predicting-heavy-metal-adsorption-in-soil
A machine learning model based on gradient boosting decision tree for predicting heavy metal adsorption in soil.
This tool helps environmental scientists and soil engineers predict how much heavy metal will be adsorbed by soil. By inputting various soil properties (like pH, clay content) and system conditions (like temperature, heavy metal concentration), it outputs the predicted amount of heavy metal that the soil will absorb. This is useful for assessing contamination risks or designing remediation strategies.
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Use this if you need to quickly estimate the capacity of soil to adsorb heavy metals like Cd, Cr, Cu, Pb, Ni, and Zn under specific environmental conditions without extensive lab testing.
Not ideal if you require predictions for heavy metals not listed or need to understand adsorption mechanisms beyond the nine input parameters provided.
Stars
16
Forks
2
Language
Python
License
MIT
Category
Last pushed
Nov 06, 2021
Commits (30d)
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