Machine Learning Approach to Identify the Relationship Between Heavy Metals and Soil Parameters in Salt Marshes

 

Abstract

Influences from tidal flooding and freshwater inundation from upland watersheds create an environmentally important ecosystem along coastlines, namely salt marshes. Salt marshes have been recognized as effective sinks for organic carbon and heavy metal contaminants. A detailed understanding of the specific binding agents in the soils on the storage of contaminants is investigated herein using two modern machine learning algorithms: extreme gradient boosting (XGboost) and random forest (RF). Results of the current work indicate that Fe is the most important binding agent for As, Cd, Cr and Zn while Mn and organic matter are the most important binding agents for Cu and Pb. Noting the fact that an increase in salinity not only causes heavy metal release into aquatic systems but also leads to a decrease in floral growth and organic matter production, the findings of this study help to formulate proper remediation strategies to contain heavy metals in tidal marshes.

 

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