Ecological risk assessment of oilfield soil through the use of machine learning combining with spatial interaction effects

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     Ecological risk assessment of oilfield-contaminated soil is vital for ensuring environmental and public health. By integrating machine learning with spatial interaction effects, researchers can better identify contamination hotspots, predict pollutant dispersion, and evaluate long-term ecological impacts. Machine learning models, such as random forest and support vector machines, enhance the accuracy of risk prediction by analyzing complex datasets, including heavy metal concentrations, soil properties, and land-use patterns. Spatial interaction effects further refine these assessments by considering how contamination spreads and interacts across geographic regions. This combined approach enables more precise, data-driven decision-making for soil remediation and sustainable land management in oil-impacted regions.

Hashtags:
#EcologicalRiskAssessment #OilfieldSoil #MachineLearning #SoilContamination #SpatialAnalysis #EnvironmentalMonitoring #SoilHealth #PollutionMapping #HeavyMetals #SoilRemediation #DataDrivenDecisions #GeospatialModeling #SustainableLandUse #EnvironmentalRisk #ContaminatedSoil #AIinEnvironmentalScience #SoilDegradation #RiskPrediction #SpatialEffects #EnvironmentalImpact #MLinSoilScience #SoilEcotoxicology #SmartSoilManagement #OilPollution #SoilQualityAssessment




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