Explainable Machine Learning-Based Electric Field Strength Mapping for Urban Environmental

Electronics · 2025 · Unclear · Insufficient evidence

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Study snapshot

PublicationElectronics · 2025

Study typeengineering

Reported directionUnclear

Evidence ratingInsufficient

Abstract

Explainable Machine Learning-Based Electric Field Strength Mapping for Urban Environmental Monitoring Kiouvrekis Y, Psomadakis I, Vavouranakis K, Zikas S, Katis I, Tsilikas I, Panagiotakopoulos T, Filippopoulos I. Explainable Machine Learning-Based Electric Field Strength Mapping for Urban Environmental Monitoring: A Case Study in Paris Integrating Geographical Features and Explainable AI. Electronics. 2025; 14(2):254. doi.org Abstract The objective of this study is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, advancing the field of environmental monitoring. These models are unique because they use a detailed dataset that goes beyond electromagnetic readings, incorporating information like population density, urbanization levels, and building characteristics. This novel approach, combined with explainable AI, helps identify the key factors affecting electromagnetic exposure. The models enable the creation of highly detailed and dynamic maps of electromagnetic pollution. These maps are not just static snapshots, they can track changes over time, evaluate the success of mitigation efforts, and provide deeper insights into how electromagnetic fields are distributed in urban areas. To construct a detailed electric field strength map, we conducted an extensive analysis using 410 machine learning models across the urban area of Paris, incorporating three fundamental approaches: k-nearest neighbors, neural networks, and decision trees. This comprehensive exploration allowed us to evaluate and optimize various model configurations, ensuring robust and accurate predictions of electric field strength across diverse urban environments. The kNN model exhibited the most consistent performance, with an RMSE of 1.63 and an SD of 0.20. The analysis indicates that kNN outperforms simple neural networks and decision trees in terms of both RMSE and performance stability. From the SHAP analysis, we conclude that the feature representing the total volume of buildings in the area around each antenna (V) is the most significant in predicting electromagnetic field strength in the kNN regression model, consistently showing a high impact across predictions. The population density feature (POP) also demonstrates considerable influence. Open access paper: mdpi.com

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AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeengineering

Effect directionunclear

PopulationNot supplied

Sample sizeNot supplied

ExposureNot supplied

Evidence strengthInsufficientConfidence: 74% · Peer reviewed: yes

Main findings

Across 410 evaluated machine learning model configurations for mapping electric field strength in Paris, k-nearest neighbors (kNN) showed the most consistent performance (RMSE 1.63; SD 0.20) and outperformed simple neural networks and decision trees on RMSE and stability. SHAP analysis indicated that total volume of buildings around each antenna was the most significant predictor of electric field strength in the kNN model, with population density also influential.

Outcomes measured

Predicted electric field strength mapping accuracy (e.g., RMSE, stability)
Identification of key predictors of electric field strength via SHAP (e.g., building volume, population density)

View raw extracted JSON

{
  "study_type": "engineering",
  "exposure": {
    "band": null,
    "source": "urban antennas (implied)",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": null
  },
  "population": null,
  "sample_size": null,
  "outcomes": [
    "Predicted electric field strength mapping accuracy (e.g., RMSE, stability)",
    "Identification of key predictors of electric field strength via SHAP (e.g., building volume, population density)"
  ],
  "main_findings": "Across 410 evaluated machine learning model configurations for mapping electric field strength in Paris, k-nearest neighbors (kNN) showed the most consistent performance (RMSE 1.63; SD 0.20) and outperformed simple neural networks and decision trees on RMSE and stability. SHAP analysis indicated that total volume of buildings around each antenna was the most significant predictor of electric field strength in the kNN model, with population density also influential.",
  "effect_direction": "unclear",
  "limitations": [],
  "evidence_strength": "insufficient",
  "confidence": 0.74,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "electric field strength",
    "electromagnetic exposure",
    "environmental monitoring",
    "urban mapping",
    "machine learning",
    "k-nearest neighbors",
    "neural networks",
    "decision trees",
    "SHAP",
    "explainable AI",
    "Paris",
    "geographical features",
    "electromagnetic pollution"
  ],
  "suggested_hubs": [
    {
      "slug": "exposure-assessment",
      "weight": 0.9,
      "reason": "Focuses on constructing and validating urban electric field strength maps for environmental monitoring."
    }
  ]
}

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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.3390/electronics14020254
PubMed: Not supplied

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