Prediction of Magnetic Pollution with Artificial Neural Network in Living Areas

Sakacı, Furkan Hasan; Cerezci, O.

doi:10.1007/s42835-021-00772-y

Cited by 2 publications

(1 citation statement)

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“…Computed and measured received signal strengths were compared, which were shown to be reasonably close. Electric [27] and magnetic field [28] estimation was performed under/near high voltage transmission and distribution lines using ANN, and the electric and magnetic field levels were predicted accurately. Indoor wideband electric field radiation was predicted in [29] using the combined wavelet transform and time series analysis-based method.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network Based Prediction of Long-Term Electric Field Strength Level Emitted by 2G/3G/4G Base Station

Engiz

2023

Applied Sciences

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Accurate predictions of radio frequency electromagnetic field (RF-EMF) levels can help implement measures to reduce exposure and check regulatory compliance. Therefore, this study aims to predict the RF-EMF levels in the medium using an artificial neural network (ANN). The work was conducted at Ondokuz Mayis University, Kurupelit Campus, where the measurement location has line-of-sight to the base stations. Band selective measurements were also performed to assess the contribution of 2G/3G/4G services to the total RF-EMF level, which was found to be the highest among all services within the total band. Long-term RF-EMF measurements were carried out for 35 days within the frequencies of 100 kHz to 3 GHz. Then, an ANN model with Levenberg–Marquardt (LM) and Bayesian Regulation (BR) algorithms was proposed, which utilized inputs from real-time RF-EMF measurements. The performance of the models was assessed in terms of mean squared error (MSE) and regression performance. The average MSE and regression performances of the models were similar, with the lowest testing MSEs of 2.78 × 10−3 and 3.76 × 10−3 for LM and BR methods, respectively. The analysis of the models showed that the proposed models help to predict the RF-EMF level in the medium with up to 99% accuracy.

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Section: Related Workmentioning

confidence: 99%