AN APPLICATION OF MACHINE LEARNING FOR PREDICTING AIRBORNE CHLORIDE IN COASTAL AREAS

In this study, a machine learning approach using the Isolation Forest, one of the anomaly detection machine learning algorithms, was proposed to exclude anomalous values from the training data. In addition, the influences of the modified training data on the prediction of airborne chloride were inve...

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Veröffentlicht in:Journal of Structural and Construction Engineering (Transactions of AIJ) 2024/08/01, Vol.89(822), pp.818-829
Hauptverfasser: SAKIHARA, Kohei, TAKI, Yuta, NAKAMURA, Fuminori, UKEMASU, Kei
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container_title Journal of Structural and Construction Engineering (Transactions of AIJ)
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creator SAKIHARA, Kohei
TAKI, Yuta
NAKAMURA, Fuminori
UKEMASU, Kei
description In this study, a machine learning approach using the Isolation Forest, one of the anomaly detection machine learning algorithms, was proposed to exclude anomalous values from the training data. In addition, the influences of the modified training data on the prediction of airborne chloride were investigated. Therefore, it was found that combining statistical processing with the Isolation Forest improves the accuracy of predicting airborne chloride. Furthermore, it was revealed that the most contributing feature importance to the prediction of airborne chloride is the significant wave height.
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subjects Airborne Chloride
Algorithms
Anomalies
Artificial Intelligence
Chloride Attack
Chlorides
Coastal zone
Durability
Machine Learning
Predictions
Random Forest
Wave height
title AN APPLICATION OF MACHINE LEARNING FOR PREDICTING AIRBORNE CHLORIDE IN COASTAL AREAS
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