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 |
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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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Struct. Constr. Eng.</addtitle><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.</description><subject>Airborne Chloride</subject><subject>Algorithms</subject><subject>Anomalies</subject><subject>Artificial Intelligence</subject><subject>Chloride Attack</subject><subject>Chlorides</subject><subject>Coastal zone</subject><subject>Durability</subject><subject>Machine Learning</subject><subject>Predictions</subject><subject>Random Forest</subject><subject>Wave height</subject><issn>1340-4202</issn><issn>1881-8153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNo9kE9PwkAUxDdGExE9-QU28Vzcf92-3lxLgU1ql5TeN92yVRoEbOHgt7eI8TRvMr_MJA-hR0omnHLyXG3afgLxBChcoREFoAHQkF8PNxckEIywW3TX9y0hUsSSjlCpcqyWy0wnqtQmx2aG31Sy0HmKs1QVuc7neGYKvCzSqU7Ks1W6eDXFACSLzBR6mmKd48SoVakyrIpUre7RTVNte__wp2NUztIyWQSZmQ9DWdBCSAPJhPDOhw5CFgspG9c0zgGExEdk7eqGDwk4zkQlwojJ2EU1kQ2Rcbz2jAs-Rk-X2kO3_zr5_mjb_anbDYuWUyIiITjQgXq5UG1_rN69PXSbz6r7tlV33NRbb88_sxBbYOxXKPxH9UfVWb_jPwsqYBA</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>SAKIHARA, Kohei</creator><creator>TAKI, Yuta</creator><creator>NAKAMURA, Fuminori</creator><creator>UKEMASU, Kei</creator><general>Architectural Institute of Japan</general><general>Japan Science and Technology Agency</general><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20240801</creationdate><title>AN APPLICATION OF MACHINE LEARNING FOR PREDICTING AIRBORNE CHLORIDE IN COASTAL AREAS</title><author>SAKIHARA, Kohei ; TAKI, Yuta ; NAKAMURA, Fuminori ; UKEMASU, Kei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-j851-6244ebe5b8529466fbffbb8850e70dbcf3b858b324a457269b7c06f0699de2343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>jpn</language><creationdate>2024</creationdate><topic>Airborne Chloride</topic><topic>Algorithms</topic><topic>Anomalies</topic><topic>Artificial Intelligence</topic><topic>Chloride Attack</topic><topic>Chlorides</topic><topic>Coastal zone</topic><topic>Durability</topic><topic>Machine Learning</topic><topic>Predictions</topic><topic>Random Forest</topic><topic>Wave height</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>SAKIHARA, Kohei</creatorcontrib><creatorcontrib>TAKI, Yuta</creatorcontrib><creatorcontrib>NAKAMURA, Fuminori</creatorcontrib><creatorcontrib>UKEMASU, Kei</creatorcontrib><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of Structural and Construction Engineering (Transactions of AIJ)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>SAKIHARA, Kohei</au><au>TAKI, Yuta</au><au>NAKAMURA, Fuminori</au><au>UKEMASU, Kei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AN APPLICATION OF MACHINE LEARNING FOR PREDICTING AIRBORNE CHLORIDE IN COASTAL AREAS</atitle><jtitle>Journal of Structural and Construction Engineering (Transactions of AIJ)</jtitle><addtitle>J. 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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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