TY - GEN
T1 - Determination of liquefaction-prone zones in lebak, banten using the machine learning method approach
AU - Listiyarini, Mediyana
AU - Maula, Vina Ma Unatul
AU - Nurfitria, Milasari
AU - Indra, Tito Latif
AU - Septyandy, Muhammad Rizqy
AU - Wusqa, Urwatul
N1 - Funding Information:
ACKNOWLEDGMENT The authors would like to thank the Geology Study Program and Department of Geography Universitas Indonesia for supporting this research. This research was funded by Universitas Indonesia International Indexed Publication (PUTI Proceeding) grant with contract number NKB-3569/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/29
Y1 - 2021/6/29
N2 - Liquefaction is a phenomenon in which soil becomes liquefied and loses its resistance, usually caused by earthquakes. Liquefaction should be one of the considerations in planning development because this phenomenon can damage building structures. The liquefaction susceptibility was measured by the Cone Penetration Test (CPT) method. The Liquefaction Potential Index (LPI) value is obtained from the measurement results, divided into four levels (very low, low, high, very high). However, the cost required to measure only at one location point is quite expensive. In this paper, we propose a machine learning approach to modeling a liquefaction-prone zone map.
AB - Liquefaction is a phenomenon in which soil becomes liquefied and loses its resistance, usually caused by earthquakes. Liquefaction should be one of the considerations in planning development because this phenomenon can damage building structures. The liquefaction susceptibility was measured by the Cone Penetration Test (CPT) method. The Liquefaction Potential Index (LPI) value is obtained from the measurement results, divided into four levels (very low, low, high, very high). However, the cost required to measure only at one location point is quite expensive. In this paper, we propose a machine learning approach to modeling a liquefaction-prone zone map.
KW - Decision Trees
KW - Liquefaction
KW - LPI
KW - Machine Learning
KW - Random Forest
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85113992664&partnerID=8YFLogxK
U2 - 10.1109/ISESD53023.2021.9501604
DO - 10.1109/ISESD53023.2021.9501604
M3 - Conference contribution
AN - SCOPUS:85113992664
T3 - Proceeding - 2021 International Symposium on Electronics and Smart Devices: Intelligent Systems for Present and Future Challenges, ISESD 2021
BT - Proceeding - 2021 International Symposium on Electronics and Smart Devices
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Symposium on Electronics and Smart Devices, ISESD 2021
Y2 - 29 June 2021 through 30 June 2021
ER -