Version 1
: Received: 3 September 2024 / Approved: 3 September 2024 / Online: 3 September 2024 (18:10:18 CEST)
How to cite:
Cong, Y.; Inazumi, S. Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities. Preprints2024, 2024090243. https://doi.org/10.20944/preprints202409.0243.v1
Cong, Y.; Inazumi, S. Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities. Preprints 2024, 2024090243. https://doi.org/10.20944/preprints202409.0243.v1
Cong, Y.; Inazumi, S. Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities. Preprints2024, 2024090243. https://doi.org/10.20944/preprints202409.0243.v1
APA Style
Cong, Y., & Inazumi, S. (2024). Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities. Preprints. https://doi.org/10.20944/preprints202409.0243.v1
Chicago/Turabian Style
Cong, Y. and Shinya Inazumi. 2024 "Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities" Preprints. https://doi.org/10.20944/preprints202409.0243.v1
Abstract
This paper explores how smart cities can cope with land subsidence and liquefaction in the context of rapid urbanization in Japan. Since the 1960s, liquefaction has become an important topic in geotechnical engineering, and great efforts have been made to evaluate the resistance of soil to liquefaction. There is currently a lack of machine learning applications specifically for smart cities in areas such as geological hazards. This study uses two machine learning techniques, artificial neural networks and ensemble learning, to obtain a prediction model with high performance in predicting the bearing layer depth to improve the accuracy of geo-engineering surveys. The model was developed by analyzing actual survey data from 433 locations in Setegaya, Tokyo, by using artificial neural networks (ANNs) and bagging, respectively. The results show that machine learning has great advantages in predicting the bearing layer depth. In addition, compared with a single model such as artificial neural networks, the prediction performance of ensemble learning can be improved by about 20%. Both interdisciplinary approaches can help predict address risks and thus promote sustainable urban development, highlighting the potential of smart cities in the future.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
