A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

Yang, Qiyuan; Wang, Xianmin; Yin, Jing; Du, Aiheng; Zhang, Aomei; Wang, Lizhe; Guo, Haixiang; Li, Dongdong

doi:10.1016/j.gsf.2023.101770

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…The AI technique has been widely applied in the construction industry over the last two decades in structural engineering [10][11][12][13], geotechnical engineering [14][15][16] and material sciences [17][18][19]. The application of the AI technique in prediction problems has been recognized as a reliable and robust computational solution [5].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence approach for predicting compressive strength of foamed concrete

Loc,

Duc,

Luyen

et al. 2024

UD-JST

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Accurately predicting the compressive strength of foamed concrete plays a key role in the wide application of foamed concrete in practice. This study investigates the performance of the six AI models in estimating the compressive strength of foamed concrete. A dataset of 150 samples available in the literature was used for training and testing the AI models. The dry density, cement and sand content, and water-to-cement ratio were employed as input parameters, while the 28-day compressive strength was used as the output parameter. Four statistical indicators were utilized to evaluate the performance of the AI models. The study results reveal that the AI models yield an accurate prediction of the compressive strength of foamed concrete. The best performance model in estimating the compressive strength of foamed concrete is the M5Rules model, while the least accurate model depends on the indicators used to measure the accuracy of the AI models.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence approach for predicting compressive strength of foamed concrete

Loc,

Duc,

Luyen

et al. 2024

UD-JST

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Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions

Han,

Semnani

2024

Acta Geotech.

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Rainfall-induced landslides have caused a large amount of economic losses and casualties over the years. Machine learning techniques have been widely applied in recent years to assess landslide susceptibility over regions of interest. However, a number of challenges limit the reliability and performance of machine learning-based landslide models. In particular, class imbalance in the dataset, selection of landslide conditioning factors, and potential extrapolation problems for landslide prediction under future conditions need to be carefully addressed. In this work, we introduce methodologies to address these challenges using XGBoost to train the landslide prediction model. Data resampling techniques are adopted to improve the model performance with the imbalanced dataset. Various models are trained and their performances are evaluated using a combination of different metrics. The results show that synthetic minority oversampling technique combined with the proposed gridded hyperspace sampling technique performs better than the other imbalance learning techniques with XGBoost. Subsequently, the extrapolation performance of the XGBoost model is evaluated, showing that the predictions remain valid for the projected climate conditions. As a case study, landslide susceptibility maps in California, USA are generated using the developed model and are compared with the historical California landslide catalog. These results suggest that the developed model can be of great significance in global landslide susceptibility mapping under climate change scenarios.

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A systematic review of trustworthy artificial intelligence applications in natural disasters

Albahri,

Khaleel,

Habeeb

et al. 2024

Computers and Electrical Engineering

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A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

Cited by 6 publications

References 33 publications

Artificial intelligence approach for predicting compressive strength of foamed concrete

Artificial intelligence approach for predicting compressive strength of foamed concrete

Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions

A systematic review of trustworthy artificial intelligence applications in natural disasters

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