Identification of causal factors for the Majiagou landslide using modern data mining methods

Ma, Junwei; Tang, Huiming; Hu, Xinli; Bobet, Antonio; Zhang, Ming; Zhu, Tingwei; Song, Youjian; Eldin, M. A. M. Ez

doi:10.1007/s10346-016-0693-7

Cited by 76 publications

(41 citation statements)

References 36 publications

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“…Movement and failure of landslide can be induced by many different causes [25], including rainfall and thrust loading. Due to rapid urbanization, construction of buildings, spoil, vehicle dynamic loads, or surcharge loads will increase the driving forces resulting in thrust load-caused landslide [26].…”

Section: Descriptions Of the Landslide Model Testmentioning

confidence: 99%

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure

Niu

Liu

et al. 2019

Sensors

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A laboratory model test is an effective method for studying landslide risk mitigation. In this study, thermal infrared (TIR) imagery, a modern no-contact technique, was introduced and integrated with terrestrial laser scanning (TLS) and particle tracking velocimetry (PTV) to characterize the failure of a landslide model. The characteristics of the failure initiation, motion, and region of interest, including landslide volume, deformation, velocity, surface temperature changes, and anomalies, were detected using the integrated monitoring system. The laboratory test results indicate that the integrated monitoring system is expected to be useful for characterizing the failure of landslide models. The preliminary results of this study suggest that a change in the relative TIR signal (ΔTIR) can be a useful index for landslide detection, and a decrease in the average value of the temperature change ( Δ T I R ¯ ) can be selected as a precursor to landslide failure.

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Section: Descriptions Of the Landslide Model Testmentioning

confidence: 99%

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure

Niu

Liu

et al. 2019

Sensors

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“…The distributed landslide prediction model does not enhance the efficiency of the algorithm. In the study on mid-long-term landslide warning, the operational efficiency of the prediction model is vital to the timeliness of landslide prevention and control [14].…”

Section: Introductionmentioning

confidence: 99%

Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

Guo

Zuo

et al. 2019

Applied Sciences

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In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides.

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“…Considering hazard risk as a complex system, the transfer mechanism between causal factors and risk magnitudes can be highlighted by 'support' and 'confidence', which are two parameters in the procedure of association rules. Through data mining, the cause-and-effect relationships between hydrological parameters and landslide movement are determined (Ma et al 2017). Studies have shown that ARM has successfully determined the level of association in various fields, and can provide evidence for probable causeand-effect relations (Qodmanan et al 2011;Nahar et al 2013;Guo et al 2014;Peng et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Performance of multi-level association rule mining for the relationship between causal factor patterns and flash flood magnitudes in a humid area

Zhong

Jiang

Hong

et al. 2019

Geomatics, Natural Hazards and Risk

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Identification of causal factors for the Majiagou landslide using modern data mining methods

Cited by 76 publications

References 36 publications

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure

Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

Performance of multi-level association rule mining for the relationship between causal factor patterns and flash flood magnitudes in a humid area

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