Editorial: Landslide Hazard in a Changing Environment

Tiranti, Davide; Cremonini, R.

doi:10.3389/feart.2019.00003

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…Landslide-risk evaluation is a significant work [5,6]. Landslides are monitored by many factors, which build the model with geological-environment variables, and landslide characteristics and their influencing factors, so it is difficult to effectively guarantee the quality of landslide-susceptibility research [7][8][9]. Therefore, improving the prediction ability of landslide-susceptibility research is an urgent problem.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

et al. 2022

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Landslide susceptibility prediction (LSP) is the key technology in landslide monitoring, warning, and evaluation. In recent years, a lot of research on LSP has focused on machine learning algorithms, and the ensemble learning algorithm is a new direction to build the optimal prediction. Logistic model tree (LMT) combines the advantages of decision tree and logistic regression, which is smaller and more robust than ordinary algorithms. The main aim of this study is to construct and test LMT-based random forest (RF) and selected ensemble learning algorithms including bagging and boosting algorithms to compare their performance. Firstly, taking the county of Ziyang, China, as the study area, through historical reports, aerial-photo interpretations, and field investigations, 690 inventory maps of landslide locations were constructed and randomly divided into the 70/30 ratio for a training and validation dataset. Secondly, considering geological conditions, and landslide-induced disease and its characteristics, 14 landslide-conditioning factors was selected. Thirdly, the variance-inflation factor (VIF) and tolerance (TOL) were used to analyze the 14 factors, and the prediction ability was calculated with information-gain technology. Ultimately, the receiver-operating-characteristic (ROC) curve was applied to verify and compare model performance. Results showed that the LMT-RF model (0.897) was superior to other models, and the performance of LMT single model (0.791) was the worst. Therefore, it can be inferred that the LMT-RF model is a promising model, and the outcome of this study will be useful to planners and scientists in landslide sensitivity studies in similar situations.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

et al. 2022

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“…Several principles of landslide management were summarized through years of engineering experience: Several principles of landslide management were summarized through years of engineering experience: 1) find out the history of landslide occurrence, the current situation and engineering geological characteristics (Jimenez-Peralvarez, 2018); 2) try to avoid landslide areas or pre-reinforcement (Zhang et al, 2021); 3) eliminate landslide hazards at once to avoid repeated treatment (Tiranti and Cremonini, 2019); 4) if the scale of landslides is large, the overall planning should be done; 5) timely management in the early development period of landslide (Yin et al, 2014); 6) to ensure the premise of safety to minimize the cost of the project (Martin et al, 2021); 7) to take effective design solutions and scientific and efficient construction (Cui et al, 2022); 8) Form a dynamic response mechanism to ensure dynamic design and dynamic construction (He et al, 2008;Wang et al, 2022b); 9) focus on inspection, repair and maintenance of facilities (Liao et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A study on soil arching effect of anti-slide pile considering different pile arrangements

et al. 2023

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Among the methods of landslide treatment, anti-slide piles are an effective measure. The soil arching effect cannot be ignored in the design of anti-slide piles, which has a significant impact on the supporting. Based on the field investigation, this paper selects the Xinyan landslide in Yanbian area of Jilin Province and uses Flac-3D to build a numerical model to simulate its soil arching effect with different pile arrangements. The simulation results show that the pile arrangement has a significant influence on the soil arching effect. When arranging piles in parallel, the soil arches between the front and rear rows of piles are distributed parallel to each other. When arranging piles in quincunx-shape, the soil arches between the rear rows of piles are the same as when laying piles in parallel. The soil between the piles flows around once when the piles are laid out in parallel, while it flows around twice when the piles are laid out in a quincunx-shape. The latter has better support than the former.

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“…Here, we focus our attention on the application of the TL-ERT method for landslide monitoring. In the last years, climate change and the occurrence of frequent extreme meteorological events have contributed to the triggering of landslide phenomena, with considerable effects on the economic and social life of the affected areas [30][31][32]. Indeed, the number of cases in which these phenomena involved strategic infrastructures (e.g., bridges, railways, highways) as well as civil housing and commercial activities [33][34][35], sometime causing fatalities [36,37], has considerably grown.…”

Section: Introductionmentioning

confidence: 99%

Time-Lapse Electrical Resistivity Tomography (TL-ERT) for Landslide Monitoring: Recent Advances and Future Directions

Lapenna

Perrone

2022

Applied Sciences

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To date, there is a growing interest for challenging applications of time-lapse electrical resistivity tomography (TL-ERT) in Earth sciences. Tomographic algorithms for resistivity data inversion and innovative technologies for sensor networks have rapidly transformed the TL-ERT method in a powerful tool for the geophysical time-lapse imaging. In this paper, we focus our attention on the application of this method in landslide monitoring. Firstly, an overview of recent methodological advances in TL-ERT data processing and inversion is presented. In a second step, a critical analysis of the main results obtained in different field experiments and lab-scale simulations are discussed. The TL-ERT appears to be a robust and cost-effective method for mapping the water-saturated zones, and for the identification of the groundwater preferential pathways in landslide bodies. Furthermore, it can make a valuable contribution to following time-dependent changes in top-soil moisture, and the spatio-temporal dynamics of wetting fronts during extreme rainfall events. The critical review emphasizes the limits and the advantages of this geophysical method and discloses a way to identify future research activities to improve the use of the TL-ERT method in landslide monitoring.

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Editorial: Landslide Hazard in a Changing Environment

Cited by 14 publications

References 16 publications

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

Performance Analysis of Logistic Model Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

A study on soil arching effect of anti-slide pile considering different pile arrangements

Time-Lapse Electrical Resistivity Tomography (TL-ERT) for Landslide Monitoring: Recent Advances and Future Directions

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