Susceptibility mapping of instability related to shallow mining cavities in a built-up environment

Pellicani, Roberta; Spilotro, Giuseppe; Gutiérrez, Francisco

doi:10.1016/j.enggeo.2016.12.011

Cited by 20 publications

(7 citation statements)

References 43 publications

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“…The learning rate is one of the most important hyper-parameters of ANNs that affect model performance. In present study, the learning rate is calculated by the following formula [22]: where η ( n ) is the learning rate in the nth times training; η min is the minimum value of the learning rate; η max is the maximum value of the learning rate, and d is the delay rate.…”

Section: Methodsmentioning

confidence: 99%

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

et al. 2019

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The fragile ecological environment near mines provide advantageous conditions for the development of landslides. Mine landslide susceptibility mapping is of great importance for mine geo-environment control and restoration planning. In this paper, a total of 493 landslides in Shangli County, China were collected through historical landslide inventory. 16 spectral, geomorphic and hydrological predictive factors, mainly derived from Landsat 8 imagery and Global Digital Elevation Model (ASTER GDEM), were prepared initially for landslide susceptibility assessment. Predictive capability of these factors was evaluated by using the value of variance inflation factor and information gain ratio. Three models, namely artificial neural network (ANN), support vector machine (SVM) and information value model (IVM), were applied to assess the mine landslide sensitivity. The receiver operating characteristic curve (ROC) and rank probability score were used to validate and compare the comprehensive predictive capabilities of three models involving uncertainty. Results showed that ANN model achieved higher prediction capability, proving its advantage of solve nonlinear and complex problems. Comparing the estimated landslide susceptibility map with the ground-truth one, the high-prone area tends to be located in the middle area with multiple fault distributions and the steeply sloped hill.

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Section: Methodsmentioning

confidence: 99%

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

et al. 2019

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“…In urban environments, occlusion of sinkholes would present additional challenges, as the visual appearance and morphology of sinkholes will differ from natural cavities due to the influence of anthropogenic structures such as buildings and vehicles [69]. It may be anticipated that, as the number of recognised sinkhole occurrences in urban areas has increased substantially in recent decades [70], adaptation of our model to urban landscapes may be especially important. Adaptive learning methods can be used to allow the model to dynamically adjust to new data distributions and enhance its performance in different environments.…”

Section: Model Generalisability To Other Karst Environmentsmentioning

confidence: 99%

Deep-Learning-Based Automatic Sinkhole Recognition: Application to the Eastern Dead Sea

Alrabayah,

Caus,

Watson

et al. 2024

Remote Sensing

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Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea’s eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data and manual, time-consuming expert analysis. This study introduces an efficient deep learning model designed to improve sinkhole mapping using accessible satellite imagery, which could enhance management practices related to sinkholes and other geohazards in evaporite karst regions. The developed AI system is centered around the U-Net architecture. The model was initially trained on a high-resolution drone dataset (0.1 m GSD, phase I), covering 250 sinkhole instances. Subsequently, it was additionally fine-tuned on a larger dataset from a Pleiades Neo satellite image (0.3 m GSD, phase II) with 1038 instances. The training process involved an automated image-processing workflow and strategic layer freezing and unfreezing to adapt the model to different input scales and resolutions. We show the usefulness of initial layer features learned on drone data, for the coarser, more readily-available satellite inputs. The validation revealed high detection accuracy for sinkholes, with phase I achieving a recall of 96.79% and an F1 score of 97.08%, and phase II reaching a recall of 92.06% and an F1 score of 91.23%. These results confirm the model’s accuracy and its capability to maintain high performance across varying resolutions. Our findings highlight the potential of using RGB visual bands for sinkhole detection across different karst environments. This approach provides a scalable, cost-effective solution for continuous mapping, monitoring, and risk mitigation related to sinkhole hazards. The developed system is not limited only to sinkholes however, and can be naturally extended to other geohazards as well. Moreover, since it currently uses U-Net as a backbone, the system can be extended to incorporate super-resolution techniques, leveraging U-Net based latent diffusion models to address the smaller-scale, ambiguous geo-structures that are often found in geoscientific data.

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“…In recent years, urban ground collapse (UGC) [1,2] events have become more frequent with the increased intensity of urban underground space construction, presenting a continuous expansion pattern parallel with urbanization; this has serious consequences for urban economic development and the safety of citizens [1,3]. The contradiction between underground space development and urban safety has intensified so much that it has become urgent that the government formulates better management and control strategies and conducts in-depth research on collapse risk [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An LLM-Based Inventory Construction Framework of Urban Ground Collapse Events with Spatiotemporal Locations

Hao,

Qi,

et al. 2024

IJGI

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Historical news media reports serve as a vital data source for understanding the risk of urban ground collapse (UGC) events. At present, the application of large language models (LLMs) offers unprecedented opportunities to effectively extract UGC events and their spatiotemporal information from a vast amount of news reports and media data. Therefore, this study proposes an LLM-based inventory construction framework consisting of three steps: news reports crawling, UGC event recognition, and event attribute extraction. Focusing on Zhejiang province, China, as the test region, a total of 27 cases of collapse events from 637 news reports were collected for 11 prefecture-level cities. The method achieved a recall rate of over 60% and a precision below 35%, indicating its potential for effectively and automatically screening collapse events; however, the accuracy needs to be improved to account for confusion with other urban collapse events, such as bridge collapses. The obtained UGC event inventory is the first open access inventory based on internet news reports, event dates and locations, and collapse co-ordinates derived from unstructured contents. Furthermore, this study provides insights into the spatial pattern of UGC frequency in Zhejiang province, effectively supplementing the statistical data provided by the local government.

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Susceptibility mapping of instability related to shallow mining cavities in a built-up environment

Cited by 20 publications

References 43 publications

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

Deep-Learning-Based Automatic Sinkhole Recognition: Application to the Eastern Dead Sea

An LLM-Based Inventory Construction Framework of Urban Ground Collapse Events with Spatiotemporal Locations

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