Characteristics and main causes of earth fissures in northeastern Beijing Plain, China

Wan, Jiawei; Tan, Chengxuan; Feng, Chengjun; Zhang, Peng; Qi, Bangshen

doi:10.1007/s10064-020-01731-z

Cited by 13 publications

(3 citation statements)

References 37 publications

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“…The presence of a fault zone has been established as a determinant in a fissure's propagation course. This was further confirmed by the authors of [28], who used site evaluation, drilling, and trench unearthing to define and assess the basis for fissure formation in Qinglong Gaben, China. Syn-sedimentary faults, as well as an excessive drawdown of groundwater and erosion processes, were shown to be major drivers in the creation and multiplication of fissures in the region, according to the research.…”

Section: Pre-existence Of Faultsmentioning

confidence: 67%

Understanding the Mechanisms of Earth Fissuring for Hazard Mitigation in Najran, Saudi Arabia

et al. 2023

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Being a fast-growing city with a high rate of urbanization and agricultural development, the city of Najran, situated in the southwest of the Kingdom of Saudi Arabia, has witnessed a series of earth fissuring events and some other geo-environmental hazards in recent times. These fissures have posed a significant threat to inhabitants and infrastructure in the area. A few studies suggest that excessive groundwater withdrawal is responsible for fissuring activities. Because of the intensity of this geo-hazard, this article presupposes that groundwater extraction alone cannot be responsible for the magnitude of fissuring activity in the area and discusses other severe factors that could be responsible for the earth fissures. The study proposes that the cause of the problem is multifaceted and synergistic, and outlines threatening factors that can inherently trigger more fissures in the region, based on the geologic history of the area and a critical review of investigative studies conducted in the area and beyond. Predicated on the region’s structural history, some undiscovered elements that can potentially cause fissuring in the region were identified and discussed. Some of these include the pre-existence of a fault system, a crack from the bedrock ridge, the existence of paleochannels, the collapsibility of loess, the tectonic (earthquake) history of the area, and differential compaction due to heterogeneity. The use of a metaheuristic and a combined application integrating other optimization algorithms can be utilized to determine optimum hyperparameters and present their statistical importance, thereby improving accuracy and dependability in fissure prediction in Najran. Reliable models would primarily be used to monitor active fissures and identify key factors utilizing spatial information, subsidence, groundwater-related data sets, etc.

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Section: Pre-existence Of Faultsmentioning

confidence: 67%

Understanding the Mechanisms of Earth Fissuring for Hazard Mitigation in Najran, Saudi Arabia

et al. 2023

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“…It's worth noting that the earth fissures in Yemen have never been studied previously. Earth fissures result from numerous occurrences, including tectonic activities (e.g., earthquakes, fault movement, and landslides) and human activities (e.g., groundwater withdrawal in dry and semi-dry areas and underground mining) 18 , 25 – 30 . Water overpumping can produce substantial underground stress and is considered a key cause of soil compaction 20 .…”

Section: Introductionmentioning

confidence: 99%

Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen

Al-Masnay

Al‐Areeq

Ullah

et al. 2022

Sci Rep

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Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa’ Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures.

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“…Overall, the model comparison findings show that the RS-NBTree model, followed by the RS-ADTree model, RS-LMT, RS-FLDA and the RS model, is the most trustworthy for use as a prediction for earth fissures.5 | DISCUSSIONGround fissure is a common geohazard, particularly in areas where there is a high risk of catastrophic land subsidence. It happened due to a variety of reasons, including earthquakes, aseismic fault creep, post affect after seismic activity, excessive groundwater use, arid or semiarid climates, and unique sedimentary textures and structures(Wan et al, 2020). Identifying the actual cause of earth fissures or land subsidence is not very easy, especially when there are multiple indicators for land subsidence.…”

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confidence: 99%

Earth fissure susceptibility mapping: Application of random subspace‐based novel ensemble approaches

Santosh,

Arabameri,

Arora

2024

Geological Journal

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The development of earth fissures, which are linear fractures with openings or offsets on the land surface, can severely affect landforms, especially in urban areas, in the form of earthquakes causing major concern on human lives as well as damage to infrastructures. Thus, an early warning map for lands susceptible to earth fissures can better equip planners for formulating mitigation strategies. In this study, we focus on the Damghan Plain in Iran for preparation of earth fissure susceptible maps using several topographical, hydrological, geological and environmental conditioning factors. In order to train these conditioning factors and preparation of earth fissure susceptibility maps, 124‐earth fissure field‐based samples, for training and validation purposes, were used by random subspace (RS) model based on four other machine learning ensemble methods such as RS‐Naïve‐Bayes Tree (NBTree), RS‐alternating decision tree (ADTree), RS‐Fisher's Linear Discriminant Function (FLDA) and RS‐Logistic model tree (LMT). From the validation technique, the receiver operating characteristic (ROC) curve performance test demonstrates that the RS‐NBTree model was the best suited with area under curve (AUC) = 0.974 followed by RS‐ADTree (AUC = 0.966), RS‐LMT (AUC = 0.954), RS‐FLDA (AUC = 0.948) and RS (AUC = 0.923). The results from our study can be useful for environmental management and risk reduction.

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Characteristics and main causes of earth fissures in northeastern Beijing Plain, China

Cited by 13 publications

References 37 publications

Understanding the Mechanisms of Earth Fissuring for Hazard Mitigation in Najran, Saudi Arabia

Understanding the Mechanisms of Earth Fissuring for Hazard Mitigation in Najran, Saudi Arabia

Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen

Earth fissure susceptibility mapping: Application of random subspace‐based novel ensemble approaches

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