Vertical land motion monitored with satellite radar altimetry and tide gauge along the Texas coastline, USA, between 1993 and 2020

Qiao, Xiaojun; Chu, Tianxing; Tissot, Philippe; Ali, Ibraheem; Ahmed, Mohamed

doi:10.1016/j.jag.2023.103222

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…It should be noted that the 250 m × 250 m spatial grids were utilized to facilitate the calculation and analysis of feature values of both the nine features and the VLM. Besides, the inclusion of around 30-year data of both HPRD and GWWD was a consideration for their enduring influence on land subsidence [39]- [41]. Extensive details concerning these nine features are listed in Table II.…”

Section: Datamentioning

confidence: 99%

“…Apart from these well-documented impacts, subsidence in densely populated coastal regions can compound a host of additional critical issues including flooding and seawater intrusion [5]. These challenges are primarily associated with complex land-water interactions, especially in the context of the rising trend of the global sea level [6], [7]. Consequently, land subsidence emerges as a pivotal factor in shaping the future of coastal regions [8].…”

Section: Introductionmentioning

confidence: 99%

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Interpretation and Attribution of Coastal Land Subsidence: An InSAR and Machine Learning Perspective

Qiao,

Chu,

Krell

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Subsidence, the downward vertical land motion (VLM), plays a pivotal role in contributing to the risk of coastal flooding. Accurately estimating VLM and identifying its potential features related to subsidence can provide crucial information for stakeholders to make better informed decisions. This study aimed to estimate large-scale subsidence at the Texas Gulf Coast and identify potential subsidence features using explainable models. Nine potential features were considered for modeling the VLM, ranging from natural terrain variations to anthropogenic activities. These features were used to train a random forest (RF) machine learning (ML) model. Explainable artificial intelligence (XAI) techniques including SHapley Additive exPlanations (SHAP) and impurity-and permutation-based feature importance were used to identify the contributions to subsidence. The results demonstrated favorable performance of the RF model, achieving an R 2 value of 0.56 during validation. XAI results underscored the significance of the digital elevation model (DEM) on explaining subsidence at the Texas Coast. Additionally, XAI analysis highlighted the overall contribution of subsidence from anthropogenic activities, such as hydrocarbon extraction and groundwater withdrawal. Furthermore, the sample-level SHAP map provided detailed and reasonable subsidence-attribution results across the study area, showing potentials for automatic and data-driven explanations of the VLM.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpretation and Attribution of Coastal Land Subsidence: An InSAR and Machine Learning Perspective

Qiao,

Chu,

Krell

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Coastal Subsidence Analysis Between Houston and Galveston, Texas, USA

Qiao,

Chu,

Tissot

2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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Machine learning application in modelling marine and coastal phenomena: a critical review

Pourzangbar,

Jalali,

Brocchini

2023

Front. Environ. Eng.

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This study provides an extensive review of over 200 journal papers focusing on Machine Learning (ML) algorithms’ use for promoting a sustainable management of the marine and coastal environments. The research covers various facets of ML algorithms, including data preprocessing and handling, modeling algorithms for distinct phenomena, model evaluation, and use of dynamic and integrated models. Given that machine learning modeling relies on experience or trial-and-error, examining previous applications in marine and coastal modeling is proven to be beneficial. The performance of different ML methods used to predict wave heights was analyzed to ascertain which method was superior with various datasets. The analysis of these papers revealed that properly developed ML methods could successfully be applied to multiple aspects. Areas of application include data collection and analysis, pollutant and sediment transport, image processing and deep learning, and identification of potential regions for aquaculture and wave energy activities. Additionally, ML methods aid in structural design and optimization and in the prediction and classification of oceanographic parameters. However, despite their potential advantages, dynamic and integrated ML models remain underutilized in marine projects. This research provides insights into ML’s application and invites future investigations to exploit ML’s untapped potential in marine and coastal sustainability.

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Vertical land motion monitored with satellite radar altimetry and tide gauge along the Texas coastline, USA, between 1993 and 2020

Cited by 3 publications

References 28 publications

Interpretation and Attribution of Coastal Land Subsidence: An InSAR and Machine Learning Perspective

Interpretation and Attribution of Coastal Land Subsidence: An InSAR and Machine Learning Perspective

Coastal Subsidence Analysis Between Houston and Galveston, Texas, USA

Machine learning application in modelling marine and coastal phenomena: a critical review

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