The 6M approach to land subsidence

Erkens, Gilles; Stouthamer, Esther

doi:10.5194/piahs-382-733-2020

Cited by 10 publications

(9 citation statements)

References 9 publications

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“…This phenomenon occurs suddenly or sometimes gradually due to a number of natural as well as anthropogenic factors. The main factors for their occurrences are earthquakes, volcanic activity, floods, overexploitation of groundwater and its decline, mining activities, tunnel construction, and so on (Erkens and Stouthamer, 2020;Lyu et al, 2020). Among all these possible factors, groundwater exploitation and structural weakness are the most important issues in this concern (Yu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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“…Within IGCP 663 we developed an analysis of the vulnerability of these environments to relative sea level rise (RSLR) considering an uneven land subsidence distribution that improved the assessment of vulnerability to RSLR at the regional scale. The outcomes of the analysis are available to the authorities responsible for water resources and coastal protection (Wang et al, 2012;Erkens and Stouthamer, 2020;Yang et al, 2020).…”

Section: Dissemination Sessionmentioning

confidence: 99%

Sustainable development of coastal cities through control of land subsidence: activities of IGCP Project 663 in Jakarta

Yan

Yang

et al. 2022

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The awareness on the importance that land subsidence plays on coastal processes at the regional scale is increased over the last two decades, and it clearly appears that land subsidence can contribute primarily to the relative sea level rise affecting coastal zones. Jakarta is one of the cities mostly affected by the combination of sea-level rise and land subsidence. In this paper, the activities carried out in Jakarta under the umbrella of the IGCP Project 663 were presented, and the possible measures and best practices mitigating land subsidence for the research associates and potential stakeholders were provided, with which can serve as inspiration for authorities and communities facing land subsidence. Meanwhile, major achievements of IGCP 663 in Jakarta were summarized and introduced, including dissemination session, scientific session and field trips. Specifically, major advances on coastal subsidence studies regarding the effect of relative sea level rise, subsidence mapping, monitoring and simulation, as well as the support of policy making are highlighted and summarized.

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“…Population adaptation to land subsidence has been a better strategy for managing the hazard. Some adaptations have been implemented by the community, such as creating traditional embankments and raising houses hence they will not be inundated by the flood [8]. Flooding is one of the disasters that resulted from land subsidence in Jakarta City.…”

Section: Disaster Mitigationmentioning

confidence: 99%

A Review Disaster Mitigation of Jakarta Land Subsidence Areas

et al. 2021

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The Jakarta Capital Special Region of Jakarta is an area in the Java Island that has the highest level of vulnerability to land subsidence disasters. Land subsidence is caused by several factors such as excessive use of groundwater and natural compression of soil conditions. This study used a review analysis method based on a literature study on mitigation of land subsidence disasters in Jakarta city. The mitigation planning of land subsidence disasters in Jakarta will refer to government regulations on disaster management and Jakarta City spatial planning. Disaster mitigation is necessary to anticipate or reduce disaster risk. Disaster mitigation is a series of efforts to reduce disaster risk through physical development combined with awareness and capacity building in dealing with disaster threats. Education cannot be separated from the active role of the community and local government. High-densely populated areas will lead to more use of groundwater in Jakarta city. The excessive use of groundwater has resulted in more intensive land subsidence in Jakarta’s urban area. The safe zone mapping of subsidence disasters is prominent to reduce and control land subsidence disasters. The government’s efforts to lessen the impacts of land subsidence are by educating the population in disaster areas.

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The 6M approach to land subsidence

Cited by 10 publications

References 9 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Sustainable development of coastal cities through control of land subsidence: activities of IGCP Project 663 in Jakarta

A Review Disaster Mitigation of Jakarta Land Subsidence Areas

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