Geospatial subsidence hazard modelling at Sterkfontein Caves

Ashraf, Hamid; Cawood, Frederick

doi:10.4314/sajg.v4i3.8

Cited by 9 publications

(4 citation statements)

References 13 publications

(9 reference statements)

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“…The common reasons for LS are over‐exploitation of groundwater or natural gas, subsurface mining, and earthquakes (Fiaschi et al, 2017; Marfai & King, 2007). LS can be gradual and result from compaction of lower lithological layers by pressure from top layers (Ashraf & Cawood, 2015). Uncontrolled extraction of groundwater is a primary cause of LS in many regions (Arabameri et al, 2020; Chaussard et al, 2014; Petersen‐Perlman et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Application of novel hybrid model for land subsidence susceptibility mapping

2022

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Land subsidence (LS) is one of the challenging geo‐hazards in several regions of the world that is mainly caused by the overexploitation of groundwater and subsequent collapse of underground cavities. The prediction of LS is difficult due to limitations in monitoring, proper surveys, and knowledge related to the functioning and behaviour of the phenomenon. The application of remote sensing (RS) and global positioning system (GPS) for the proper location identification of LS in the ground and its implementation in susceptibility mapping can help to better constrain this geohazard. However, proper LS susceptibility maps to identify LS prone areas and to mitigate this problem in a way are generally lacking. Several methodologies have been developed for preparing LS susceptibility maps through RS techniques coupled with statistical and machine learning (ML) algorithms. Furthermore, different regions have unique natural and anthropogenic causes for the occurrence of LS, such as subsidence due to groundwater over‐exploitation, road construction, consolidation of alluvial soil, mining activities, and so forth, resulting in severe economic losses. The LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages. ML techniques are becoming increasingly proficient at modelling such occurrences and have wide application in LSSM. This study compares the performances of single and hybrid ML models to predict and map the susceptible areas prone to LS. A combination of several algorithms helps to improve the overall results. In order to achieve this, the gradient‐boosting machine‐evolutionary genetic algorithm (GBM‐EG) was adopted in this study for LS assessment and compared to three algorithms, namely random forest, artificial neural networks, and alternating decision tree, that stand as commonly used benchmarks. This case study on LSSM was carried out in the Abozidabad watershed, Isfahan Province, Iran, and a total of 155 LS locations were identified by using GPS. Measures of 12 conditioning factors were compiled for each of the sites. These data were input into the models in five different training‐to‐validation configurations of sample ratios: D1 (90%:10%), D2 (80%:20%), D3 (70%:30%), D4 (60%:40%), and D5 (50%:50%). The performance of the algorithms were evaluated with the receiver operating characteristic curve and seed cell area index (SCAI). The results show that the best area under curve value (0.989) and SCAI value were achieved by the GBM‐EG hybrid model in an 80:20 ratio. The results demonstrate that LSSMs can be effectively produced to support planning and land use for management decisions.

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Section: Introductionmentioning

confidence: 99%

Application of novel hybrid model for land subsidence susceptibility mapping

2022

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“…This movement happens suddenly or gradually when the underground layers cannot withstand the pressure of the upper layers (Pacheco et al 2006;Ashraf and Cawood 2015). In most cases, land subsidence is a slow geological process that is hardly noticeable.…”

Section: Introductionmentioning

confidence: 99%

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

et al. 2018

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In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while onethird of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.

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“…It can occur suddenly or gradually. The most prominent cause of this type of deformation is a structural weakness of the underground layers (Ashraf and Cawood 2015). This weakness is usually caused by human activities, such as mineral extraction from underground sources (Dehghani et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Several recent studies have been conducted to map regions susceptible to natural hazards based on surface deformation. These studies used a range of methods, such as global positioning system survey methods (GPS, Abidin et al 2007, Ge et al 2014, artificial neural networks (ANN, Lee et al 2012, Chen et al 2017a), multi-criteria decision analysis (Feizizadeh and Blaschke 2014, Ashraf and Cawood 2015, logistic regression analysis Lee 2011, LR, Pham et al 2016), decision trees (Lee and Park 2013), k-nearest-neighbour analysis (k-NN, Pradhan and Jebur 2017), weights of evidence and support vector machines (WOE and SVM, Goetz et al 2015, Okwuashi andNdehedehe 2017), neuro-fuzzy systems (Vahidnia et al 2010) and adaptive neuro-fuzzy inference systems (ANFIS, Sezer et al 2011, Bui et al 2012, Basser et al 2014, Polykretis et al 2017, Chen et al 2017b. A literature review indicates that a variety of methods have been used for natural hazard mapping and susceptibility prediction.…”

Section: Introductionmentioning

confidence: 99%

A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping

Ghorbanzadeh

Blaschke

Aryal

et al. 2018

Journal of Spatial Science

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In this study, we evaluated the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) with six different membership functions (MFs). Using a geographic information system (GIS), we applied ANFIS to land subsidence susceptibility mapping (LSSM) in the study area of Amol County, northern Iran. As a novelty, we derived a land subsidence inventory from the differential synthetic aperture radar interferometry (DInSAR) of two Sentinel-1 images. We used 70% of surface subsidence deformation areas for training, while 30% were reserved for testing and validation. We then investigated regions that are susceptible to subsidence via the ANFIS method and evaluated the resulting prediction maps using receiver operating characteristics (ROC) curves. Out of the six different versions, the most accurate map was generated with a Gaussian membership function, yielding an accuracy of 84%.

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Geospatial subsidence hazard modelling at Sterkfontein Caves

Abstract: This paper covers a GIS

Cited by 9 publications

References 13 publications

Application of novel hybrid model for land subsidence susceptibility mapping

Application of novel hybrid model for land subsidence susceptibility mapping

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping

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