Study of the method to calculate subsidence coefficient based on SVM

Tan, Zhiying; Li, Peixian; Yan, Lili; Deng, Kazhong

doi:10.1016/j.proeps.2009.09.150

Cited by 22 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the literature overview, there are several models and methods (qualitative and quantitative) have been successfully applied and developed in different areas of the world as land subsidence susceptibility mapping (LSSM). The quantitative methods can deal with the disadvantages of qualitative ones which include: logistic regression (LR) [ 15 ], frequency ratio (FR) [ 16 ], analytical hierarchy processes (AHP) [ 17 ], weight-of-evidence (WOE) [ 18 ], evidential-belief-functions (EBF) [ 16 ], artificial neural network (ANN) [ 7 ], support vector machine (SVM) [ 19 ], random forest (RF), grey model (GM) [ 20 ], sensitivity analysis (SA) [ 6 ], fuzzy logic (FL) [ 21 ] and adaptive neuro-fuzzy inference system (ANFIS) [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Bui

Shahabi

Shirzadi

et al. 2018

Sensors

126

View full text Add to dashboard Cite

In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.

show abstract

Section: Introductionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Bui

Shahabi

Shirzadi

et al. 2018

Sensors

126

View full text Add to dashboard Cite

show abstract

“…In the past decade, a new alternative kernel‐based technique called a support vector machines (SVM) has been found to be popular in modeling studies due to its some advantages over ANN. This method seems to be a powerful alternative that overcome some of the basic weakness related to ANN while retaining all strengths of ANN .…”

Section: Introductionmentioning

confidence: 99%

Modeling of tensile strength of rocks materials based on support vector machines approaches

Ceryan

Okkan

Samui

et al. 2012

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS-SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS-SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS-SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS-SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS-SVM model resulted in error reductions relative to that of the other models.Tensile strength was used for different geotechnical aims, and laboratory and full-scale studies were carried out to explore the possible relationship between fine production and water content of rock material [11]. Results of the laboratory work showed that the percentage of fines fraction produced by a cone crusher machine was a function of the type of rock tested and the tensile strength of individual specimens [11].There are mainly two methods for determining the tensile strength of rock materials. One of the methods is laboratory tests including direct uniaxial tensile test and indirect tensile tests (direct method) or making use of the previously derived empirical equations from literature called as indirect method. Direct uniaxial tensile test have some disadvantages and limitations. This test, which is theoretically the simplest and most effective method for the determination of tensile strength, is in fact difficult to carry out in practice for rock material and the difficulty in specimen preparation [4,9,[12][13][14][15][16]. Therefore, to determine tensile strength, indirect tensile tests are developed, for example Brazilian test, ring test, hoop test, bending test, etc. Brazilian test which is much more popular among those carried out has been proved as a satisfactory technique for determining the tensile strength of many rocks [6,[8][9][10][11][17][18][19][20]. Brazilian test measures tensile strength indirectly by developing tension across the diameter of a rock disc that is subjected to compression through a vertical load [8]. Testing proc...

show abstract

“…According to the literature review, several models and methods (qualitative and quantitative) have been successfully applied and developed in different parts of the world for land subsidence susceptibility mapping. AI methods successfully implemented to determine the susceptibility of a given area to land subsidence include Logistic Regression (LR) [188], Frequency Ratio (FR) [189], Analytical Hierarchy Processes (AHP) [190], Weight-of-Evidence (WOE) [191], Evidential-Belief-Functions (EBF) [189], Grey Model (GM) [192], Sensitivity Analysis (SA) [193], Fuzzy Logic (FL) [194], Adaptive Neuro-Fuzzy Inference System (ANFIS) [195], Artificial Neural Network (ANN) [185], and Support Vector Machine (SVM) [196].…”

Section: Ai Methodsmentioning

confidence: 99%

State of the Art and Recent Advancements in the Modelling of Land Subsidence Induced by Groundwater Withdrawal

Guzy

Malinowska

2020

Water

View full text Add to dashboard Cite

Land subsidence is probably one of the most evident environmental effects of groundwater pumping. Globally, freshwater demand is the leading cause of this phenomenon. Land subsidence induced by aquifer system drainage can reach total values of up to 14.5 m. The spatial extension of this phenomenon is usually extensive and is often difficult to define clearly. Aquifer compaction contributes to many socio-economic effects and high infrastructure-related damage costs. Currently, many methods are used to analyze aquifer compaction. These include the fundamental relationship between groundwater head and groundwater flow direction, water pressure and aquifer matrix compressibility. Such solutions enable satisfactory modelling results. However, further research is needed to allow more efficient modelling of aquifer compaction. Recently, satellite radar interferometry (InSAR) has contributed to significant progress in monitoring and determining the spatio-temporal land subsidence distributions worldwide. Therefore, implementation of this approach can pave the way to the development of more efficient aquifer compaction models. This paper presents (1) a comprehensive review of models used to predict land surface displacements caused by aquifer drainage, as well as (2) recent advances, and (3) a summary of InSAR implementation in recent years to support the aquifer compaction modelling process.

show abstract

Study of the method to calculate subsidence coefficient based on SVM

Cited by 22 publications

References 0 publications

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Modeling of tensile strength of rocks materials based on support vector machines approaches

State of the Art and Recent Advancements in the Modelling of Land Subsidence Induced by Groundwater Withdrawal

Contact Info

Product

Resources

About