Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

Akçay, Özgün

doi:10.3390/ijgi4042131

Cited by 16 publications

(14 citation statements)

References 49 publications

(48 reference statements)

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“…Numerous models and approaches for landslide susceptibility mapping have been developed throughout the world over the past decades [3][4][5][6][7]. The most used methods are based on soft computing or statistical techniques, e.g., the fuzzy logic method [8,9], artificial neural network model [10,11], logistic regression model [12,13], cellular automata methods [14], and analytic hierarchy process [15,16].…”

Section: Introductionmentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

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Section: Introductionmentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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“…The recent integration of computer vision algorithms and photogrammetric methods is leading to interesting procedures which have increasingly automated the entire image-based 3D modelling process (Remondino et al, 2014). In last two decades, Close-Range Photogrammetry (CRP) as a contribution of photogrammetry and computer vision, spread into many fields of engineering applications such as medical modelling applications (Xiao et al, 2014), orthophotos by Unmanned Aerial Systems (UAS) (Akcay, 2015) and documentation of cultural heritages (Yılmaz et al, 2007). Three dimensional textured models, digital surface models and true orthophotos can be produced using advantages of low-cost CRP software.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Jpeg Compression in Close Range Photogrammetry

Akçay¹,

Erenoǧlu²,

Avşar³

2017

International Journal of Engineering and Geosciences

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Digital photogrammetry, using digital camera images, is an important low-cost engineering method to produce precise three-dimensional model of either an object or the part of the earth depending on the image quality. Photogrammetry which is cheaper and more practical than the new technologies such as LIDAR, has increased point cloud generation capacity during the past decade with contributions of computer vision. Images of new camera technologies needs huge storage space due to larger image file sizes. Moreover, this enormousness increases image process time during extraction, orientation and dense matching. The Joint Photographic Experts Group (JPEG) is one of the most commonly used methods as lossy compression standard for the storage purposes of the oversized image file. Particularly, image compression at different rates causes image deteriorations during the processing period. Therefore, the compression rates affect accuracy of photogrammetric measurements. In this study, the close range images compressed at the different levels were investigated to define the compression effect on photogrammetric results, such as orientation parameters and 3D point cloud. The outcomes of this study show that lower compression ratios are acceptable in photogrammetric process when moderate accuracy is sufficient.

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“…Currently the main methods of landslide deformation prediction include Grey models, neural networks, support vector machine (SVM), least squares support vector machine (LS-SVM), and a variety of combinations of forecasting methods [5][6][7][8][9]. When the original data sequence fluctuation is large and the information is too dispersed, the prediction accuracy of Grey theory is relatively low.…”

Section: Introductionmentioning

confidence: 99%

“…They have important significance in slope stability evaluation, slope safety early warning, and slippery slope hazard control for timely grasping of the slope deformation evolution rules and accurate prediction of future evolution rules and trends of slope deformation [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

Xie

Liang

Zhong

et al. 2017

IJGI

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Abstract:Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.

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Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

Cited by 16 publications

References 49 publications

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

The Effect of Jpeg Compression in Close Range Photogrammetry

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

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