Integration of an InSAR and ANN for Sinkhole Susceptibility Mapping: A Case Study from Kirikkale-Delice (Turkey)

Suitable route determination for linear engineering structures is a fundamental problem in engineering geology. Rapid evaluation of alternative routes is essential, and novel approaches are indispensable. This study aims to integrate various InSAR (Interferometric Synthetic Aperture Radar) techniques for sinkhole susceptibility mapping in the Kirikkale-Delice Region of Turkey, in which sinkhole formations have been observed in evaporitic units and a high-speed train railway route has been planned. Nine months (2019–2020) of ground deformations were determined using data from the European Space Agency’s (ESA) Sentinel-1A/1B satellites. A sinkhole inventory was prepared manually using satellite optical imagery and employed in an ANN (Artificial Neural Network) model with topographic conditioning factors derived from InSAR digital elevation models (DEMs) and morphological lineaments. The results indicate that high deformation areas on the vertical displacement map and sinkhole-prone areas on the sinkhole susceptibility map (SSM) almost coincide. InSAR techniques are useful for long-term deformation monitoring and can be successfully associated in sinkhole susceptibility mapping using an ANN. Continuous monitoring is recommended for existing sinkholes and highly susceptible areas, and SSMs should be updated with new results. Up-to-date SSMs are crucial for the route selection, planning, and construction of important transportation elements, as well as settlement site selection, in such regions.

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Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach

Vietnam Journal of Earth Sciences ◽

10.15625/0866-7187/42/3/15047 ◽

2020 ◽

Vol 42 (3) ◽

Cited By ~ 1

Author(s):

Tran Van Phong ◽

Hai-Bang Ly ◽

Phan Trong Trinh ◽

Indra Prakash

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Conditioning Factors ◽

Environmental Conditioning ◽

Landslide Modeling ◽

Machine Learning Approach ◽

First Time

Landslide susceptibility mapping is a helpful tool for assessment and management of landslides of an area. In this study, we have applied first time Forest by Penalizing Attributes (FPA) algorithm-based Machine Learning (ML) approach for mapping of landslide susceptibility at Muong Lay district (Vietnam). For this aim, 217 historical landslides locations were identified and analyzed for the development of FPA model and generation of susceptibility map. Nine landslide topographical and geo-environmental conditioning factors (curvature, geology/lithology, aspect, distance from faults, rivers and roads, weathering crust, slope, and deep division) were utilized to construct the training and validating datasets for landslide modeling. Different quantitative statistical indices including Area Under the Receiver Operating Characteristic (ROC) curve (AUC) were used to evaluate the performance of the model. The results indicate that the predictive capability of the FPA is very good for landslide susceptibility mapping on both training (AUC = 0.935) and validating (AUC = 0.882) datasets. Thus, the novel FPA based ML model can be utilized for the development of accurate landslide susceptibility map of the study area and this approach can also be applied in other landslide prone areas.

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GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam

Vietnam Journal of Earth Sciences ◽

10.15625/0866-7187/42/1/14758 ◽

2020 ◽

Vol 42 (1) ◽

pp. 55-66 ◽

Cited By ~ 7

Author(s):

Dang Quang Thanh ◽

Duy Huu Nguyen ◽

Indra Prakash ◽

Abolfazl Jaafari ◽

Viet -Tien Nguyen ◽

...

Keyword(s):

Land Use ◽

Landslide Susceptibility ◽

Frequency Ratio ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Ratio Method ◽

Susceptibility Map ◽

High Susceptibility ◽

Conditioning Factors ◽

Prone Area

Landslide susceptibility mapping of the city of Da Lat, which is located in the landslide prone area of Lam Dong province of Central Vietnam region, was carried out using GIS based frequency ratio (FR) method. There are number of methods available but FR method is simple and widely used method for landslide susceptibility mapping. In the present study, eight topographical and geo-environmental landslide-conditioning factors were used including slope, elevation, land use, weathering crust, soil, lithology, distance to geology features, and stream density in conjunction with 70 past landslide locations. The results show that 6.27% of the area is in the very low susceptibility area, 21.03% in the low susceptibility area, 27.09% in the moderate susceptibility area and 27.41% of the area is in the high susceptibility zone and 18.21% in the very high susceptibility zone. The landslide susceptibility map produced in this study helps to assist decision makers in proper land use management and planning.

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Lighting of Engineering Structures and Industrial Facilities New Aspects of the Topic

Light & Engineering ◽

10.33383/2017-071 ◽

2018 ◽

pp. 29-36

Author(s):

Nikolai I. Shepetkov ◽

George N. Cherkasov ◽

Vladimir A. Novikov

Keyword(s):

Fundamental Problem ◽

Lighting Design ◽

Aesthetic Value ◽

Engineering Structures ◽

Industrial Facilities ◽

Artificial Lighting ◽

Industrial Structures ◽

Design Solutions ◽

Architectural Lighting ◽

The Aesthetic

This paper considers the fundamental problem of artificial lighting in various types and scales of industrial facilities, focusing on exterior lighting design solutions. There is a lack of interest from investors, customers and society in highquality lighting design for industrial facilities in Russia, which in many cities are very imaginative structures, practically unused in the evening. Architectural lighting of various types of installations is illustrated with photographs. The purpose of the article is to draw attention to the aesthetic value of industrial structures, provided not only by the architectural, but also by a welldesigned lighting solution.

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A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020093 ◽

2021 ◽

Vol 10 (2) ◽

pp. 93

Author(s):

Wei Xie ◽

Xiaoshuang Li ◽

Wenbin Jian ◽

Yang Yang ◽

Hongwei Liu ◽

...

Keyword(s):

Machine Learning ◽

Hybrid Method ◽

Roc Curve ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Assessment Model ◽

Landslide Susceptibility Mapping ◽

Support Vector ◽

Susceptibility Map ◽

Area Index

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

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Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Remote Sensing ◽

10.3390/rs13112166 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2166

Author(s):

Xin Yang ◽

Rui Liu ◽

Mei Yang ◽

Jingjue Chen ◽

Tianqiang Liu ◽

...

Keyword(s):

Land Use ◽

Hybrid Model ◽

Landslide Susceptibility ◽

Spatial Information ◽

Landslide Susceptibility Mapping ◽

Ratio Method ◽

Support Vector ◽

Susceptibility Map ◽

Conditioning Factors ◽

Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

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Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

Applied Sciences ◽

10.3390/app11114756 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4756

Author(s):

Gaoran Guo ◽

Xuhao Cui ◽

Bowen Du

Keyword(s):

Random Forest ◽

High Speed ◽

Recognition Rate ◽

Operation Time ◽

Deformation Monitoring ◽

Vibration Monitoring ◽

Railway Track ◽

Ballastless Track ◽

Train Operation ◽

Track Slab

High-speed railways (HSRs) are established all over the world owing to their advantages of high speed, ride comfort, and low vibration and noise. A ballastless track slab is a crucial part of the HSR, and its working condition directly affects the safe operation of the train. With increasing train operation time, track slabs suffer from various defects such as track slab warping and arching as well as interlayer disengagement defect. These defects will eventually lead to the deformation of track slabs and thus jeopardize safe train operation. Therefore, it is important to monitor the condition of ballastless track slabs and identify their defects. This paper proposes a method for monitoring track slab deformation using fiber optic sensing technology and an intelligent method for identifying track slab deformation using the random-forest model. The results show that track-side monitoring can effectively capture the vibration signals caused by train vibration, track slab deformation, noise, and environmental vibration. The proposed intelligent algorithm can identify track slab deformation effectively, and the recognition rate can reach 96.09%. This paper provides new methods for track slab deformation monitoring and intelligent identification.

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Investigation of repeatability of digital surface model obtained from point clouds in a concrete arch dam for monitoring of deformations

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702013000200007 ◽

2013 ◽

Vol 19 (2) ◽

pp. 268-286 ◽

Cited By ~ 2

Author(s):

Kutalmis Gumus ◽

Halil Erkaya ◽

Metin Soycan

Keyword(s):

Water Reservoir ◽

Point Clouds ◽

Deformation Monitoring ◽

Water Levels ◽

Arch Dam ◽

Surface Model ◽

Engineering Structures ◽

Concrete Arch Dam ◽

Accuracy Of Measurements ◽

Laser Scanners

Applicability of Terrestrial Laser Scanners/Scanning (TLS) in deformation measurement in dams is an active area of study. With the advance of modern technology, accuracy of measurements is much improved by developments in design of terrestrial laser scanners. Currently, this technology is used in large and complex engineering structures such as dams. Although TLS is a high cost technology, it is particularly used in monitoring of dam deformations, due to its speed in obtaining thousands of data points, ability to visualize the scanned object and its environment with high accuracy and ability to take long-range measurements. In order to determine the effect of change in water reservoir levels on body of the dam, TLS are used to take deformation measurements in different time intervals, where the water level was at maximum, minimum and medium levels. This paper provides an overview of terrestrial laser scanning technology for deformation monitoring. The concrete arch dam in Antalya Oymapinar, Turkey was used for case study. Four different scannings were performed in this dam in order to verify the replicability of TLS results on same water levels and equivalent conditions. Digital Surface Models reflecting dam surface have been created. Results obtained from surface model differences were examined using surface matching method.

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Accuracy of Static Differential GPS Techniques; Implications for Structural Deformation Monitoring

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.62-64.31 ◽

2009 ◽

Vol 62-64 ◽

pp. 31-38

Author(s):

J.O. Ehiorobo

Keyword(s):

Carrier Phase ◽

Reference Frames ◽

Phase Measurement ◽

Sampling Rate ◽

Tall Buildings ◽

Deformation Monitoring ◽

Structural Deformation ◽

Control Network ◽

Differential Gps ◽

Engineering Structures

In recent years, the need to monitor for Deformation in Engineering Structures such as Dams, Bridges and Tall buildings have become more necessary as a result of reported failures of many of these structures with catastrophic consequences globally. Global Positioning System (GPS) is highly automated and less labour intensive than other conventional techniques used in structural deformation monitoring. For most applications, such as National Geodetic Control Network, Urban Control Network and other Engineering Control Network, an accuracy in the cm level for most GPS work is quite adequate. For Structural deformation monitoring however, the required accuracy is in millimeters. In this paper, the use of Static Differential GPS method with multiple receivers for high precision measurement was investigated using the monitoring Stations at Ikpoba Dam as case study Scenerio. Four units of LEICA 300 Dual Frequency GPS receivers were deployed for code and carrier phase measurements with observation session of 1hr at a sampling rate of 15 sec. Baseline Processing and Least Squares Adjustment of observation was carried out in WGS 84 and NTM reference frames using the LEICA SKI-PRO Processing software and Move. Analysis of the results revealed that the number of outliers in the observation were <5% and the accuracy of horizontal and vertical coordinates were 4mm maximum for horizontal and 2mm maximum for vertical. The study revealed that in areas with favourable satellite constellation and appropriate reduction or elimination of multipath and other noise like errors, Static Differential GPS techniques with a combination of code and carrier phase measurement gives good results for structural deformation monitoring.

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Deformation Monitoring of Earth Fissure Hazards Using Terrestrial Laser Scanning

Sensors ◽

10.3390/s19061463 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1463 ◽

Cited By ~ 7

Author(s):

Yunfeng Ge ◽

Huiming Tang ◽

Xulong Gong ◽

Binbin Zhao ◽

Yi Lu ◽

...

Keyword(s):

Laser Scanning ◽

Ground Surface ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Vertical Displacement ◽

Deformation Monitoring ◽

Earth Fissure ◽

Displacement Analysis ◽

Remote Sensing Technique ◽

Noise Data

Deformation monitoring is a powerful tool to understand the formation mechanism of earth fissure hazards, enabling the engineering and planning efforts to be more effective. To assess the evolution characteristics of the Yangshuli earth fissure hazard more completely, terrestrial laser scanning (TLS), a remote sensing technique which is regarded as one of the most promising surveying technologies in geohazard monitoring, was employed to detect the changes to ground surfaces and buildings in small- and large-scales, respectively. Time-series of high-density point clouds were collected through 5 sequential scans from 2014 to 2017 and then pre-processing was performed to filter the noise data of point clouds. A tiny deformation was observed on both the scarp and the walls, based on the local displacement analysis. The relative height differences between the two sides of the scarp increase slowly from 0.169 m to 0.178 m, while no obvious inclining (the maximum tilt reaches just to 0.0023) happens on the two walls, based on tilt measurement. Meanwhile, global displacement analysis indicates that the overall settlement slowly increases for the ground surface, but the regions in the left side of scarp are characterized by a relatively larger vertical displacement than the right. Furthermore, the comparisons of monitoring results on the same measuring line are discussed in this study and TLS monitoring results have an acceptable consistency with the global positioning system (GPS) measurements. The case study shows that the TLS technique can provide an adequate solution in deformation monitoring of earth fissure hazards, with high effectiveness and applicability.

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Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽

10.3390/w11071402 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1402 ◽

Cited By ~ 32

Author(s):

Nohani ◽

Moharrami ◽

Sharafi ◽

Khosravi ◽

Pradhan ◽

...

Keyword(s):

Land Use ◽

Landslide Susceptibility ◽

Vegetation Index ◽

Slope Angle ◽

Aerial Photographs ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Susceptibility Map ◽

Conditioning Factors ◽

Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

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