Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry,object-based classification,and geological features: a case study from Mayo,Yukon Territory,Canada

Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of ?2 to ?4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data.     

Discriminating Weathering Degree by Integrating Optical Sensor and SAR Satellite Images for Potential Mapping of Groundwater Resources in Basement Aquifers of Semiarid Regions

Natural Resources Research - Unlike in coastal and sedimentary basins, regional-scale exploration of groundwater resources using only geophysical methods is costlier in consolidated rocks such as...     

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.     

Spatio‐temporal evaluation of the groundwater quality in Kafr Al‐Zayat District,Egypt

Eighteen groundwater well sites located in Kafr Al‐Zayat (Egypt) were sampled monthly from January 2009 to November 2011 for microbial content, Mn⁺², Fe⁺², total dissolved solids (TDS), total hardness, NO₃^?, and turbidity. The data were analyzed combining the integrated use of factor and cluster analyses as well as the geostatistical semi‐variogram modeling. The prime objectives were to assess the groundwater suitability for drinking, to document the factors governing the spatio‐tempral variability, and to recognize distinctive groundwater quality patterns to help enable effective sustainability and proactive management of the limited resource. The groundwater microbial, Mn⁺², Fe⁺², TDS, and total hardness contents violated the drinking water local standards while the turbidity and the nitrate content complied with them. Factor analysis indicated that the microbial content is the most influential factor raising the variability potential followed, in decreasing order, by Mn²⁺, Fe²⁺, TDS, NO₃^?, turbidity, and finally the total hardness. Turbidity resulting from urban and agricultural runoff was strongly associated with most of the quality parameters. Quality parameters fluctuate sporadically without concrete pattern in space and time while their variability scores peak in November every year. Three spatially distinctive quality patterns were recognized that were consistent with and affected by the cumulative effects of the local topography, depth to water table, thickness of the silty clay (cap layer), surface water, and groundwater flow direction and hence the recharge from contaminated surface canals and agricultural drains. Copyright © 2013 John Wiley & Sons, Ltd.     

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine

Natural Resources Research - It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted...     

Technical and Non-technical Challenges of Development of Offshore Petroleum Reservoirs: Characterization and Production

Natural Resources Research - Offshore oil and gas reservoirs comprise a significant portion of the world’s reserve base, and their development is expected to help close a potential gap in the...     

Potential sea-level rise effects on the hydrodynamics and transport processes in Hudson–Raritan Estuary,NY–NJ

Ocean Dynamics - The climatic change has led to the sea-level rise (SLR), which is expected to continue based on the current industrial and human activities. Previous studies indicated that most of...     

Performance evaluation of the M8 algorithm to predict M7+ earthquakes in Turkey

The M8 algorithm is one of the most reliable intermediate-term middle-range earthquake prediction algorithms. The present study evaluates the ability of the M8 algorithm and its modified versions for predicting major events (M7+) in Turkey. Thirty different algorithms were developed by changing the radius of circle of investigation (CI) and the lower magnitude cutoff of the M8 algorithm. These modified algorithms were executed all over the territory of Turkey, and the results were evaluated using the error diagram. Each modified algorithm was executed for consecutive half-year intervals over a specified period of time. Subsequently, the seismic catalog was updated, and failures-to-predict ratio and the fraction of alarm were considered. Results showed that the location of areas of alarm change gradually over consecutive intervals, and no sudden changes can be observed. In addition, the annual changes of areas of alarm are not random and follow a pattern. This study also showed that the modified algorithm having a three to six annual average of events and a 393-km CI radius is an efficient algorithm for predicting the future seismic events in Turkey. This algorithm predicted six out of six target events, retrospectively, with a confidence level of 96.4 %. According to the obtained results, it will be possible to rely on this modified algorithm to predict near future earthquakes of Turkey. Furthermore, this study proves that it is possible to alter the M8 algorithm for being used in regional studies.     

Superiority of neural networks for pillar stress prediction in bord and pillar method

Estimation of pillar stress is a crucial task in underground mining. This is used to determine pillar dimensions, room width, roof conditions, and general mine layout. There are several methods for estimating induced stresses due to underground excavations, i.e., empirical methods, numerical solutions, and currently artificial intelligence (AI). AI based techniques are gradually gaining popularity especially for problems involving uncertainty. In this paper, an attempt has been made to predict stresses developed in the pillars of bord and pillar mining using artificial neural network. A comparison has also been done to compare the obtained results with the boundary element method as well as measured field values. For this purpose, a multilayer perceptron neural network model was developed. A number of architectures with different hidden layers and neurons were tried to get the best solution, and the architecture 5-20-8-1 was found to be an optimum solution. Sensitivity analysis was also carried out to understand the influence of important input parameters on pillar stress concentration.