Surface and groundwater quality characterization of Deoria District,Ganga Plain,India

A water quality investigation was carried out in the Deoria district, Ganga plain, to assess the suitability of surface and groundwaters for domestic, agricultural, and industrial purposes. As much as 50 representative samples from river and groundwater were collected from various stations to monitor the water chemistry of various ions, comprising Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃ ⁻, SO₄ ²⁻, NO₃ ⁻, Cl⁻, F⁻, and trace metals, such as Fe, Cu, Mn, Zn, Cd, and Pb. The results showed that electrical conductance (EC), total dissolved solids (TDS), HCO₃ ⁻, Mg²⁺, Na⁺, and total hardness (TH) are above the maximum desirable limit, and apart from Fe and Mn all other trace metals are within the maximum permissible limit for drinking water. The calculated values for sodium absorption ratio (SAR), salinity, residual sodium carbonate (RSC), and permeability index (PI) indicate well to permissible use of water for irrigation. High values of Na%, RSC, and Mg-hazard (MH) at some stations restrict its use for agricultural purpose. Anthropogenic activities affect the spatial variation of water quality. Economic and social developments of the study area is closely associated with the characteristics of the hydrological network.     

Assessment of rock slope stability by probabilistic-based Slope Stability Probability Classification method along highway cut slopes in Adilcevaz-Bitlis (Turkey)

Rock slope stability is of great concern along highway routes as stability problems on cut slopes may cause fatal events as well as loss of property. In rock slope engineering, stability evaluations are commonly performed by means of analytical or numerical analyses, principally considering the factor of safety concept. As a matter of fact, the probabilistic assessment of slope stability is progressively getting popularity due to difficulties in assigning the most appropriate values to design parameters in analytical or numerical methods. Additionally, the effect of heterogeneities in rock masses and discontinuities on the analysis results is minimized through the probabilistic concept. In this study, slope stability of high and steep sedimentary rock cut slopes along a state highway in Adilcevaz-Bitlis (Turkey) was evaluated on the basis of probabilistic approach using the Slope Stability Probability Classification (SSPC) system. The probabilistic assessment indicates major slope stability problems because of discontinuity controlled and discontinuity orientation independent mass movements. Almost all studied cut slopes suffer from orientation-independent stability problems with very low stability probabilities. Additionally, the probability of planar and toppling failures is significantly high with respect to the SSPC system. The stability problems along the investigated rock slopes were also verified by field reconnaissance. Remedial measures such as slope re-design and reinforcement at the studied locations should be taken to prevent hazardous events along the highway. On the other hand, the probabilistic approach may be a useful tool during rock slope engineering to overcome numerous uncertainties when probabilistic and analytic results are compared.     

Misconceptions about the Hubble recession law

Almost all astronomers now believe that the Hubble recession law was directly inferred from astronomical observations. It turns out that this common belief is completely false. Those models advocating the idea of an expanding universe are ill-founded on observational grounds. This means that the Hubble recession law is really a working hypothesis. One alternative to the Hubble recession law is the tired-light hypothesis originally proposed by Zwicky (Proc. Nat. Acad. Sci. 15:773, 1929). This hypothesis leads to a universe that is an eternal cosmos continually evolving without beginning or end. Such a universe exists in a dynamical state of virial equilibrium. Observational studies of the redshift-magnitude relation for Type Ia supernovae in distant galaxies might provide the best observational test for a tired-light cosmology. The present study shows that the model Hubble diagram for a tired-light cosmology gives good agreement with the supernovae data for redshifts in the range 0<z<2. This observational test of a static cosmology shows that the real universe is not necessarily undergoing expansion nor acceleration. An erratum to this article can be found at     

Seismic clusters and their characteristics at the Arabian Sea Triple Junction: Supportive evidences for plate margin deformations

The plate margin features defining the Arabian Sea Triple Junction (ASTJ) are: the Aden Ridge (AR), Sheba Ridge (SR) with their intervening Alula-Fartak Transform (AFT), Carlsberg Ridge (CR) and Owen Fracture Zone (OFZ). Exact nature of ASTJ is presently debated: whether it is RRF (ridge-ridge-fault) or RRR (ridge-ridge-ridge) type. A revised seismicity map for ASTJ is given here using data for a period little more than a century. “Point density spatial statistical criterion” is applied to short-listed 742 earthquakes (mb ≥ 4.3), 10 numbers of spatio-temporal seismic clusters are identified for ASTJ and its arms. Relocated hypocentres help better constraining the cluster identification wherever such data exist. Seismic clusters actually diagnose the most intense zones of strain accumulation due to far field as well as the local stress operating at ASTJ. An earthquake swarm emanating from a prominent seismic cluster below SR provides an opportunity to investigate the pore pressure diffusion process (due to the active source) by means of “r-t plot”. Stress and faulting pattern in the active zones are deduced from 43 CMT solutions. While normal or lateral faulting is characteristic for these arms, an anomalous thrust earthquake occurs in the triangular ‘Wheatley Deep’ deformation zone proximal to ASTJ. The latter appears to have formed due to a shift of the deformational front from OFZ towards a transform that offsets SR. Though ASTJ is still in the process of evolution, available data favour that this RRF triple junction may eventually be converted to a more stable RRR type.     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

利用气象卫星遥感监测沙尘暴

介绍了沙尘暴的定义及其危害,简述了内蒙古沙尘暴天气的发生情况,并对利用极轨气象卫星遥感技术监测沙尘暴的原理以及方法进行了探讨和论述。同时,对沙尘暴的遥感监测方法进行了进一步的探讨,提出了遥感监测沙尘暴的思路。     

PTB220型气压传感器调整方法

1概述目前内蒙古自动站气压传感器使用的输出方式有两种：RS232串行输出和模拟电压输出。当对气压传感器进行检定后,如果发现各检定点测量误差的总平均大于±0.3 hPa,则需要进行调整。     

基于灰色关联度模型的区域滑坡敏感性评价

数理统计和机器学习模型如支持向量机(support vector machine,SVM)等,在区域滑坡敏感性评价中得到广泛的应用.但这些模型的建模过程往往较复杂,如在对机器学习进行训练和测试时难以选取合理的非滑坡栅格单元,而且有较多的模型参数需要确定.为提高滑坡敏感性评价建模的效率和精度,提出基于灰色关联度的敏感性评价模型.灰色关联度模型能有效计算各比较样本与参考样本之间的定量的关联度,具有建模过程简洁和评价精度高的优点,该模型目前在区域滑坡敏感性评价中的应用还没有引起研究人员的足够关注且有待进一步拓展.拟将灰色关联度模型用于浙江省飞云江流域南田—雅梅图幅(南田地区)的滑坡敏感性评价,并将得到的评价结果与SVM模型的敏感性评价结果作对比分析.结果显示,灰色关联度模型在高和极高敏感区的滑坡预测精度优于SVM模型,而在中等敏感区的滑坡预测精度略低于SVM模型;整体而言,灰色关联度模型对整个南田地区滑坡敏感性分布的预测精度略高于SVM模型.对两个模型建模过程的对比结果显示,灰色关联度模型建模较简单,具有比SVM模型更高的建模效率,为滑坡敏感性评价提供了一种新思路.     

Remote estimation of surficial seafloor properties through the application Angular Range Analysis to multibeam sonar data

The variation of the backscatter strength with the angle of incidence is an intrinsic property of the seafloor, which can be used in methods for acoustic seafloor characterization. Although multibeam sonars acquire backscatter over a wide range of incidence angles, the angular information is normally neglected during standard backscatter processing and mosaicking. An approach called Angular Range Analysis has been developed to preserve the backscatter angular information, and use it for remote estimation of seafloor properties. Angular Range Analysis starts with the beam-by-beam time-series of acoustic backscatter provided by the multibeam sonar and then corrects the backscatter for seafloor slope, beam pattern, time varying and angle varying gains, and area of insonification. Subsequently a series of parameters are calculated from the stacking of consecutive time series over a spatial scale that approximates half of the swath width. Based on these calculated parameters and the inversion of an acoustic backscatter model, we estimate the acoustic impedance and the roughness of the insonified area on the seafloor. In the process of this inversion, the behavior of the model parameters is constrained by established inter-property relationships. The approach has been tested using a 300 kHz Simrad EM3000 multibeam sonar in Little Bay, NH. Impedance estimates are compared to in situ measurements of sound speed. The comparison shows a very good correlation, indicating the potential of this approach for robust seafloor characterization.