Variance component estimation in errors-in-variables models and a rigorous total least-squares approach

A method for variance component estimation (VCE) in errors-in-variables (EIV) models is proposed, which leads to a novel rigorous total least-squares (TLS) approach. To achieve a realistic estimation of parameters, knowledge about the stochastic model, in addition to the functional model, is required. For an EIV model, the existing TLS techniques either do not consider the stochastic model at all or assume approximate models such as those with only one variance component. In contrast to such TLS techniques, the proposed method considers an unknown structure for the stochastic model in the adjustment of an EIV model. It simultaneously predicts the stochastic model and estimates the unknown parameters of the functional model. Moreover the method shows how an EIV model can support the Gauss-Helmert model in some cases. To make the VCE theory into EIV model more applicable, two simplified algorithms are also proposed. The proposed methods can be applied to linear regression and datum transformation. We apply these methods to these examples. In particular a 3-D non-linear close to identical similarity transformation is performed. Two simulation studies besides an experimental example give insight into the efficiency of the algorithms.     

A weighted least-squares solution to a 3-D symmetrical similarity transformation without linearization

A weighted least-squares (WLS) solution to a 3-D non-linear symmetrical similarity transformation within a Gauss-Helmert (GH) model, and/or an errors-in-variables (EIV) model is developed, which does not require linearization. The geodetic weight matrix is the inverse of the observation dispersion matrix (second-order moment). We suppose that the dispersion matrices are non-singular. This is in contrast to Procrustes algorithm within a Gauss-Markov (GM) model, or even its generalized algorithms within the GH and/or EIV models, which cannot accept geodetic weights. It is shown that the errors-invariables in the source system do not affect the estimation of the rotation matrix with arbitrary rotational angles and also the geodetic weights do not participate in the estimation of the rotation matrix. This results in a fundamental correction to the previous algorithm used for this problem since in that algorithm, the rotation matrix is calculated after the multiplication by row-wise weights. An empirical example and a simulation study give insight into the efficiency of the proposed procedure.     

Spectroscopic-based assessment of the content and geochemical behaviour of arsenic in a highly heterogeneous sulphide-rich mine waste dump

Soil pollution by arsenic is a serious environmental problem in many mining areas. Quick identification of the amount and extent of the pollution is an important basis for developing appropriate remediation strategies. In a case study, 55 soil samples were collected from a highly heterogeneous waste dump around the Sarcheshmeh copper mine, south east Iran. Samples’ visible and near-infrared (VNIR) reflectance spectra were measured, transformed to absorbance and then pre-processed using Savitzky–Golay first-derivative (FD) and Savitzky–Golay second-derivative (SD) transformation methods. The obtained spectra were then subjected to three regression models including principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) for predicting arsenic concentration. The best prediction accuracies were obtained by SVR and PLSR methods applied on first-derivative pre-processed spectra with R ² values of 0.81 and 0.69, respectively. It was found that VNIR spectroscopy is a successful method for predicting As concentration in contaminated soils of the dumpsites. Study of the prediction mechanism showed that the intercorrelation between arsenic and spectral features of soil including iron oxy/hydroxides and clay minerals was the major mechanism enabling the prediction of arsenic concentration. However, higher values of correlation coefficients at ~460, ~560 and ~590 nm suggested the internal association between arsenic and iron minerals as the more important mechanism for prediction. This conclusion supported previous speciation studies conducted in the same waste dump using improved correlation analysis and chemical sequential extraction method.     

Seasonal variation of the double diffusion processes at the Strait of Hormuz

Profiles of salinity and temperature were measured in the strait of Hormuz (SH) during the winter of 2012, spring and summer of 2013. To investigate the double diffusion (DD) processes, Turner (TU) angle values are calculated in all the stations in the SH. Different TU angle values correspond to salt fingering (SF), diffusive convection (DC) and stable stratification. The distributions of the two forms of DD were plotted vertically along transects in the eastern, central and western part of the SH, and corresponding DD processes were described. The results show that both SF and DC occurred in most part of the study area. Two different water masses (the Indian Ocean surface water and the Persian Gulf water) were evident at the SH, and SF and DC were evident at the interface of two water masses. Due to evaporation, SF occurred in the surface layer of most Stations throughout the year. In the eastern part of the SH, occurrences of DC were more feasible in wintertime. SF was the main phenomenon at the end of hot season. For central part, SF occurred throughout the year in water column. In the western part, water column was stable in summer and DC happened in most part of water column in winter.     

Evaluation of ANFIS and LR models for seismic rockfalls’ susceptibility mapping: a case study of Firooz Abad-Kojour,Iran, Earthquake (2004)

Seismic rockfall is one of the prevalent geohazards that cause huge losses in the earthquake-stricken areas. In the present research, a model is developed to map susceptibility (occurrence probability) of seismic rockfalls in a regional scale using Logistic Regression (LR) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques. In this research, Firooz Abad-Kojour earthquake of 2004 was introduced as the benchmark and the model base. The susceptible zones predicted by LR and ANFIS methods were compared with the database (distribution map) of seismic rockfalls, by which the results revealed a good overlapping between the susceptible zones predicted by the ANFIS and the field observation of rockfalls triggered by this earthquake. Besides, for the statistical evaluation of results obtained by LR and ANFIS models, the verification parameters with high accuracy such as density ratio (Dr), quality sum (Qs), and receiver-operating characteristic curve (ROC) were used. By analyzing the susceptibility maps and considering the Qs index obtained by LR (21.04184) and ANFIS (26.75592), it could be found that the Qs of ANFIS is higher than that of LR. Moreover, based on the obtained value of the area under the curve (AUC) from LR (0.972) and ANFIS (0.984) methods, ANFIS provided a higher accuracy in zonation and susceptibility mapping of rockfalls triggered by Firooz Abad-Kojour earthquake of 2004 compared to the LR method.     

Removal of H2S from crude oil via stripping followed by adsorption using ZnO/MCM-41 and optimization of parameters

In the present work, H₂S of crude oil was removed via a two-step method including stripping followed by adsorption. First, ZnO/MCM-41 adsorbents containing 5, 17.5 and 30 wt% of zinc were synthesized and characterized using XRD and nitrogen physisorption. Then, these materials were used as adsorbents for the removal of the H₂S stripped from crude oil. At second step, the H₂S of crude oil was extracted to gas phase by hot stripping. The obtained extract was collected in a storage tank for the subsequent H₂S adsorption process. A three-factor Box–Behnken design with five center points and one response was performed for the optimization of adsorption of H₂S. The influence of process parameters and their interactional effects on the adsorption of H₂S were analyzed using the obtained adsorption experimental data. A model including three important factors, i.e., temperature, space velocity and amount of supported zinc and their interactions, was developed to generate the optimum condition. The point of Zn = 30 wt%, T = 300 °C and space velocity = 3,000 h^?1 had the optimum point with the highest break point time (t _bp = 973 min).     

Multi-temporal landsat images based on eco-environmental change analysis in and around Chah Nimeh reservoir,Balochestan (Iran)

Chah Nimeh reservoirs have served as a water storage facility, especially during droughts over the last three decades. It is also an important wintering site for migratory birds. In this study, thematic mapper time-series data were derived from Landsat images for prolonged droughts that occurred in two satellite images (2002 and 2011). The data derived from these images were used for the detection of changes in land cover and water storage in the reservoirs. First, a vegetation cover map was produced using soil-adjusted vegetation index and field sampling. Subsequently, land use/cover maps were generated using supervised and hybrid image classification method. Using the spatial change detector (SCD v1.0) software extension, the layers were combined and the change map was generated. The overall accuracy of the produced thematic images was assessed in regards to quantity and allocation disagreements. A total of five classes were defined in this investigation: deep water, shallow water, vegetation, salt land and bare land. The results showed that during the period of study, water volume reduced and vegetation cover increased, especially around the reservoirs that are important as shelter for wintering migratory birds. Comparison of land use/cover maps showed the increase in total available surface of shallow water, which indicated an increase in the habitats for surface feeding and diving birds.