Visibility Analysis In Vision Metrology Network Design

The aim of this paper is to present a method whereby accuracy enhancement of an existing photogrammetric network is achieved through the automatic selection of additional camera stations. The determination of the positions of these 'accuracy fulfilment' camera stations is based upon what has been termed 'visibility uncertainty prediction modelling' of visibility constraints derived from the existing network geometry. Following a review of vision constraints in network design, the concepts of visibility uncertainty prediction and visibility uncertainty spheres are introduced. These provide a mechanism to predict the visibility of current object target points for the new accuracy fulfilment images. This in turn aids in network design improvement. The visibility uncertainty modelling is then illustrated for two close range photogrammetric network configurations, for which the test results demonstrate that the proposed model can reliably predict target visibility with an overall certainty of 75%.     

Identifying complexity of annual precipitation variation in Iran during 1960–2010 based on information theory and discrete wavelet transform

The hydrologic process and dynamic system of precipitation is influenced by many physical factors which are excessively complex and variable. Present study used a wavelet transform based multiscale entropy (WME) and wavelet-based multiscale relative entropy (WMRE) approach in order to analyze and gage the complexity of the precipitation series and spatially classify the raingauges in Iran. For this end, historical annual precipitation data of 51 years (1960–2010) from 31 raingauges was decomposed using WT in which smooth Daubechies (db) mother wavelet (db5–db10), optimal level of decomposition and boundary extensions were considered. Next, entropy concept was applied for components obtained from WT to measure of dispersion, uncertainty, disorderliness and diversification in a multi-scale form. Spatial classification of raingauges was performed using WME and WMRE values as input data to SOM and k-means approaches. Three validity indices namely Davis Bouldin (DB), Silhouette coefficient (SC) and Dunn index were used to validate the proposed model’s efficiency. Based on results, it was observed that k-means approach had better performance in determining homogenous areas with SC = 0.337, DB = 0.769 and Dunn = 1.42. Finally, spatial structure of precipitation variation in latitude and longitude directions demonstrated that WME and WMRE values had a decreasing trend with latitude, however, it was seen that WME and WMRE had an increasing relationship with longitude in Iran.     

Numerical modelling-based comparison of longitudinal dispersion coefficient formulas for solute transport in rivers

River water quality models usually apply the Fischer equation to determine the longitudinal dispersion coefficient (D_x) in solving the advection–dispersion equation (ADE). Recently, more accurate formulas have been introduced to determine D_x in rivers, which could strongly affect the accuracy of the ADE results. A numerical modelling-based approach is presented to evaluate the performance of various D_x formulas using the ADE. This approach consists of a finite difference approximation of the ADE, a MATLAB code and a MS Excel interface; it was tested against the analytical ADE solution and demonstrated using eight well-known D_x formulas and tracer study data for the Chattahoochee River (USA), the Severn (UK) and the Athabasca (Canada). The results show that D_x has an important effect on tracer concentrations simulated with the ADE. Comparison between the simulated and measured concentrations confirms the appropriate performance of Zeng and Huai’s formula for D_x estimation. Use of the newly proposed equations for D_x estimation could enhance the accuracy of solving the ADE.     

A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique

Determination of homogenous precipitation-based regions is a very important task in effective management of water resources. The present study tried to propose an effective precipitation-based regionalization methodology by conjugating both temporal pre-processing and spatial clustering approaches in a way to take advantage of multiscale properties of precipitation time series. Annual precipitation data of 51 years (1960-2010) for 31 rain gauges (RGs) were collected and used in proposed clustering approaches. Discreet wavelet transform (DWT) was used to capture the time-frequency attributes of the time series and multiscale regionalization was performed by using k-means and Self Organizing Maps (SOM) clustering techniques. Daubechies function (db) was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the approximation (A) and detail (D) coefficients were used to determine the input dataset as a basis of spatial clustering. The proposed model’s efficiency in spatial clustering stage was verified using three different indexes namely, Silhouette Coefficient (SC), Dunn index and Davis Bouldin index (DB). Results approved superior performance of k-means technique in comparison to SOM. It was also deduced that DWT-based regionalization methodology showed improvements in comparison to historical-based models. Cross mutual information was used to investigate the RGs of cluster 3’s homogeneousness in DWT-k-means approach. Results of non-linear correlation approach verified homogeneity of cluster 3. Verifications based on mean annual precipitation values of rain gauges in each cluster also approved the capability of multiscale approach in precipitation regionalization.     

Groundwater pesticides residue in the southwest of Iran-Shushtar plain

Study area with an area of about 415 km² is located from 31°40′ to 32°05′ northern latitudes and 48°45′ to 49°00′ eastern longitudes 85 km to the north-east of Ahwaz city, in the north of Khuzestan province, and south west of Iran. The purpose of this study is: (1) the determination of the pesticides concentration in the groundwater of the Shushtar plain (Mian-Ab) and (2) the assessment of geology, hydrogeology and anthropogenic activities impacts the groundwater quality. Thirty-seven groundwater samples were taken from product wells based on the standard methods. A simple and efficient automated method for extraction and preconcentration was used. In this method, a pyrrole-based polymer was synthesized and applied as an efficient sorbent for micro-solid-phase extraction. After extraction, analytes were desorbed in ethyl acetate and analyzed using gas chromatography–flame. The study area is surrounded by Aghajari Formation dominated by silt and clay sediments and the Bakhtiari Formation dominated by sand and gravel. Existence of these formations affects the aquifer sediments and the hydrogeological properties. In the study area, the sediments grade from gravel and sand in the north and east into silt and clay to the south and west, respectively. The topsoil in the south of the study area contains more clay sediments. In this study, the concentration of two common herbicides, i.e., 2,4-D and clodinafop propargyl and two pesticides, i.e., permethrin and diazinon, in the groundwater of Mian-Ab aquifer was assessed. Chemical analysis results showed that the 2,4-D residue in the groundwater has the highest concentration (15 ppm). About 50% of the samples have concentration values more than the maximum contamination level based on EPA drinking standard. The pesticides concentrations decrease from the north to the south of the study area. Pesticides influx to the groundwater in the south of the area is prevented or diminished due to the specific geological situation and soil type. Distribution pattern of population centers, which increase to the north of the study area, and the role of groundwater as the main source of drinking water are two important issues that must be considered in management of pesticides use in the area.     

Changes in precipitation extremes over arid to semiarid and subhumid Punjab,Pakistan

Asymmetrical monsoons during the recent past have resulted into spatially variable and devastating floods in South Asia. Analysis of historic precipitation extremes record may help in formulating mitigation strategies at local level. Eleven indices of precipitation extremes were evaluated using RClimDex and daily time series data for analysis period of 1981–2010 from five representative cities across Punjab province of Pakistan. The indices include consecutive dry days, consecutive wet days, number of days above daily average precipitation, number of days with precipitation ≥10 mm, number of days with precipitation ≥20 mm, very wet days, extremely wet days, simple daily intensity index, maximum 1-day precipitation quantity, maximum 5 consecutive day precipitation quantity, and annual total wet-day precipitation. Mann-Kendall test and Sen’s slope extremes were used to detect trends in indices. Droughts and excessive precipitation were dictated by elevation from mean sea level with prolonged dry spells in southern Punjab and vice versa confirming spatial trends for precipitation extremes. However, no temporal trend was observed for any of the indices. Summer in the region is the wettest season depicting contribution of monsoons during June through August toward devastating floods in the region.     

Cluster and R-mode factor analyses on soil geochemical data of Masjed-Daghi exploration area, northwestern Iran

Masjed-Daghi is located in Julfa sheet (1:100,000 series) in the northwest of Iran. The area consists of a very likely gold mineralization bearing epithermal mineralization which appears to be associated with a porphyry Cu–Mo system at deeper levels. Ninety-three soil samples were collected and analyzed for 13 elements (Au, Mo, Cu, Pb, Sn, Ag, Zn, Cr, Mn, Ba, Be, Ni, Co) by using emission spectrometry and atomic absorption spectrophotometry. The data were processed and interpreted using univariate and multivariate statistical analyses. The distribution of the majority of variables is slightly to moderately positively skewed which can be interpreted by log-normal model. Only Ni, Be, and Mn show normal distribution. Based on cluster analysis, the variables can be classified into two main groups. The first group consists of the main ore forming elements such as Au, Ag, and Ba which belong to epithermal system and Mo, Sn, and Cu which have more affiliation to porphyry mineralization. The principal component analysis extracted three factors. These factors calculated using varimax rotated R-mode factor loading matrix account for more than 65 % of the total variance. The first factor represents the main constituents of the epithermal system (Au, Ag, Ba) and its geochemical halo at the northeast of the study area. The second factor represents the main constituents of the porphyry system (Cu, Mo, Sn) and its geochemical halo in the western part of the study area which is overlapped with the volcanic rocks affected by local intrusions with higher alteration overprint. The third factor, however with less significance, represents Pb and Zn which are not the main ore constituents but can be considered as pathfinder elements. The results have been used to locate hidden orebodies using presented factor score mapping.