Fast integer least-squares estimation for GNSS high-dimensional ambiguity resolution using lattice theory

GNSS ambiguity resolution is the key issue in the high-precision relative geodetic positioning and navigation applications. It is a problem of integer programming plus integer quality evaluation. Different integer search estimation methods have been proposed for the integer solution of ambiguity resolution. Slow rate of convergence is the main obstacle to the existing methods where tens of ambiguities are involved. Herein, integer search estimation for the GNSS ambiguity resolution based on the lattice theory is proposed. It is mathematically shown that the closest lattice point problem is the same as the integer least-squares (ILS) estimation problem and that the lattice reduction speeds up searching process. We have implemented three integer search strategies: Agrell, Eriksson, Vardy, Zeger (AEVZ), modification of Schnorr–Euchner enumeration (M-SE) and modification of Viterbo-Boutros enumeration (M-VB). The methods have been numerically implemented in several simulated examples under different scenarios and over 100 independent runs. The decorrelation process (or unimodular transformations) has been first used to transform the original ILS problem to a new one in all simulations. We have then applied different search algorithms to the transformed ILS problem. The numerical simulations have shown that AEVZ, M-SE, and M-VB are about 320, 120 and 50 times faster than LAMBDA, respectively, for a search space of dimension 40. This number could change to about 350, 160 and 60 for dimension 45. The AEVZ is shown to be faster than MLAMBDA by a factor of 5. Similar conclusions could be made using the application of the proposed algorithms to the real GPS data.     

GPS position time-series analysis based on asymptotic normality of M-estimation

The efficacy of robust M-estimators is a well-known issue when dealing with observational blunders. When the number of observations is considerably large—long time series for instance—one can take advantage of the asymptotic normality of the M-estimation and compute reasonable estimates for the unknown parameters of interest. A few leading M-estimators have been employed to identify the most likely functional model for GPS coordinate time series. This includes the simultaneous detection of periodic patterns and offsets in the GPS time series. Estimates of white noise, flicker noise, and random walk noise components are also achieved using the robust M-estimators of (co)variance components, developed in the framework of the least-squares variance component estimation (LS-VCE) theory. The method allows one to compute confidence interval for the (co)variance components in asymptotic sense. Simulated time series using white noise plus flicker noise show that the estimates of random walk noise fluctuate more than those of flicker noise for different M-estimators. This is because random walk noise is not an appropriate noise structure for the series. The same phenomenon is observed using the results of real GPS time series, which implies that the combination of white plus flicker noise is well described for GPS time series. Some of the estimated noise components of LS-VCE differ significantly from those of other M- estimators. This reveals that there are a large number of outliers in the series. This conclusion is also affirmed by performing the statistical tests, which detect (large) parts of the outliers but can also leave parts to be undetected.     

Surveying general prospects and challenges of GIS implementation in developing countries: a SWOT–AHP approach

We propose a combined method based on the strengths, weaknesses, opportunities and threats (SWOT) and analytic hierarchy process (AHP) to investigate the challenges and prospects of adopting geographic information systems (GIS) in developing countries. In this context, we identify, group, and analyse SWOT indicators in relation to the main GIS components: data, people, and technology. The relative significance of each SWOT indicator and its related SWOT groups in each GIS component is quantified. The method is then applied in a situation assessment of GIS adoption in the governmental organisations and strategic planning. The SWOT–AHP approach proves to be very useful in identifying and quantifying the relative significance of the major factors affecting GIS implementation, and effectively facilitates GIS strategic planning.      

Stage–discharge estimation in straight compound channels using isovel contours

For various hydrological applications such as flood control projects, a knowledge of stage–discharge relationship is of particular interest to river engineers. Stage–discharge curves in compound channels cannot be easily predicted in comparison with single channels due to their 3D characteristics of flow. In this paper, the concept of cross‐sectional isovel contours is used for estimation of stage–discharge curves in compound channels. The multivariate Newton's method is applied to the difference between the observed and estimated data to optimize the exponent values of the governing parameters. The accuracy of the proposed model is tested successfully against available experimental results, which are taken from the Flood Channel Facility (FCF) laboratory. Then the results are compared with the Single and Divided Channel Methods (SCM and DCM, respectively), the Weighted Divided Channel Method (WDCM), the exchange discharge method (EDM), and the Coherence Method (COHM). The average values of mean absolute percentage error (MAPE) and normalized root mean square error (NRMSE) in discharge estimation based on each referenced section at any level for 6 sections of the experimental cases are within 3.1% and 0.023, respectively. The biggest advantage of the proposed method is its inherent simplicity, which does not need any calibration.     

A new family of multistep numerical integration methods based on Hermite interpolation

In this paper, a new family of explicit and implicit multistep methods is presented both for the error-controlled and uncontrolled modes. The main concept is to replace the Newton interpolation with the Hermite interpolation, where the Hermite polynomial is fitted to the function values and its derivatives. This idea is very useful in the numerical solution of problems (e.g., orbit propagation problem) where higher-order derivatives can easily be computed. In addition to the theoretical concept, the stability regions of the proposed methods are determined. The new methods are more stable than the well-known multistep numerical integrators (i.e., Adams–Bashforth and Adams–Bashforth–Moulton) in the explicit, implicit, and predictor–corrector forms. Using the second-order derivatives gives smaller error constants in the proposed method. The new integrators are numerically tested for a few examples, and the solutions are compared with those of the well-known multistep methods. Moreover, the CPU time and absolute integration error are compared in the satellite orbit propagation problem using various integration methods. The CHAMP mission, i.e., a German small-satellite mission for geoscientific and atmospheric research and applications, is considered as a case study for comparing the achievable accuracy of the proposed method with the existing method for solving the two-body problem.     

A reappraisal of active faults in central-east Iran (Kerman province)

Fault lineaments are the main input data in earthquake engineering and seismology studies. This study presents a digitally-based active fault map of the Kerman region in central-east Iran which experienced several devastating earthquakes on poorly exposed and/or not identified active faults. Using Landsat 8 data, we have carried out the image-based procedures of fault mapping, which include applying the contrast stretching technique, the principal component analysis, the color composite technique, the spectral rationing, and creating the false-color composite images. Besides, we have cross-checked the resulting map with the geological maps provided by the Geological Survey of Iran to decrease the associated uncertainties. The resulting map includes 123 fault segments, still, a part of which has been expressed in the previously compiled active-fault maps of Iran. Indeed, the new one is mapping the poorly exposed active faults, so-called secondary faults, which are able to produce strong events. These faults are primarily associated with poorly defined areas that accommodate low levels of seismicity; however, sporadic strong events are likely to occur. It has also been investigated that these kinds of faults are seismogenic and are able to produce destructive events. In total, the outcome of this study can also be jointed with seismic studies for investigating parts of the earthquake activity in central-east Iran, in particular for the fault-based approaches in impending earthquake-resistant buildings.     

Bivariate frequency analysis of low flow using copula functions (case study: Dez River Basin,Iran)

Accurate estimation of low flow as a criterion for different objectives in water resource management, including drought is of crucial importance. Despite the complex nature of water deficits, univariate methods have often been used to analyze the frequency of low flows. In this study, low flows of Dez River basin were examined during period of 1956–2012 using copula functions at the upstream of headbranches’ junction. For this purpose, at first 7-day series of low flow was extracted at the studied stations, then their homogeneity was examined by Mann–Kendall test. The results indicated that 7-day low flow series of Dez basin were homogenous. In the next stage, 12 different distribution functions were fitted onto the low flow data. Finally, for Sepid Dasht Sezar (SDS), Sepid Dasht Zaz (SDZ), and Tang Panj Bakhtiyari (TPB) stations, logistic distribution had the best fit, while for Tang Panj Sezar (TPS) station, GEV distribution enjoyed the best fit. After specifying the best fitted marginal distributions, seven different copula functions including Ali–Mikhail–Haq (AMH), Frank, Clayton, Galambos, Farlie–Gumbel–Morgenstern (FGM), Gumbel–Hougaard (GH), and Plackett were used for bivariate frequency analysis of the 7-day low flow series. The results revealed that the GH copula had the best fitness on paired data of SDS and SDZ stations. For TPS and TPB stations, Frank copula has had the best correspondence with empirical copula values. Next, joint and conditional return periods were calculated for the low flow series at the upstream of branches’ junction. The results of this study indicated that the risk of incidence of severe drought is higher in upstream stations (SDZ and SDS) when compared with downstream stations (TPB and TPS) in Dez basin. Generally, application of multivariate analysis allows researchers to investigate hydrological events with a more comprehensive view by considering the simultaneous effect of the influencing factors on the phenomenon of interest. It also enables them to evaluate different combinations of required scenarios for integrated management of basin and planning to cope with the damages caused by natural phenomena.