Context-aware abstraction and generalization of street networks: two cognitively engineered user-oriented approaches using network Voronoi diagrams

Generalization and spatial contextual awareness are prevalent concepts in geographic information systems. This study adopted a context-dependent user-centred network generalization method to create a sub-network for optimal route finding. The results show an increase in the number of preferred traversed edges. A route presentation approach is suggested that is based on cognitively engineered user-oriented abstraction of street network. Successive abstractions hierarchize the street network to create a hierarchical presentation structure. The route is projected onto one level in the structure. Then, instead of showing the exact route, the network Voronoi regions represented by the projected route are shown. Experience indicates that the suggested method is an efficient way of route presentation for a hierarchical regionalized structure of a human cognitive map.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and <Emphasis Type="Italic">t</Emphasis>-distributed deviations

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student’s) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.     

Equatorial radius of the Earth: A dynamical determination

An intrresting variation on the familiar method of determining the earth's equatorial radius a_e, from a knowledge of the earth's equatorial gravity is suggested. The value of equatorial radius thus found is 6378,142±5 meters. The associated parameters are GM=3.986005±.000004 × 10²⁰ cm³ sec-⁻² which excludes the relative mass of atmosphere ≅10⁻⁶ ξ GM, the equatorial gravity γ_e 978,030.9 milligals (constrained in this solution by the Potsdam Correction of 13.67 milligals as the Potsdam Correction is more directly, orless indirectly, measurable than the equatorial gravity) and an ellipsoidal flattening of f=1/298.255.     

A novel spatial index using spatial analyses and hierarchical fuzzy expert system for obtaining green TOD: a case study in Tehran city

Sustainable development is a vital and challenging factor for managing urban growth smartly. This factor contains three main components, namely economic growth, ecological protection and social justice. Green Transit-Oriented Development (GTOD) is a consummate planning approach in line with those components. Implementation of GTOD in an urban area is underpinned by its quantification. Therefore, a quantitative spatial index based on several indicators related to TOD and Green urbanism concepts should be developed. In this study, Geo-spatial Information Science and hierarchical fuzzy inference system (HFIS) were employed to calculate the indicators and aggregate them, respectively. In order to showcase the feasibility of the proposed method, it was implemented in a case study area in the City of Tehran, Iran. The result of this method is an integrated spatial GTOD index, which measures the neighbourhoods’ GTOD levels. These measurements specify weaknesses and strengths of neighbourhoods’ factors. Therefore, this index helps decision-makers to plan neighbourhoods based on land use and public transit views. Additionally, the HFIS method helps decision-makers to consider criteria and indicators with their inherent uncertainties and aggregate them with much fewer rules. For evaluating the results, the developed GTOD index was assessed with municipal action planning and attraction maps. According to the outcomes of the assessment, it is concluded that the proposed method is adequately robust and efficient for smart and sustainable urban planning.     

Mixed precision parallel preconditioner and linear solver for compositional reservoir simulation

Reservoir simulation role in value creation and strategic management decisions cannot be over emphasized. Simulation of complex challenging reservoirs with millions of grid blocks especially in compositional mode is very time-consuming even with fast modern computers. On the other hand, high price of cluster supercomputers prevents them for being commonly used for fast simulation of such reservoirs. In recent years, the development of many-core processors like cell processors, DSPs, and graphical processing units (GPUs) has provided a very cost-effective hardware platform for fast computational operations. However, programming for such processors is much more difficult than conventional CPUs, and new parallel algorithm design and special parallel implementation methods are needed. Using the computational power of CPUs, GPUs, and/or any other processing unit, Open Computing Language (OpenCL) provides a framework for programming for heterogeneous platforms. In this paper, OpenCL is used to employ the computational power of a GPU to build a preconditioner and solve the linear system arising from compositional formulation of multiphase flow in porous media. The proposed parallel preconditioner is proved to be quite effective, even in heterogeneous porous media. Using data-parallel modules on GPU, the preconditioner/solver runtime reduced at least 1 order of magnitude compared to their serial implementation on CPU.     

Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network

Al-Mansourieh zone is a part of Al-Khalis City within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2.Groundwater is the main water source for agriculture in this zone.Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly.Therefore,it is necessary to estimate the levels of groundwater in wells through observed data.In this study,Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data.These data included the coordinates of wells(x,y),elevations,well depth,discharge and groundwater levels.Three ANN structures(5-3-3-1,5-10-10-1 and 5-11-11-1)were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values.The coefficient of correlation,coefficient determination(R2)and sum-square error(SSE)were used to evaluate the performance of the ANN models.According to the ANN results,the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells.The best predictor was achieved in the structure 5-3-3-1,with R2 about 0.92,0.89,0.84 and 0.91 in training,validation,testing and all processes respectively.The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively.On the other hand,the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater.     

Early Cretaceous sedimentary provenance and structural evolution of the central Sanandaj–Sirjan Zone,Iran: implications for palaeogeographic reconstructions of the northern Neo-Tethyan margin

ABSTRACT

The Early Cretaceous was an important epoch in the evolution of the Earth system in which major tectonic episodes occurred, especially along the Alpine–Himalayan belt. The paucity of reliable palaeogeographic data from the central segment of this geological puzzle, however, hampers the reconstruction of a panoramic view of its Early Cretaceous palaeogeography and geodynamic setting. Here we present multidisciplinary provenance data from Lower Cretaceous strata of the overriding plate of the Neo-Tethyan subduction zone (the Sanandaj–Sirjan Zone; SSZ, of central Iran), including structural, basin-fill evolution, petrographic and geochemical analyses. Sandstone provenance analysis of Lower Cretaceous red beds suggests the occurrence of sub-mature litho-quartzose sandstones attributed to an active continental arc margin in convergent setting predominantly derived from plutonic, quartzose sedimentary and metamorphic rocks exposed in the central SSZ. Weathering indices indicate moderate chemical weathering in the source area which may be related to close source-to-sink relationships or arid climate. Our palaeogeographic reconstructions and original geological mapping indicate that the erosion of uplifted basement rocks exposed in horst blocks provided the sediment sources for the syn-extensional deposition of uppermost Jurassic–lowermost Cretaceous conglomerates and Lower Cretaceous siliciclastic red beds within a continental retro-arc basin during initiation of the ‘Neo-Tethys 2?. The polyphase tectonic reactivation along the principal fault of the study area controlled the syn- and post-extensional tectonostratigraphic evolution that reflect the corresponding mechanical decoupling/coupling along the northern Neo-Tethyan plate margin.     

The Miduk porphyry Cu deposit,Kerman, Iran: A geochemical analysis of the potassic zone including halogen element systematics related to Cu mineralization processes

Miduk hypogene and supergene porphyry Cu–Mo mineralization occurs within the Miocene porphyritic quartz–diorite and host Eocene plagioclase–hornblende phyric andesitic pyroclastic and flow sequence. Both the host rocks were extensively altered by hydrothermal fluids to dominantly potassic, phyllic, and argillic with interstitial to distal propylitic types.     

Application of integrated geoelectrical methods in Khenadarreh (Arak,Iran) graphite deposit exploration

This paper is devoted to application of SP, resistivity and mise-a-la-masse method in Khenadarreh (Arak, Iran) graphite deposit exploration. Mise-a-la-masse method is one of the main geophysical exploration methods used to investigate conductive bodies with outcrops on the surface or in exploratory pits. By integrating mise-a-la-masse results with other geoelectrical methods, such as self-potential and resistivity surveys, useful results can be acquired. Powerful survey design, data processing and interpretation methods can make geoelectrical surveying results more reliable. Here, we applied integrated geoelectrical methods to explore the Khenadarreh-bala graphite deposit in the Sarband region of the Markazi province, Iran. We present a new and simple method for processing and interpreting mise-a-la-masse data, which corrects for the effect of distance from the current electrode in the conductive body. After correction, the residual potential values corresponded with the SP results and the apparent resistivity pseudosections. The geophysical results were confirmed by drill holes in the study area.