Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models

The rapid increase in human population has increased the groundwater resources demand for drinking, agricultural and industrial purposes. The main purpose of this study is to produce groundwater potential map (GPM) using weights-of-evidence (WOE) and evidential belief function (EBF) models based on geographic information system in the Azna Plain, Lorestan Province, Iran. A total number of 370 groundwater wells with discharge more than 10 m³s^?1were considered and out of them, 256 (70%) were randomly selected for training purpose, while the remaining114 (30%) were used for validating the model. In next step, the effective factors on the groundwater potential such as altitude, slope aspect, slope angle, curvature, distance from rivers, drainage density, topographic wetness index, fault distance, fault density, lithology and land use were derived from the spatial geodatabases. Subsequently, the GPM was produced using WOE and EBF models. Finally, the validation of the GPMs was carried out using areas under the ROC curve (AUC). Results showed that the GPM prepared using WOE model has the success rate of 73.62%. Similarly, the AUC plot showed 76.21% prediction accuracy for the EBF model which means both the models performed fairly good predication accuracy. The GPMs are useful sources for planners and engineers in water resource management, land use planning and hazard mitigation purpose.     

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.     

Combining EGM2008 and SRTM/DTM2006.0 residual terrain model data to improve quasigeoid computations in mountainous areas devoid of gravity data

A global geopotential model, like EGM2008, is not capable of representing the high-frequency components of Earth’s gravity field. This is known as the omission error. In mountainous terrain, omission errors in EGM2008, even when expanded to degree 2,190, may reach amplitudes of 10 cm and more for height anomalies. The present paper proposes the utilisation of high-resolution residual terrain model (RTM) data for computing estimates of the omission error in rugged terrain. RTM elevations may be constructed as the difference between the SRTM (Shuttle Radar Topography Mission) elevation model and the DTM2006.0 spherical harmonic topographic expansion. Numerical tests, carried out in the German Alps with a precise gravimetric quasigeoid model (GCG05) and GPS/levelling data as references, demonstrate that RTM-based omission error estimates improve EGM2008 height anomaly differences by 10 cm in many cases. The comparisons of EGM2008-only height anomalies and the GCG05 model showed 3.7 cm standard deviation after a bias-fit. Applying RTM omission error estimates to EGM2008 reduces the standard deviation to 1.9 cm which equates to a significant improvement rate of 47%. Using GPS/levelling data strongly corroborates these findings with an improvement rate of 49%. The proposed RTM approach may be of practical value to improve quasigeoid determination in mountainous areas without sufficient regional gravity data coverage, e.g., in parts of Asia, South America or Africa. As a further application, RTM omission error estimates will allow refined validation of global gravity field models like EGM2008 from GPS/levelling data.     

Neutron stars in the multidimensional einstein theory of gravitation

The radius, mass, total number of baryons, and other parameters of static, spherically symmetric, superdense stars are calculated. A model with one Ricci-flat inner space of arbitrary dimensionality and the approximation p₁=?0.5ε + ap for additional components of the energy — momentum tensor are used (ε and ρ are the total energy density and the pressure of the stellar matter and a is a fitting parameter). In the case of white dwarfs, the results of the multidimensional theory do not depend on the dimensionality D of space-time for ?10 ? a ? 10 and coincide with the analogous data of the general theory of relativity (GTR). For neutron stars there is a dependence on D and a. For D>4, in particular, the greatest mass M_max of a neutron star as a function of a has a maximum at 3<a(D) ? 4, which exceeds the greatest mass M _max ⁰ =2.14 M_⊙ in the GTR. A comparison of theoretical results with observational data determines the allowable values of a. Data for PSR 1913 + 16 lead to 0.2 ≤ a ≤ 9.2 in the case of D=26, while the results of [P. C. Joss and S. A. Rappaport, Annu. Rev. Astron. Astrophys.,22, 537 (1984)] lead to the stricter limits 1 ≤ a ≤ 7.4.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Meteorological Office, Pune-411 005

Natural disasters like floods, tornadoes, tropicalcyclones, heat and cold wavewreak havoc and cause tremendous loss ofproperty all over the world. Most ofthe natural disasters are either dueto weather or are triggered due toweather related processes.Extreme weather events claimed thousands oflives and caused damage on vastscale. Recent super cyclone which affectedOrissa in 1999, Bangladesh cyclone of1970 and Hurricane Andrew in 1992 areexamples of some of the more damagingtropical cyclones which affected developingas well as the developed world. Heatand cold waves are also extreme events,which cause enormous losses in terms oflives lost and human discomfort and ailmentsarising out of them. The heat waveof 1995 and 1998 are still fresh in the mindof the Indian public. The estimated lossof human lives due to heat wave in 1998 was morethan 15,000. Economic losses asa result of these disasters and in particular inassociation with tropical cyclones haveincreased enormously over the last three decades.During 1961–1991, total loss oflives from drought alone was 1,333,728 overthe whole world. In terms of economiclosses, there is 8–10 fold increase from thebase figure of 1960. The socio-economicimpact of natural disaster is complex dependingupon the vulnerability of the placeand mitigation strategies that are put in place.Meteorology plays a crucial role in forewarningpeople about the severe/extremeweather systems and a constant endeavour by themeteorological services worldover has gone a long way towards minimizing thelosses caused by natural disasters.The paper summarises the natural disasterstatistics over south Asia and the possibleprediction strategies for combating theirsocio-economic impacts.     

The interaction of the solar wind with a comet

The flow of plasma on the sunward side of a comet is investigated by means of an axialsymmetric model based on hydrodynamics modified by source terms. The model assumes a given curvature of the isobaric surfaces, which corresponds to paraboloids around the nucleus of the comet. The flow on the axis can be represented by a solution of a system of seven ordinary differential equations (respectively five in case of pure photo-ionization). The flow pattern always contains a widely detached bow shock and a contact discontinuity separating a cavity with purely cometary plasma from the transition region containing also solar wind ions. The model is applied to the special case where the cometary gas is ionized by the solar UV radiation only. Numerical solutions are integrated for five levels of production of neutral gas by the comet and for seven typical situations in the undisturbed solar wind. The results imply standoff distances of the stagnation point from the nucleus of the order of 10 000 km or more, and distances of the bow shock of the order of 10⁶–10⁷ km.     

Qualitätsanforderungen an Oberflächengewässer: Zielvorgaben,Qualitätsziele und chemische Gewässergüteklassifizierung

Quality Requirements for Fresh Waters: Water Quality Targets, Water Quality Objectives, and Chemical Water Quality Classification In the Federal Republic of Germany, water quality requirements for the protection of inland surface waters against hazardous substances are formulated on the basis of a quality targets derivation concept developed jointly by the Federal Government and the Federal States. The quality requirements were termed “water quality targets” in order to make it clear that the values derived are orientational values rather than legally binding limit values. The international comparison of quality requirements for surface waters shows that, on the whole, the national quality targets ensure a high level of protection. According to present scientific knowledge, impairments of uses, such as supply of drinking water, or risks to aquatic communities need not to be expected if the quality targets are complied with. A comparison of water quality data with the water quality targets makes it possible, on the one hand, to identify those substances whose inputs must be further reduced; on the other hand, it also shows that, for a number of substances, there is no need at present for concern over their adversely water quality. A further differentiation of the aquatic hazard potential of pollutants allows a water quality classification system to be developed on the basis of the quality targets derivation concept. The basic elements of this water quality classification system are presented, and its application is explained by way of examples.     

Sea level variability at Antalya and Mente? tide gauges in Turkey: atmospheric, steric and land motion contributions

Sea level trends and interannual variability at Antalya and Menteş tide gauges are investigated during the 1985–2001 period, quantifying the roles of atmospheric, steric and local land motion contributions. Tide gauge sea level measurements, temperature/salinity climatologies and GPS data are used in the analyses and the results are compared with the output of a barotropic model forced by atmospheric pressure and wind. The overall sea level trends at two tide gauges collocated with GPS are in the range of 5.5 to 7.9 mm/yr during the study period, but showing different behaviour in the sub-periods 1985–1993 and 1993–2001 due to variations in the contributing factors both in space and time. After the removal of the atmospheric forcing and steric contribution from sea level records, the resulting trends vary between 1.9 to 4.5 mm/yr in Antalya and −1.2 to −11.6 mm/yr in Menteş depending on the period considered. Vertical land movement estimated from GPS data seems to explain the high positive residual trend in Antalya during the whole period. On the other hand, the source of the highly negative sea level trend of about −14 mm/yr in Menteş during 1985–1993 could not be resolved with the available datasets. Interannual variability of wind and atmospheric pressure appear to dominate the sea level at both tide gauges during the study period. Atmospheric and steric contributions together account for ∼50% of the total sea level variance at interannual time scales. Mass induced sea level variations which were not considered in this study may help to close the sea level trend budgets as well as to better explain the interannual sea level variance.