Present paper uses powerful technique of interval neural network (INN) to simulate and estimate structural response of multi-storey shear buildings subject to earthquake motion. The INN is first trained for a real earthquake data, viz., the ground acceleration as input and the numerically generated responses of different floors of multi-storey buildings as output. Till date, no model exists to handle positive and negative data in the INN. As such here, the bipolar data in [−1, 1] are converted first to unipolar form, i.e., to [0, 1] by means of a novel transformation for the first time to handle the above training patterns in normalized form. Once the training is done, again the unipolar data areconverted back to its bipolar form by using the inverse transformation. The trained INN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by INN model are good for practical purposes.

A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six observations and six states of the model. The most probable observation and state sequence has been computed using Forward and Viterbi algorithms, respectively. Baum–Welch algorithm has been used for optimizing the model parameters. The model has been validated for two winters (2012–2013 and 2013–2014) by computing root mean square error (RMSE), accuracy measures such as percent correct (PC), critical success index (CSI) and Heidke skill score (HSS). The RMSE of the model has also been calculated using leave-one-out cross-validation method. Snowfall predicted by the model during hazardous snowfall events in different parts of the Himalaya matches well with the observed one. The HSS of the model for all the stations implies that the optimized model has better forecasting skill than random forecast for both the days. The RMSE of the optimized model has also been found smaller than the persistence forecast and standard deviation for both the days.

Shared nearest neighbour (SNN) cluster algorithm has been applied to seasonal (June–September) rainfall departures over 30 sub-divisions of India to identify the contiguous homogeneous cluster regions over India. Five cluster regions are identified. Rainfall departure series for these cluster regions are prepared by area weighted average rainfall departures over respective sub-divisions in each cluster. The interannual and decadal variability in rainfall departures over five cluster regions is discussed. In order to consider the combined effect of North Atlantic Oscillation (NAO) and Southern Oscillation (SO), an index called effective strength index (ESI) has been defined. It has been observed that the circulation is drastically different in positive and negative phases of ESI-tendency from January to April. Hence, for each phaseof ESI-tendency (positive and negative), separate prediction models have been developed for predicting summer monsoon rainfall over identified clusters. The performance of these models have been tested and found to be encouraging.

A new species of fossil palm rhizome having root-mat under the organ genus Rhizopalamoxylon (Rhizopalmoxylon nypoides sp. nov.) is reported. The specimen shows the closest resemblance with the modern monotypic genus Nypa Wurmb of the Arecaceae. The specimen was collected from the late Maastrichtian–early Danian sediments of Deccan Intertrappean beds, Mothi, Sagar district, Madhya Pradesh, India. Nypa is a mangrove palm naturally found in estuaries and swamps of the tropical region and represents one of the oldest records of the genus from the Deccan Intertrappean beds of centralIndia. The abundance of palms, including Nypa and previously recorded coastal and mangrove elementssuch as Acrostichum, Barringtonia, Cocos, Sonneratia and marine algae (Distichoplax and Peyssonellia)from the Deccan Intertrappean beds indicate marine influence and existence of tropical rainforestecosystem in the vicinity of fossil locality in contrast to the deciduous forests occurring there at present.

Understanding the inherent features of wind speed (variability on different time scales) has become critical for assured wind power availability, grid stability, and effective power management. The study utilizes the wavelet, autocorrelation, and FFT (fast Fourier transform) techniques to analyze and assimilate the fluctuating nature of wind speed data collected over a period of 29–42 years at different locations in the Kingdom of Saudi Arabia. The analyses extracted the intrinsic features of wind speed, including the long-term mean wind speed and fluctuations at different time scales (periods), which is critical for meteorological purposes including wind power resource assessment and weather forecasting. The longterm mean wind speed varied between 1.45 m/s at Mecca station and 3.73 m/s at Taif. The annual variation is the largest (±0.97 m/s) at Taif and the smallest (±0.25 m/s) at Mecca. Similarly, the wind speed fluctuation with different periods was also discussed in detail. The spectral characteristics obtained using FFT reveal that Al-Baha, Najran, Taif and Wadi-Al-Dawasser having a sharp peak at a frequency f = 0.00269 (1/day) retain a more regular annual repetition of wind speed than Bisha, Khamis-Mushait, Madinah, Mecca, and Sharourah. Based on the autocorrelation analysis and FFT results, the stations are divided into three groups: (i) having strong annual modulations (Al-Baha, Najran, Taif and Wadi-Al-Dawasser), (ii) having comparable annual and half-yearly modulations (Bisha, Khamis-Mushait, and Mecca) and (iii) having annual modulation moderately prominent (Madinah and Sharourah).

The present study focusses on field description of small normal fault zones in Upper Miocene–Pliocene sedimentary rocks on the northwestern side of the Red Sea, Egypt. The trend of these fault zones is mainly NW–SE. Paleostress analysis of 17 fault planes and slickenlines indicate that the tension direction is NE–SW. The minimum (σ3) and intermediate (σ2) paleostress axes are generally sub-horizontal and the maximum paleostress axis (σ1) is sub-vertical. The fault zones are composed of damage zones and fault core. The damage zone is characterized by subsidiary faults and fractures that are asymmetrically developed on the hanging wall and footwall of the main fault. The width of the damage zone varies for each fault depending on the lithology, amount of displacement and irregularity of the fault trace. The average ratio between the hanging wall and the footwall damage zones width is about 3:1. The fault core consists of fault gouge and breccia. It is generally concentrated in a narrow zone of ∼0.5 to ∼8 cm width. The overall pattern of the fault core indicates that the width increases with increasing displacement. The faults with displacement <1 m have fault cores ranging from 0.5 to 4.0 cm, while the faults with displacements of >2 m have fault cores ranging from 4.0 to 8.0 cm. The fault zones are associated with sliver fault blocks, clay smear, segmented faults and fault lenses’ structural features. These features are mechanically related to the growth and linkage of the fault arrays. The structural features may represent a neotectonic and indicate that the architecture of the fault zones is developed as several tectonic phases.

Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006–2014 from these stations was performed; the 2013–2014 subhourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.

Withregard to the lack of quality information and data in watersheds, it is of high importance to present a new method for evaluating flood potential. Shannon’s entropy model is a new model in evaluating dangers and it has not yet been used to evaluate flood potential. Therefore, being a new model in determining flood potential, it requires evaluation and investigation in different regions and this study is going to deal with this issue. For to this purpose, 70 flooding areas were recognized and their distribution map was provided by ArcGIS10.2 software in the study area. Information layers of altitude, slope angle, slope aspect, plan curvature, drainage density, distance from the river, topographic wetness index (TWI), lithology, soil type, and land use were recognized as factors affecting flooding and the mentioned maps were provided and digitized by GIS environment. Then, flood susceptibility forecasting map was provided and model accuracy evaluation was conducted using ROC curve and 30% flooding areas express good precision of the model (73.5%) for the study area.

A number of models have been established to simulate the behaviour of solute transport due to chemical pollution, both in croplands and groundwater systems. An approximate polynomial solution to convection–dispersion equation (CDE) based on boundary layer theory has been verified for the use to describe solute transport in semi-infinite systems such as soil column. However, previous studies have only proposed low order polynomial solutions such as parabolic and cubic polynomials. This paper presents a general polynomial boundary layer solution to CDE. Comparison with exact solution suggests the prediction accuracy of the boundary layer solution varies with the order of polynomial expression and soil transport parameters. The results show that prediction accuracy increases with increasing order up to parabolic or cubic polynomial function and with no distinct relationship between accuracy and order for higher order polynomials ($n\geqslant 3$). Comparison of two critical solute transport parameters (i.e., dispersion coefficient and retardation factor), estimated by the boundary layer solution and obtained by CXTFIT curve-fitting, shows a good agreement. The study shows that the general solution can determine the appropriate orders of polynomials for approximate CDE solutions that best describe solute concentration profiles and optimal solute transport parameters. Furthermore, the general polynomial solution to CDE provides a simple approach to solute transport problems, a criterion for choosing the right orders of polynomials for soils with different transport parameters. It is also a potential approach for estimating solute transport parameters of soils in the field.

The tectonic stress pattern in the Chinese Mainland and kinematic models have been subjected to much debate. In the past several decades, several tectonic stress maps have been figured out; however, they generally suffer a poor time control. In the present study, 421 focal mechanism data up to January 2010 were compiled from the Global/Harvard CMT catalogue, and 396 of them were grouped into 23 distinct regions in function of geographic proximity. Reduced stress tensors were obtained from formal stress inversion for each region. The results indicated that, in the Chinese Mainland, the directions of maximum principal stress were ∼NE–SW-trending in the northeastern region, ∼NEE–SWW-trending in the North China region, ∼N–S-trending in western Xinjiang, southern Tibet and the southern Yunnan region, ∼NNE–SSW-trending in the northern Tibet and Qinghai region, ∼NW–SE-trending in Gansu region, and ∼E–W-trending in the western Sichuan region. The average tectonic stress regime was strikeslip faulting (SS) in the eastern Chinese Mainland and northern Tibet region, normal faulting (NF) in the southern Tibet, western Xinjiang and Yunnan region, and thrust faulting (TF) in most regions of Xinjiang, Qinghai and Gansu. The results of the present study combined with GPS velocities in the Chinese Mainland supported and could provide new insights into previous tectonic models (e.g., the extrusion model). From the perspective of tectonics, the mutual actions among the Eurasian plate, Pacific plate and Indian plate caused the present-day tectonic stress field in the Chinese Mainland.

Reservoir characterization of sand-shale sequences has always challenged geoscientists due to the presence of anisotropy in the form of shale lenses or shale layers. Water saturation and volume of shale are among the fundamental reservoir properties of interest for sand-shale intervals, and relate to the amount of fluid content and accumulating potentials of such media. This paper suggests an integrated workflow using synthetic data for the characterization of shaley-sand media based on anisotropic rock physics (T-matrix approximation) and seismic reflectivity modelling. A Bayesian inversion scheme for estimating reservoir parameters from amplitude vs. offset (AVO) data was used to obtain the information about uncertainties as well as their most likely values. The results from our workflow give reliable estimates of water saturation from AVO data at small uncertainties, provided background sand porosity values and isotropic overburden properties are known. For volume of shale, the proposed workflow provides reasonable estimates even when larger uncertainties are present in AVO data.

Climate change, particularly due to the changed precipitation trend, can have a severe impact on soil erosion. The effect is more pronounced on the higher slopes of the Himalayan region. The goal of this study was to estimate the impact of climate change on soil erosion in a watershed of the Himalayan region using RUSLE model. The GCM (general circulation model) derived emission scenarios (HadCM3 A2a and B2a SRES) were used for climate projection. The statistical downscaling model (SDSM) was used to downscale the precipitation for three future periods, 2011–2040, 2041–2070, and 2071–2099, at large scale. Rainfall erosivity (R) was calculated for future periods using the SDSM downscaled precipitation data. ASTER digital elevation model (DEM) and Indian Remote Sensing data – IRS LISS IV satellite data were used to generate the spatial input parameters required by RUSLE model. A digital soil-landscape map was prepared to generate spatially distributed soil erodibility (K) factor map of the watershed. Topographic factors, slope length (L) and steepness (S) were derived from DEM. Normalised difference vegetation index (NDVI) derived from the satellite data was used to represent spatial variation vegetation density and condition under various land use/land cover. This variation was used to represent spatial vegetation cover factor. Analysis revealed that the average annual soil loss may increase by 28.38, 25.64 and 20.33% in the 2020s, 2050s and 2080s, respectively under A2 scenario, while under B2 scenario, it may increase by 27.06, 25.31 and 23.38% in the 2020s, 2050s and 2080s, respectively, from the base period (1985–2013). The study provides a comprehensive understanding of the possible future scenario of soil erosion in the mid-Himalaya for scientists and policy makers.

Sandstones of Jhuran Formation from Jara dome, western Kachchh, Gujarat, India were studied for major, trace and rare earth element (REE) geochemistry to deduce their paleo-weathering, tectonic setting, source rock characteristics and provenance. Petrographic analysis shows that sandstones are having quartz grains with minor amount of K-feldspar and lithic fragments in the modal ratio of Q₈₉:F₇:L₄. On the basis of geochemical results, sandstones are classified into arkose, sub-litharenite, wacke and quartz arenite. The corrected CIA values indicate that the weathering at source region was moderate to intense. The distribution of major and REE elements in the samples normalized to upper continental crust (UCC) and chondrite values indicate similar pattern of UCC. The tectonic discrimination diagram based on the elemental concentrations and elemental ratios of Fe₂O₃+MgO vs. TiO₂, SiO₂ vs. log(K₂O/Na₂O), Sc/Cr vs. La/Y, Th–Sc–Zr/10, La–Th–Sc plots Jhuran Formation samples in continental rift and collision settings. The plots of Ni against TiO₂, La/Sc vs. Th/Co and V–Ni–Th∗10 reveals that the sediments of Jhuran Formation were derived from felsic rock sources. Additionally, the diagram of (Gd/Yb)_N against Eu/Eu^∗ suggest the post-Archean provenance as source possibly Nagar Parkar complex for the studied samples.

The Trichinopoly Group (later redesignated as Garudamangalam) has unconformable relationship with underlying Uttatur Group and is divided into lower Kulakanattam Formation and upper Anaipadi Formation. These calcareous sandstones are analysed major, trace and rare earth elements (REEs) to find out CIA, CIW, provenance and tectonic setting. The silica content of fossiliferous calcareous sandstone show wide variation ranging from 12.93 to 42.56%. Alumina content ranged from 3.49 to 8.47%. Higher values of Fe₂O₃ (2.29–22.02%) and low MgO content (0.75–2.44%) are observed in the Garudamangalam Formation. CaO (23.53–45.90) is high in these sandstones due to the presence of calcite as cementing material. Major element geochemistry of clastic rocks (Al₂O₃ vs. Na₂O) plot and trace elemental ratio (Th/U) reveal the moderate to intense weathering of the source rocks. The Cr/Zr ratio of clastic rocks reveal with an average of 1.74 suggesting of felsic provenance. In clastic rocks, high ratios of ΣLREE/ΣHREE, La/Sc, Th/Sc, Th/Co, La/Co and low ratios of Cr/Zr, and positive Eu anomaly ranges from (Eu/Eu* = 1.87–5.30) reveal felsic nature of the source rocks.