Geochemistry and petrogenesis of anorogenic(?) granitoids of west Garo Hills,Meghalaya

The granite in Samingiri — Dilsekgiri area occurs as discordant, isolated pluton within the migmatitic terrain of West Garo Hills district, Meghalaya. The pluton is exposed over 140 sq km (18 km × 8 km). It exhibits structures of solid state and piecemeal stoping effect proximal to the contact and enveloped by a contact metamorphic aureole of albite-epidote-hornfels facies. Modally, it is biotite-monzogranite and biotite-syenogranite with minor biotite, chlorite, epidote and sericite and accessories like zircon, apatite, allanite, pyrite, magnetite and sphene. Geochemically, it is marked by restricted composition (69–76 wt% SiO₂), high alkalies, low Ca, metaluminous to strongly peraluminous (Molar Al₂O₃/CaO+Na₂O+K₂O = 0.95 ? 1.54), high FeO/MgO, high Ga/Al, high contents of Rb, Sr, Ba, Y, Zr and Ce and depleted in Ti and P. The field observation, mineralogical and geochemical aspects indicate the post-tectonic nature of West Garo pluton more like as A-type granite formed by partial melting of lower crustal blocks followed by low to moderate degree of fractional differentiation. Low Ca, alkaline nature and peraluminous character point to A-type nature of West Garo granite significantly different from other granites of Meghalaya Plateau. Rb-Sr age (616±86 Ma) of granite, however, corresponds to widespread Middle to Upper Pan African activity, a thermal event prevailed during Late Proterozoic — Early Palaeozoic (500–800 Ma) period, manifested in the form of several granitic intrusions in the basement gneissic complex and the overlying Proterozoic metasediments of the Shillong Group in Meghalaya Plateau.     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.     

Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS

The main goal of this study is to investigate the application of the probabilistic-based frequency ratio (FR) model in groundwater potential mapping at Langat basin in Malaysia using geographical information system. So far, the approach of probabilistic frequency ratio model has not yet been used to delineate groundwater potential in Malaysia. Moreover, this study includes the analysis of the spatial relationships between groundwater yield and various hydrological conditioning factors such as elevation, slope, curvature, river, lineament, geology, soil, and land use for this region. Eight groundwater-related factors were collected and extracted from topographic data, geological data, satellite imagery, and published maps. About 68 groundwater data with high potential yield values of ≥11 m³/h were randomly selected using statistical software of SPSS. Then, the groundwater data were randomly split into a training dataset 70 % (48 borehole data) for training the model and the remaining 30 % (20 borehole data) was used for validation purpose. Finally, the frequency ratio coefficients of the hydrological factors were used to generate the groundwater potential map. The validation dataset which was not used during the FR modeling process was used to validate the groundwater potential map using the prediction rate method. The validation results showed that the area under the curve for frequency model is 84.78 %. As far as the performance of the FR approach is concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative groundwater potential. This information could be used by government agencies as well as private sectors as a guide for groundwater exploration and assessment in Malaysia.     

Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.     

Biostratigraphy and floristic evolution of coal swamp floras of a part of Talcher Basin,India: a window on a Permian temperate ecosystem

The present megafloral assemblage recorded from the Barakar sediments of Dholpahar section along Singda rivulet near Gopal Prasad Village in Talcher Basin comprises of equisetaceous stems, Gangamopteris buriadica, Palaeovittaria kurzii and 19 species of the genus Glossopteris. Record of Gangamopteris, Palaeovittaria and many narrow mesh forms of Glosspteris viz., G. angustifolia, G. churiensis, G. communis, G. recurva, G. spatulata, G. stenoneura, G. tenuifolia, G. vulgaris and G. zeilleri from two older fossiliferous horizons demonstrates that these fossils were preserved during Lower Barakar sedimentation. The report of middle and broad mesh forms of Glossopteris viz., G. barakarensis, G. browniana, G. indica, G. intermittens, G. karharbariensis, G. nakkarea, G. oldhamii, G. taeniensis and G. retifera in the youngest fossiliferous horizons reveals that these fossils were preserved during the deposition of Upper Barakar sediments. The continuation of some of the Karharbari plant fossils in the early phase of Barakar Formation and their disappearance in the flora of Late Barakar suggests a shift in the climatic setup. Palaeoclimate and palaeovegetation of this area are also summarised in this study. Moreover, the fossil assemblages of different fossiliferous beds of Dholpahar section demonstrate the evolution of midrib and meshes in different reticulate leaves.     

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district,Haryana (India)

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\)), differences of circular vertical and horizontal \(\sigma ^{\mathrm{o}} \, (\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}})\) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (\({\hbox {RMS}}_{\mathrm{height}}\)). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., \(\sigma ^{\mathrm{o}}\). Near surface SM measurements were related to \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\) derived using 5.35 GHz (C-band) image of RISAT-1 and \({\hbox {RMS}}_{\mathrm{height}}\). The roughness component derived in terms of \({\hbox {RMS}}_{\mathrm{height}}\) showed a good positive correlation with \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}} \, (R^{2} = 0.65)\). By considering all the major influencing factors (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\)), an SEM was developed where SM (volumetric) predicted values depend on \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\). This SEM showed \(R^{2}\) of 0.87 and adjusted \(R^{2}\) of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (\({\hbox {SM}}_{\mathrm{Observed}}\)) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash–Sutcliffe efficiency (NSE) = 0.91 (\({\approx } 1\)), index of agreement (d) = 1, coefficient of determination \((R^{2}) = 0.87\), mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences \(({\hbox {S}}_{\mathrm{d}}^{2}) = 0.004\). The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on \(\sigma ^{\mathrm{o}}\). By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.     

Geospatial modeling to identify the effects of anthropogenic processes on landscape pattern change and biodiversity

This research used geospatial data to quantify biodiversity changes and landscape pattern change to track anthropogenic impacts of such changes at the Mouteh Wildlife Refuge (MWR), Isfahan, Iran. Satellite image duration of four decades, LandSat_1-5, and IRS-P6 data were used to develop land cover classification maps for 1971, 1987, 1998, and 2011. The number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated and compared for a 40-year period. The results showed an increasing concern with regard to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. The number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated. Overall changes in natural land use between 1971 and 1998 at MWR showed that the number of patches for natural land use has increased, but it also showed a decrease in 2011. Similar changes were observed for seminatural land use. Within the artificial classes, the number and area of patches were higher and the largest patch occurred in 2011. The maximum variation of diversity is related to the year 2011. The results showed an increasing concern with regard to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. Remote sensing and geographic information system offers an important means of detecting and analyzing temporal changes occurring in our landscape.     

A recent scenario of mass wasting and its impact on the transportation in Alborz Mountains, Iran using geo-information technology

Mass movements or mass wasting is being considered as one of the severe forms of natural disasters. Iran is geographically located in the Alps–Himalaya seismicity belt. It has a high potential to mass wasting. This seismic phenomenon creates landslides and rock falls in the high mountains of Alborz and Zagros. These mass movements and various types of slides can be systematically assessed and mapped through traditional mapping frameworks using geo-information technologies. The geo-information-based technology offers the earth scientist to study and map various types of mass movement and stability of slopes. In this study, we used field data coupling with the tectonic-related factors to provide a solution for slope-related hazards. Firstly, various geological and geomorphological factors such as lineaments and faults, vegetation, lithology, slope, drainage, land use/land cover, seismicity and roads network were extracted and compiled using geo-information technology. This is because the factors mentioned above play important role in the instability of the region. Then, the study area was divided into four regions based on the rate of mass wasting and its degree of vulnerability. The results of this study showed that the erosion in Karaj formation is severe. Additionally, this research also reveals that the hydrothermal solutions caused by the erosional activities have influenced the glassy element of tuffs and subsequently changed into the clays. This change has caused the tuffs to be relatively unstable. Further, it is evident that the chemical and physical weathering has had a big impact on it whilst most of the mass wasting has occurred within the unstable tuffs of Karaj formation. Finally, the paper concluded that the recent construction of the new roads in the region has increased the potential danger for generating the mass wastes and thus makes the region more unstable.     

Consolidation and Drainage Characteristics of Expansive Soil Stabilized with Fly Ash and Dolochar

Expansive soils undergo alternate swelling and shrinkage due to cyclic wetting and drying when left to nature. This property of Expansive soil affects its strength and stiffness characteristics thereby causing damage and distress to structures built on them. Industrial wastes can be added scientifically to these soils in modifying and reducing their swelling and shrinkage behaviour and increasing their strength and stiffness. In this technical article, an attempt has been made to study the compressibility and drainage characteristics of these soils using economic and ecofriendly industrial wastes such as Fly Ash and Dolochar as stabilizers. This paper also focuses on many other improved engineering properties of base soil like liquid limit, plasticity index, differential free swell, compaction and consolidation characteristics of Expansive (BC) soil stabilized with Fly Ash and Dolochar in different proportions. The virgin Expansive soil has been collected from eastern part of India (Odisha) and different percentages of Fly Ash (5, 10, 15, 20, 25 and 30 %) and Dolochar (5, 10, 15, 20, 25 and 30 %) were added to it, to predict the influence of these additives on compaction and consolidation characteristics of Expansive soil. Addition of both Fly Ash and Dolochar were found to decrease the index properties such as liquid limit, plastic limit, plasticity index, swelling index and enhancing the consolidation as well as drainage characteristics of Expansive soil. However, the maximum dry density of soil was found to decrease with addition of Fly Ash and increase with addition of Dolochar.