GIS-based landslide susceptibility model considering effective contributing area for drainage time

This study employed GIS modelling to ascertain landslide susceptibility on Mt. Umyeon, south of Seoul, South Korea. In this study, an effective contributing area (ECA) for certain drainage time was purposed as a temporal causative factor and then used for modelling in combination with spatial causative factors such as elevation, slope, plan curvature, drainage proximity, forest type, soil type and geology. Landslide inventory map of 163 landslide locations was prepared using aerial photographic interpretation and field verifications after that digitized using GIS environment in 1:5000 scale. A presence-only-based maximum entropy model was used to establish and analyse the relationship between landslides and causative factors. Before final modelling, a jackknife test was performed to measure the variable contributions, which showed that the slope was the most significant spatial causative factor, and ECA with a drainage time of 12 h was the most significant temporal causative factor. The performances of the final models, with and without significant ECA, were assessed by plotting a receiver operating characteristic curve to be 75.5 and 81.2%, respectively.     

Surface runoff prediction regarding LULC and climate dynamics using coupled LTM,optimized ARIMA,and GIS-based SCS-CN models in tropical region

The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff.     

A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.     

Some magnetized bulk viscous string cosmological models in?General Relativity

Some Bianchi type-I viscous fluid string cosmological models with magnetic field are investigated. The viscosity coefficient of bulk viscous fluid is assumed to be a power function of mass density ξ(t)=ξ ₀ ρ ^m , where ξ ₀ and m are constants. To get a determinate model, we assume conditions ρ=(1+ω)λ, where ρ is rest energy density, ω a positive constant and λ the string tension density and expansion θ is proportional to eigen value σ ₁¹ of the shear tensor σ _j ⁱ . The behaviour of the models from physical and geometrical aspects in presence and absence of magnetic field is discussed.      

The Fe-Line Feature in the X-Ray Spectrum of Solar Flares: First Results from the SOXS Mission

We present the first results from the low-energy detector payload of the solar X-ray spectrometer (SOXS) mission, which was launched onboard the GSAT-2 Indian spacecraft on May 08, 2003 by the GSLV-D2 rocket to study solar flares. The SOXS low-energy detector (SLD) payload was designed, developed, and fabricated by the Physical Research Laboratory (PRL) in collaboration with the Space Application Centre (SAC), Ahmedabad and the Indian Space Research Organization (ISRO) Satellite Centre (ISAC), Bangalore. The SLD payload employs state-of-the-art, solid-state detectors, viz., Si PIN and Cadmium-Zinc-Telluride (CZT) devices that operate at near room temperature (−20 ^°C). The energy ranges of the Si PIN and CZT detectors are 4 – 25 and 4 – 56 keV, respectively. The Si PIN provides sub-keV energy resolution, while the CZT provides ~1.7 keV energy resolution throughout the energy range. The high sensitivity and sub-keV energy resolution of the Si PIN detector allows measuring the intensity, peak energy, and the equivalent width of the Fe-line complex at approximately 6.7 keV, as a function of time in all ten M-class flares studied in this investigation. The peak energy (E _p) of the Fe-line feature varies between 6.4 and 6.7 keV with increase in temperature from 9 to 58 MK. We found that the equivalent width (w) of the Fe-line feature increases exponentially with temperature up to 30 MK and then increases very slowly up to 40 MK. It remains between 3.5 and 4 keV in the temperature range of 30 – 45 MK. We compare our measurements of w with calculations made earlier by various investigators and propose that these measurements may improve theoretical models. We interpret the variation of both E _p and w with temperature as being to the changes in the ionization and recombination conditions in the plasma during the flare, and as a consequence, the contribution from different ionic emission lines also varies.     

Branching ratios for the photoionization cross section of atomic oxygen

Branching ratios $\text{σ(}\text{O}{}^0{}^3\text{P}\text{→}\text{O}{}^{\mathrm{+}}{}^2\text{D}{}^0\text{)}\text{σ(}\text{O}{}^0{}^3\text{P}\text{→}\text{O}{}^{\mathrm{+}}{}^4\text{S}{}^0\text{)}$ and $\text{σ(}\text{O}{}^0{}^3\text{P}\text{→}\text{O}{}^{\mathrm{+}}{}^2\text{P}{}^0\text{)}\text{σ (}\text{O}{}^0{}^3\text{P}\text{→}{}^4\text{S}{}^0\text{)}$ are calculated at 584 Å and 304 A employing the close-coupling approximation to compute the photoionization cross section values. The coupled channels include the states dominated by the ground configuration 1s²2s²p³ of O⁺and the next excited configuration ls²2s2p⁴. It is found that the partial c section $\text{σ(}{}^2\text{D}{}^0\text{)}$ decreases more rapidly than $\text{σ(}{}^2\text{P}{}^0\text{)}$, and at the lower wavelength 304 Å, the ratio $\text{σ(}{}^2\text{D}{}^0\text{)}\text{σ(}{}^4\text{S}{}^0\text{)}\text{<}\text{σ(}{}^2\text{P}{}^0\text{)}\text{σ(}{}^4\text{S}{}^0\text{)}$. Present results at 304 Å differ considerably from previous work.     

Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.     

Stability of slopes in a fire-prone mine in Jharia Coalfield, India

Stability of slope in an opencast mine is always associated with safety and economics. The steeper slope is always preferred from economic point of view but prone to failure, whereas flatter slopes are uneconomical. A proper understanding of slope which will be a steep enough to be stable is required for safety, economy, and stability of men and machineries. The Rajapur opencast mine is one of the important mines in terms of good quality coal but has problems of water seepage, fire, and weak overburden materials. The existing coal mine has three working seams which are mostly thick and occur at shallow depths of about 50–60 m. Overall slope angle of the working faces as well as final pit is very steep which leads to failures. In the present paper, an attempt has been made to characterize the materials of the mine for simulation of existing slopes. The rock samples from the coal measures were collected to determine the petrophysical characteristics of various rock units. All the pertinent geological parameters from the exposed face were also collected during field visit to assess the slope mass rating (SMR). A two-dimensional finite difference tool was employed to simulate the existing slope geometry as well as relevant parameters of the rock units. The numerical simulation indicates various vulnerable points which are prone to failure as well as displacements at various points along the slope. The results of simulations are corroborated with the SMR value. The results are well matching with the field condition.