Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations

The advent of polarimetry makes it possible to categorize hydrometeor inferences more accurately by providing detailed information of the scattering properties. In light of this, the authors have developed a fuzzy logic based system for the recognition of melting layer in the atmosphere. The fuzzy system is based on characterizing melting layer scatterers from non-melting scatterers using five crisp inputs, namely, horizontal reflectivity (Z _H), differential reflectivity (Z _DR), co-polar correlation coefficient (ρ _HV), linear depolarization ratio (LDR) and height of radar measurements (H). For the implementation of melting layer recognition, the study employs the dual polarized signatures from the 3 GHz Chilbolton Advanced Meteorological Radar (CAMRA). Furthermore, a simple but effective averaging procedure for melting level estimation from a volume RHI scan is proposed. The proposed scheme has been evaluated with Weather Research and Forecasting (WRF) model simulated and radio soundings retrieved melting level height over a total of 84 RHI scan-based bright band cases. The results confirm that the estimated melting level heights from the proposed method are in good agreement with the WRF model and radio sounding observations. The 3 GHz radar melting level height estimates correspond with the R ² and RMSE values of 0.92 and 0.24 km, respectively, when compared to the radio soundings, and 0.93 and 0.21 km, respectively, when compared to the WRF model results. Moreover, the related R ² and RMSE values are reported as 0.93 and 0.22 km respectively between the WRF and radio soundings retrievals. This implies that the downscaled WRF modelled melting level height may also be used for operational or research needs.     

Use of neural networks for the prediction of frictional drag and transmission of axial load in horizontal wellbores

The use of mud motors and other tools to accomplish forward motion of the bit in extended reach and horizontal wells allows avoiding large amounts of torque caused by rotation of the whole drill string. The forward motion of the drill string, however, is resisted by excessive amount of friction. In the presence of large compressive axial loads, the drill pipe or coiled tubing tends to buckle into a helix in horizontal boreholes. This causes additional frictional drag resisting the transmission of axial load (resulting from surface slack‐off force) to the bit. As the magnitude of the frictional drag increases, a buckled pipe may become ‘locked‐up’ making it almost impossible to drill further. In case of packers, the frictional drag may inhibit the transmission of set‐up load to the packer. A prior knowledge of the magnitude of frictional drag for a given axial load and radial clearance can help avoid lock‐up conditions and costly failure of the tubular. In this study a neural network model, for the prediction of frictional drag and axial load transmission in horizontal wellbores, is presented. Several neural network architectures were designed and tested to obtain the most accurate prediction. After cross‐validation of the Back Propagation Neural Network (BPNN) algorithm, a two‐hidden layer model was chosen for simultaneous prediction of frictional drag and axial load transmission. A comparison of results obtained from BPNN and General Regression Neural Network (GRNN) algorithms is also presented. Copyright © 2002 John Wiley & Sons, Ltd.     

Young star clusters in the Large Magellanic Cloud: NGC 1805 and 1818

We present colour–magnitude diagrams for two rich (≈10⁴ M_⊙) Large Magellanic Cloud star clusters with ages ≈10⁷ yr, constructed from optical and near-infrared data obtained with the Hubble Space Telescope . These data are part of an HST project to study LMC clusters with a range of ages. In this paper we investigate the massive star content of the young clusters, and determine the cluster ages and metallicities, paying particular attention to Be-star and blue-straggler populations and evidence of age spreads. We compare our data with detailed stellar-population simulations to investigate the turn-off structure of ≈25 Myr stellar systems, highlighting the complexity of the blue-straggler phenomenon.     

On the use of Principal Component Analysis in analysing Cepheid light curves

We show how Principal Component Analysis can be used to analyse the structure of Cepheid light curves. This method is more efficient than Fourier analysis at bringing out changes in light curve shape as a function of period. Using this technique, we study the shape of fundamental and first-overtone mode Cepheid light curves in the Galaxy, LMC and SMC over a wide period range. For fundamentals, we find evidence for structural changes at     It is suggested that the feature at     is associated with a resonance in the Cepheid normal-mode spectrum. For overtones, we recover the Z shape in the R ₂₁ period plane and reproduce the metallicity dependence of this Z shape.     

Ocean Rise Instability in Coastal Clay Slopes and Possible Countermeasures: A Centrifuge Modeling Study

Increase in saturation in natural clayey slopes along coastal zones as a result of tsunamis or storm surges may cause flow slides or failures. One of the common treatments is to increase the overall stability by soil replacement and/or re-compaction, which is often difficult to implement, expensive, and, most importantly, damages the natural vegetation. In this paper, remedial effectiveness of a relatively economical and environmentally friendly method involving insertion of geotextile strips with drainage capability into natural clayey slopes is evaluated through a series of centrifuge tests. The test results demonstrate the effectiveness of the employed technique to increase the stability of slopes and their drainage capability as well as to reduce the deformations under surcharge loadings.     

Monthly total sediment forecasting using adaptive neuro fuzzy inference system

Accurate forecasting of sediment is an important issue for reservoir design and water pollution control in rivers and reservoirs. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct monthly sediment forecasting system. To illustrate the applicability of ANFIS method the Great Menderes basin is chosen as the study area. The models with various input structures are constructed for the purpose of identification of the best structure. The performance of the ANFIS models in training and testing sets are compared with the observed data. To get more accurate evaluation of the results ANFIS models, the best fit model structures are also tested by artificial neural networks (ANN) and multiple linear regression (MLR) methods. The results of three methods are compared, and it is observed that the ANFIS is preferable and can be applied successfully because it provides high accuracy and reliability for forecasting of monthly total sediment.     

Paleoenvironment of deposition of the Dupi Tila Formation,Lalmai Hills,Comilla, Bangladesh

The Dupi Tila Formation is composed of yellow to light brown medium to very fine moderately hard to loose sandstone, siltstone, silty clay, mudstone and shale with some conglomerates with clasts of petrified wood. The lithofacies of matrix supported conglomerate, trough cross bedded conglomerate, massive sandstone, trough cross bedded sandstone, planar cross bedded sandstone, ripple cross laminated sandstone-siltstone, flaser laminated sandstone-shale, lenticular laminated sandstone-siltstone-shale, parallel laminated sandstone-siltstone, wavy laminated shale, parallel laminated blue shale, and mudstone are delineated within this formation. Based on the grain size, sedimentary structures, water depth and genesis of individual facies, facies are grouped into three types of facies associations like (i) coarse-grained conglomerate facies association in relation to tractive current deposits of alluvial fan set up at the base of litho-succession (FAC), (ii) medium to fine-grained sandstone-siltstone-mudstone facies association or facies association in relation to strong tide (FAT) characterizing the middle part of litho-succession, (iii) very fine-grained sandstone-siltstone-mudstone facies association in relation to less frequent weak tide or heterolithic facies association (FAHL) characterizing upper part of litho-succession and shallow marine facies association (FASM) composing the uppermost litho-succession. Presence of gluconite indicates that the depositional environment was shallow to deep marine. The dominant paleoflow direction during the deposition of Dupi Tila Formation was toward southeast to southwestern direction. The rivers were of braided type at the piedmont alluvial depositional set up at the lower part, which later changed to estuarine-tidal flat type environmental set up in the middle part to upper part and paleo-environment was shallow marine in the uppermost part.     

An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia

Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R ²), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.