Global distribution of the intensity and frequency of hourly precipitation and their responses to ENSO

Climate Dynamics - We investigate the global distribution of hourly precipitation and its connections with the El Niño–Southern Oscillation (ENSO) using both satellite precipitation...     

Finite orbital angular momentum states and Laguerre–Gaussian potential in two-temperature electron plasmas

Electron-acoustic waves are studied with orbital angular momentum (OAM) in an unmagnetized collisionless uniform plasma, whose constituents are the Boltzmann hot electrons, inertial cold electrons and stationary ions. For this purpose, we employ the fluid equations to obtain a paraxial equation in terms of cold electron density perturbations, which admits both the Gaussian and Laguerre–Gaussian (LG) beam solutions. Furthermore, an approximate solution for the electrostatic potential problem is found, which also allows us to express the components of the electric field in terms of LG potential perturbations. Calculating the energy flux of the electron-acoustic waves, an OAM density for these waves is obtained. Numerically, it is found that the parameters, such as, azimuthal angle, radial and angular mode numbers, and the beam waist strongly modify the LG potential profiles associated with electron-acoustic waves. The present results should be helpful to study the trapping and transportation of plasma particles and energy as well as to understand the electron-acoustic mode excitations produced by the Raman backscattering of laser beams in a uniform plasma.     

西北风沙起蚀观测试验及其研究进展

简要阐述了国家自然科学基金“西北沙漠地表沙粒运移特性及动力热力参数研究”支持的“我国西北三大沙漠地表风蚀起沙观测试验”的科学意义、试验方案和科学目标,总结了该试验在前期取得的部分研究进展和研究成果,概括叙述了该项目在地表沙粒跃移特征、起沙风速、沙尘通量等方面的重要发现和一些新的认识。最后,提出并讨论了在沙漠风蚀起沙观测试验方面需要进一步研究和思考的一些重要问题。     

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga,Ganges Basin,India

The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008-2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R2), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.     

Automatic identification of fake patterns caused by short-width wavelets in seismic data

Despite the popularity of using the Haar wavelet filter in many applications, it sometimes introduces fake patterns into the multi resolution analysis (MRA) of seismic data. In this work, we compared different wavelet filters to demonstrate that these patterns are fake and not part of the original waveforms and to show that they are a result of using the Haar wavelet filter as a short-width wavelet. To achieve this, many seismic waveforms from two different sources: the Egyptian National Seismic Network (ENSN) and the High Sensitivity Seismograph Network Japan (Hi-net) are used with different wavelet filters. We propose an algorithm based on an autoregressive (AR) model to detect these patterns automatically and fully.     

Is the age problem resolved?

The cosmological, astrophysical, and nucleocosmochronological methods for estimating the age of the universe and the corresponding uncertainties are comparatively studied in the present paper. We are led to the conclusion that the new measurements of cosmological parameters, and the recent estimates of the age of globular clusters have led to the gradual disappearance of the age problem from the arena of modern cosmology.     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.     

Rock physics analysis for time-lapse seismic at Schiehallion Field, North Sea

Rock physics analysis plays a vital role in time‐lapse seismic interpretation because it provides the link between changes in rock and fluid properties and the resulting seismic data response. In this case study of the Schiehallion Field, we discuss a number of issues that commonly arise in rock physics analyses for time‐lapse studies. We show that:

• 1 Logarithmic fits of dry bulk (K_dry) and shear (G_dry) moduli vs. effective pressure (P_eff) are superior to polynomial fits.
• 2 2D surface fits of K_dry and G_dry over porosity (φ) and effective pressure using all the core data simultaneously are more useful and accurate than separate 1D fits over φ and P_eff for each individual core.
• 3 One average set (facies) of K_dry(φ, P_eff) and G_dry(φ, P_eff) can be chosen to represent adequately the entire Schiehallion reservoir.
• 4 Saturated velocities and densities modelled by fluid substitution of K_dry(φ, P_eff), G_dry(φ, P_eff) and the dry bulk density ρ_dry(φ) compare favourably with well‐log velocities and densities.
• 5 P‐ and S‐wave impedance values resulting from fluid substitution of K_dry(φ, P_eff), G_dry(φ, P_eff) and ρ_dry(φ) show that the largest impedance changes occur for high porosities and low effective pressures.
• 6 Uncertainties in K_dry(φ, P_eff) and G_dry(φ, P_eff) derived for individual cores can be used to generate error surfaces for these moduli that represent bounds for quantifying uncertainties in seismic modelling or pressure–saturation inversion.