Numerical modelling of moisture motion in heterogeneous soils using 1D-MIRBF method

In the present paper, we develop an efficient and accurate numerical approach based on one-dimensional-moving integrated radial basis function (1D-MIRBF) and fully implicit modified Picard method for simulating fluid movement in heterogeneous soils governed by the highly non-linear Richards equation. The major advantages of the proposed 1D-MIRBF method include (i) a banded sparse system matrix that helps reduce the computational cost; (ii) the Kronecker Delta property of the constructed shape functions, which helps impose the essential boundary conditions in an exact manner; and (iii) high accuracy and fast convergence rate owing to the use of the IRBF approximation. The performance of the present method is demonstrated through several 1--D and 2--D soil infiltration problems. Numerical results obtained are in agreement with other published results in the literature. This solver for moisture motion in soils will be incorporated into a surface-water-flow solver to handle the surface irrigation problem

Predicting water allocation trade prices using a hybrid Artificial Neural Network-Bayesian modelling approach

This paper proposes an integrated (hybrid) Artificial Neural Network-Bayesian (ANN-B) modelling approach to improve the accuracy of predicting seasonal water allocation prices in Australia’s Murry Irrigation Area, which is part of one of the world’s largest interconnected water markets. Three models (basic, intermediate and full), accommodating different levels of data availability, were considered. Data were analyzed using both ANN and hybrid ANN-B approaches. Using the ANN-B modelling approach, which can simulate complex and non-linear processes, water allocation prices were predicted with a high degree of accuracy (RBASIC = 0.93, RINTER. = 0.96 and RFULL = 0.99); this was a higher level of accuracy than realized using ANN. This approach can potentially be integrated with online data systems to predict water allocation prices, enable better water allocation trade decisions, and improve the productivity and profitability of irrigated agriculture

Using 1D-IRBFN method for solving high-order nonlinear differential equations arising in models of active-dissipative systems

We analyse a nonlinear partial differential equation modelling reaction-diffusion systems with nonlocal coupling and reaction fronts of gasless combustion. The equation is of active-dissipative type, nonlinear, with 6th-order spatial derivative. To numerically solve the equations we use the one-dimensional integrated radial basis function network (1D-IRBFN)method. The method has been previously developed and successfully applied to several problems such as structural analysis, viscous and viscoelastic flows and fluid-structure interaction. A commonly used approach is to differentiate a function of interest to obtain approximate derivatives. However, this leads to a reduction in convergence rate for derivatives and this reduction is an increasing function of derivative order. Accordingly, differentiation magnifies errors. To avoid this problem and recognising that integration is a smoothing process, the proposed 1D-IRBFN method uses the integral formulation, where spectral approximants are utilised to represent highest-order derivatives under consideration and then integrated analytically to yield approximate expressions for lower-order derivatives and the function itself. Our preliminary results demonstrate good performance of the 1D-IRBFN algorithm for the equation under consideration. Numerical solutions representing travelling waves are obtained, in agreement with the earlier studies

A control volume scheme using compact integrated radial basis function stencils for solving the Richards equation

A new control volume approach is developed based on compact integrated radial basis function (CIRBF) stencils for solution of the highly nonlinear Richards equation describing transient water flow in variably saturated soils. Unlike the conventional control volume method, which is regarded as second-order accurate, the proposed approach has high-order accuracy owing to the use of a compact integrated radial basis function approximation that enables improved flux predictions. The method is used to solve the Richards equation for transient flow in 1D homogeneous and heterogeneous soil profiles. Numerical results for different boundary conditions, initial conditions and soil types are shown to be in good agreement with Warrick's semi-analytical solution and simulations using the HYDRUS-1D software package. Results obtained with the proposed method were far less dependent upon the grid spacing than the HYDRUS-1D finite element solutions

AMMONIA REMOVAL FROM SWINE WASTEWATER USING AN AEROBIC, ANOXIC FILTER AT A PILOT-SCALE IN THANH LOC BIOSTATION

Joint Research on Environmental Science and Technology for the Eart

Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector

Simulations of autonomous fluid pulses between active elastic walls using the 1D-IRBFN Method

We present numerical solutions of the semi-empirical model of self-propagating fluid pulses (auto-pulses) through the channel simulating an artificial artery. The key mechanism behind the model is the active motion of the walls in line with the earlier model of Roberts. Our model is autonomous, nonlinear and is based on the partial differential equation describing the displacement of the wall in time and along the channel. A theoretical plane configuration is adopted for the walls at rest. For solving the equation we used the One-dimensional Integrated Radial Basis Function Network (1D-IRBFN) method. We demonstrated that different initial conditions always lead to the settling of pulse trains where an individual pulse has certain speed and amplitude controlled by the governing equation. A variety of pulse solutions is obtained using homogeneous and periodic boundary conditions. The dynamics of one, two, and three pulses per period are explored. The fluid mass flux due to the pulses is calculated

Malaria in central Vietnam: analysis of risk factors by multivariate analysis and classification tree models

BACKGROUND: In Central Vietnam, forest malaria remains difficult to control due to the complex interactions between human, vector and environmental factors. METHODS: Prior to a community-based intervention to assess the efficacy of long-lasting insecticidal hammocks, a complete census (18,646 individuals) and a baseline cross-sectional survey for determining malaria prevalence and related risk factors were carried out. Multivariate analysis using survey logistic regression was combined to a classification tree model (CART) to better define the relative importance and inter-relations between the different risk factors. RESULTS: The study population was mostly from the Ra-glai ethnic group (88%), with both low education and socio-economic status and engaged mainly in forest activities (58%). The multivariate analysis confirmed forest activity, bed net use, ethnicity, age and education as risk factors for malaria infections, but could not handle multiple interactions. The CART analysis showed that the most important risk factor for malaria was the wealth category, the wealthiest group being much less infected (8.9%) than the lower and medium wealth category (16.6%). In the former, forest activity and bed net use were the most determinant risk factors for malaria, while in the lower and medium wealth category, insecticide treated nets were most important, although the latter were less protective among Ra-glai people. CONCLUSION: The combination of CART and multivariate analysis constitute a novel analytical approach, providing an accurate and dynamic picture of the main risk factors for malaria infection. Results show that the control of forest malaria remains an extremely complex task that has to address poverty-related risk factors such as education, ethnicity and housing conditions

A modeling framework to quantify the effects of compaction on soil water retention and infiltration

The water retention curve (WRC) of arable soils from the southeastern United States at different levels of compaction (no compaction, and 10 and 20% increases in soil bulk density) was estimated using the van Genuchten-Mualem (VG) model. The VG water retention parameters of the noncompacted soils were obtained first by fitting measured soil hydraulic data. To construct the WRC of the compacted soils, gravimetric values of the permanent wilting point (theta(gw), 1,500 kPa) and the residual (theta(gr)) water content were assumed to remain unchanged with compaction. The VG parameter alpha and exponent eta after compaction were estimated using two approaches. In Approach 1, alpha and eta were estimated from saturation, the permanent wilting point, and the residual water content. In Approach 2, the value of eta was assumed to remain unchanged with compaction, which allowed alpha to be estimated immediately from the VG equation. Approach 2 was found to give slightly better agreement with measured data than Approach 1. The effect of compaction on the saturated hydraulic conductivity (K-s) was predicted using semitheoretical approaches and the VG-WRC function. HYDRUS-1D was further used to simulate vertical infiltration into a single-layered soil profile to determine the impact of compaction on the infiltration characteristics of the soils used in our analyses. Results showed that a 10-20% increase in soil bulk density, due to compaction, reduced cumulative infiltration (I-c) at time T = T-final (steady-state) by 55-82%, and the available water storage capacity by 3-49%, depending upon soil type