DYNAMIC VECTOR SIMULATION AND FORECASTING OF MONTHLY FLOWS

Among the various methods for estimating reservoir volumes, the Gould probability matrix (GPM) method has been touted as a powerful method for estimating reservoir volumes. The other methods in vogue are the Behavior analysis (BA) with the latest induction of the Drought magnitude (DM) method. A comparison of the above methods in terms of ease, efficiency, and relative merits from each other is currently lacking in the literature. This paper compares the above three methods with a detailed analysis of the GPM method using the monthly flows from 16 Canadian rivers at the draft ratios of 75 and 50% with the probability of failure of 2.5, 5 and 10%. The results reported in this paper indicate that fifteen zones are sufficient in the GPM method to yield the reservoir capacity for the Canadian rivers while requiring no standardization of the data, similar to the BA method. In the DM method, standardized monthly flow sequences in combination with a scaling parameter Φ yielded effective drought length, which, when multiplied by drought intensity and the average of 12 monthly standard deviations, resulted in the appropriate values of reservoir capacity. The results of this paper affirm that the GPM method offers little special merit in obtaining reservoir capacity in view of the rigor of computational efforts and uncertainty in the correction factors for significantly autocorrelated (dependent) annual flows. The DM method was found to be comparable to the BA method, though it requires standardization of the monthly flow data. The study suggests that all three methods result in comparable estimates of reservoir capacity for nearly independent annual flows with a slight edge to the Behavior analysis (BA) method.

Assessment of land use and climate change effects on land subsidence using a hydrological model and radar technique

Conditional Recursive Estimation of Dynamic Models with Autocorrelated Perturbations

Symmetric positive-definite (SPD) matrices are ubiquitous in Computer Vision, Machine Learning and Medical Image Analysis. Finding the center/average of a population of such matrices is a common theme in many algorithms such as clustering, segmentation, principal geodesic analysis, etc. The center of a population of such matrices can be defined using a variety of distance/divergence measures as the minimizer of the sum of squared distances/divergences from the unknown center to the members of the population. It is well known that the computation of the Karcher mean for the space of SPD matrices which is a negatively-curved Riemannian manifold is computationally expensive. Recently, the LogDet divergence-based center was shown to be a computationally attractive alternative. However, the LogDet-based mean of more than two matrices can not be computed in closed form, which makes it computationally less attractive for large populations. In this paper we present a novel recursive estimator for center based on the Stein distance - which is the square root of the LogDet divergence - that is significantly faster than the batch mode computation of this center. The key theoretical contribution is a closed-form solution for the weighted Stein center of two SPD matrices, which is used in the recursive computation of the Stein center for a population of SPD matrices. Additionally, we show experimental evidence of the convergence of our recursive Stein center estimator to the batch mode Stein center. We present applications of our recursive estimator to K-means clustering and image indexing depicting significant time gains over corresponding algorithms that use the batch mode computations. For the latter application, we develop novel hashing functions using the Stein distance and apply it to publicly available data sets, and experimental results have shown favorable comparisons to other competing methods.

Recursive estimation as an aid to exploratory data analysis: an application to market share models

The ensemble Kalman filter (EnKF) has recently been proposed as a promising parameter estimation approach for constraining the description of rock flow properties, such as permeability and porosity, to reproduce flow measurements that are modeled as nonlinear functions of these properties. One of the key factors that strongly affect the performance of the EnKF is the quality or representativeness of the prior ensemble of property fields used to initialize the EnKF assimilation procedure. The initial ensemble is commonly constructed by assuming a known geological continuity model such as a variogram. However, geologic continuity models are derived from incomplete information and imperfect modeling assumptions, which can introduce a significant level of uncertainty into the produced models. Neglecting this important source of uncertainty can lead to systematic errors and questionable estimation results. In this paper, we investigate the performance of the EnKF under varying levels of uncertainty in the variogram model parameters. We first attempt to directly estimate variogram model parameters from flow data and show that the complex and nonunique relation they have with the flow data provides little sensitivity for an effective inversion with the EnKF. We then assess the performance of the EnKF for estimation of permeability values under uncertain and incorrect initial variogram parameters and show that any bias in specifying variogram parameters tends to persist throughout the EnKF analysis even though locally reasonable permeability updates may be obtained near observation points. More importantly, we show that when variogram parameters are specified probabilistically to account for the full range of structural variability in the initial permeability ensemble, the EnKF update results are quite promising. The results suggest that under uncertain geologic continuity, the EnKF tends to perform better if a very diverse set of property fields is used to form the initial ensemble than when a deterministic and potentially erroneous variogram model is used. Therefore, in applying the EnKF to model calibration problems, it is preferable to overestimate the uncertainty in geologic continuity and to initialize the EnKF procedure with a wide range of variability in property description than to overlook the variogram uncertainty at the risk of introducing systematic bias that cannot be corrected by the EnKF updates. The practical implications of the results in this paper are significant for designing the EnKF for realistic ensemble model calibration problems where the level of uncertainty in the initial ensemble is usually not known a priori.

Four-dimensional (4D) flow magnetic resonance imaging (MRI) often relies on the injection of gadolinium- or iron-oxide-based contrast agents to improve vessel delineation. In this work, a novel technique is developed to acquire and reconstruct 4D flow data with excellent dynamic visualization of blood vessels but without the need for contrast injection. Synchronization of Neighboring Acquisitions by Physiological Signals (SyNAPS) uses pilot tone (PT) navigation to retrospectively synchronize the reconstruction of two free-running three-dimensional radial acquisitions, to create co-registered anatomy and flow images. Thirteen volunteers and two Marfan syndrome patients were scanned without contrast agent using one free-running fast interrupted steady-state (FISS) sequence and one free-running phase-contrast MRI (PC-MRI) sequence. PT signals spanning the two sequences were recorded for retrospective respiratory motion correction and cardiac binning. The magnitude and phase images reconstructed, respectively, from FISS and PC-MRI, were synchronized to create SyNAPS 4D flow datasets. Conventional two-dimensional (2D) flow data were acquired for reference in ascending (AAo) and descending aorta (DAo). The blood-to-myocardium contrast ratio, dynamic vessel area, net volume, and peak flow were used to compare SyNAPS 4D flow with Native 4D flow (without FISS information) and 2D flow. A score of 0-4 was given to each dataset by two blinded experts regarding the feasibility of performing vessel delineation. Blood-to-myocardium contrast ratio for SyNAPS 4D flow magnitude images (1.5±0.3) was significantly higher than for Native 4D flow (0.7±0.1, p<0.01) and was comparable to 2D flow (2.3±0.9, p=0.02). Image quality scores of SyNAPS 4D flow from the experts (M.P.: 1.9±0.3, E.T.: 2.5±0.5) were overall significantly higher than the scores from Native 4D flow (M.P.: 1.6±0.6, p=0.03, E.T.: 0.8±0.4, p<0.01) but still significantly lower than the scores from the reference 2D flow datasets (M.P.: 2.8±0.4, p<0.01, E.T.: 3.5±0.7, p<0.01). The Pearson correlation coefficient between the dynamic vessel area measured on SyNAPS 4D flow and that from 2D flow was 0.69±0.24 for the AAo and 0.83±0.10 for the DAo, whereas the Pearson correlation between Native 4D flow and 2D flow measurements was 0.12±0.48 for the AAo and 0.08±0.39 for the DAo. Linear correlations between SyNAPS 4D flow and 2D flow measurements of net volume (r2 =0.83) and peak flow (r2 =0.87) were larger than the correlations between Native 4D flow and 2D flow measurements of net volume (r2 =0.79) and peak flow (r2 =0.76). The feasibility and utility of SyNAPS were demonstrated for joint whole-heart anatomical and flow MRI without requiring electrocardiography gating, respiratory navigators, or contrast agents. Using SyNAPS, a high-contrast anatomical imaging sequence can be used to improve 4D flow measurements that often suffer from poor delineation of vessel boundaries in the absence of contrast agents.

Linearized representations of the stochastic groundwater flow and transport equations have been heavily used in hydrogeology, e.g., for geostatistical inversion or generating conditional realizations. The respective linearizations are commonly defined via Jacobians (numerical sensitivity matrices). This study will show that Jacobian‐based linearizations are biased with nonminimal error variance in the ensemble sense. An alternative linearization approach will be derived from the principles of unbiasedness and minimum error variance. The resulting paradigm prefers empirical cross covariances from Monte Carlo analyses over those from linearized error propagation and points toward methods like ensemble Kalman filters (EnKFs). Unlike conditional simulation in geostatistical applications, EnKFs condition transient state variables rather than geostatistical parameter fields. Recently, modifications toward geostatistical applications have been tested and used. This study completes the transformation of EnKFs to geostatistical conditioning tools on the basis of best unbiased ensemble linearization. To distinguish it from the original EnKF, the new method is called the Kalman ensemble generator (KEG). The new context of best unbiased ensemble linearization provides an additional theoretical foundation to EnKF‐like methods (such as the KEG). Like EnKFs and derivates, the KEG is optimal for Gaussian variables. Toward increased robustness and accuracy in non‐Gaussian and nonlinear cases, sequential updating, acceptance/rejection sampling, successive linearization, and a Levenberg‐Marquardt formalism are added. State variables are updated through simulation with updated parameters, always guaranteeing the physicalness of all state variables. The KEG combines the computational efficiency of linearized methods with the robustness of EnKFs and accuracy of expensive realization‐based methods while drawing on the advantages of conditional simulation over conditional estimation (such as adequate representation of solute dispersion). As proof of concept, a large‐scale numerical test case with 200 synthetic sets of flow and tracer data is conducted and analyzed.

This paper presents and analyzes a comprehensive set of mean flow and turbulence data taken in the entrance region of a square duct. At the location where peaking of the axial centerline velocity occurs, the results show that certain Reynolds stress components and mean rates of strain are elevated above their counterparts for fully developed flow. Analysis of the data indicates that two recently proposed pressure-strain models can be used to model local pressure-strain behavior in both the two-dimensional and three-dimensional regions of the flow. Further analysis shows that the dissipation rate can be modeled in terms of a suitably defined length scale and either the resultant primary shear stress or the turbulence kinetic energy.

The SWAT model with modified tile drain and pothole components (SWAT-M) was evaluated at awatershed scale using 9 years of measured flow and nitrate nitrogen (NO3-N) data sets in Walnut Creekwatershed (WCW). The model was calibrated during the period of 1992 to 1995 and validated during the periodof 1996 to 1999. In addition, comparisons between the modified version and 2000 version (SWAT2000) ofSWAT models were conducted. In assessing overall performance of SWAT models, the center location (site310) and outlet (site 330) of WCW were selected. Nash-Sutcliffe E values of the simulated monthly flows andNO3-N loads were 0.69 to 0.78 and 0.52 to 0.79, respectively, indicating that SWAT-M reasonably wellestimated the monthly flows and NO3-N loads in the large flat landscape of WCW with tile drains and potholes.SWAT-M was capable of simulating daily flow and NO3-N loads but with smaller E values (0.31 to 0.50 and0.20 to 0.46, respectively), compared to the monthly results. By investigating site 210, which was exclusivelyselected for the evaluation of subsurface tile drain, it was concluded that the simulated monthly subsurfaceflows and NO3-N loads( E values ranging from 0.71 to 0.79 and 0.72 to 0.78, respectively) were reasonablywell matched to the measured values. Nevertheless, SWAT-Ms simulations of daily subsurface flows (E valuesof 0.09 to 0.18) and NO3-N loads (E values of 0.37 to 0.43) were less accurate than monthly results. SWAT-Mestimated daily and monthly flows, NO3-N loads, subsurface tile drains and subsurface tile NO3-N loads betterthan SWAT2000.

Application of Neural Networks to Rudder Roll Stabilization

Abstract. The approach of conditional nonlinear optimal perturbation (CNOP) was previously proposed to find the optimal initial perturbation (CNOP-I) in a given constraint. In this paper, we extend the CNOP approach to search for the optimal combined mode of initial perturbations and model parameter perturbations. This optimal combined mode, also named CNOP, has two special cases: one is CNOP-I that only links with initial perturbations and has the largest nonlinear evolution at a prediction time; while the other is merely related to the parameter perturbations and is called CNOP-P, which causes the largest departure from a given reference state at a prediction time. The CNOP approach allows us to explore not only the first kind of predictability related to initial errors, but also the second kind of predictability associated with model parameter errors, moreover, the predictability problems of the coexistence of initial errors and parameter errors. With the CNOP approach, we study the ENSO predictability by a theoretical ENSO model. The results demonstrate that the prediction errors caused by the CNOP errors are only slightly larger than those yielded by the CNOP-I errors and then the model parameter errors may play a minor role in producing significant uncertainties for ENSO predictions. Thus, it is clear that the CNOP errors and their resultant prediction errors illustrate the combined effect on predictability of initial errors and model parameter errors and can be used to explore the relative importance of initial errors and parameter errors in yielding considerable prediction errors, which helps identify the dominant source of the errors that cause prediction uncertainties. It is finally expected that more realistic models will be adopted to investigate this use of CNOP.

Predicting phosphorus concentrations in British rivers resulting from the introduction of improved phosphorus removal from sewage effluent

Several syntactic-based automatic worm signature generators, e.g., Polygraph, have recently been proposed. These systems typically assume that a set of suspicious flows are provided by a flow classifier, e.g., a honeynet or an intrusion detection system, that often introduces "noise" due to difficulties and imprecision inflow classification. The algorithms for extracting the worm signatures from the flow data are designed to cope with the noise. It has been reported that these systems can handle a fairly high noise level, e.g., 80% for Polygraph. In this paper, we show that if noise is introduced deliberately to mislead a worm signature generator, a much lower noise level, e.g., 50%, can already prevent the system from reliably generating useful worm signatures. Using Polygraph as a case study, we describe a new and general class of attacks whereby a worm can combine polymorphism and misleading behavior to intentionally pollute the dataset of suspicious flows during its propagation and successfully mislead the automatic signature generation process. This study suggests that unless an accurate and robust flow classification process is in place, automatic syntactic-based signature generators are vulnerable to such noise injection attacks.

Purpose – The purpose of this paper is to propose a novel approach to improve prediction accuracy of grey power models including GM(1, 1) and grey Verhulst model. Design/methodology/approach – The modified new models are proposed by optimizing the initial condition and model parameters. The new initial condition consists of the first item and the last item of a sequence generated by applying the first-order accumulative generation operator on the sequence of raw data. Findings – It is shown that the newly modified grey power model is an extension of the previous optimized GM(1, 1) and grey Verhulst model. And the optimized initial condition reflected the principle of new information priority. Practical implications – The result of a numerical example indicates that the modified grey model presented in this paper with better prediction performance. Originality/value – The new initial condition are derived by weighted combination of the first item and the last item. The coefficients of weight obtained by the least square method.