Calibrating flood inundation models: identifying and addressing uncertainty in satellite observed flood extents

Improvements in the resolution of satellite imagery have enabled extraction of water surface elevations at the margins of the flood. Comparison between modelled and observed water surface elevations provides a new means for calibrating and validating flood inundation models, however the uncertainty in this observed data has yet to be addressed. Here a flood inundation model is calibrated using a probabilistic treatment of the observed data. A LiDAR guided snake algorithm is used to determine an outline of a flood event in 2006 on the River Dee, North Wales, UK, using a 12.5m ERS-1 image. Points at approximately 100m intervals along this outline are selected, and the water surface elevation recorded as the LiDAR DEM elevation at each point. With a planar water surface from the gauged upstream to downstream water elevations as an approximation, the water surface elevations at points along this flooded extent are compared to their ‘expected’ value. The pattern of errors between the two show a roughly normal distribution, however when plotted against coordinates there is obvious spatial autocorrelation. The source of this spatial dependency is investigated by comparing errors to the slope gradient and aspect of the LiDAR DEM. A LISFLOOD-FP model of the flood event is set-up to investigate the effect of observed data uncertainty on the calibration of flood inundation models. Multiple simulations are run using different combinations of friction parameters, from which the optimum parameter set will be selected. For each simulation a T-test is used to quantify the fit between modelled and observed water surface elevations. The points chosen for use in this T-test are selected based on their error. The criteria for selection enables evaluation of the sensitivity of the choice of optimum parameter set to uncertainty in the observed data. This work explores the observed data in detail and highlights possible causes of error. The identification of significant error (RMSE = 0.8m) between approximate expected and actual observed elevations from the remotely sensed data emphasises the limitations of using this data in a deterministic manner within the calibration process. These limitations are addressed by developing a new probabilistic approach to using the observed data.

Calibrating flood inundation models: methods for assessing model performance

Satellite observed data for flood events have been used to calibrate and validate flood inundation models, providing valuable information on the spatial extent of the flood. Improvements in the resolution of this satellite imagery have enabled indirect remote sensing of water levels by using an underlying LiDAR DEM to extract the water surface elevation at the flood margin. Further to comparison of the spatial extent, this now allows for direct comparison between modelled and observed water surface elevations. Using a 12.5m ERS-1 image of a flood event in 2006 on the River Dee, North Wales, UK, both of these data types are extracted and each assessed for their value in the calibration of flood inundation models. A LiDAR guided snake algorithm is used to extract an outline of the flood from the satellite image. From the extracted outline a binary grid of wet / dry cells is created at the same resolution as the model, using this the spatial extent of the modelled and observed flood can be compared using a measure of fit between the two binary patterns of flooding. Water heights are extracted using points at intervals of approximately 100m along the extracted outline, and the students T-test is used to compare modelled and observed water surface elevations. A LISFLOOD-FP model of the catchment is set up using LiDAR topographic data resampled to the 12.5m resolution of the satellite image, and calibration of the friction parameter in the model is undertaken using each of the two approaches. Comparison between the two approaches highlights the sensitivity of the spatial measure of fit to uncertainty in the observed data and the potential drawbacks of using the spatial extent when parts of the flood are contained by the topography.

A link between low-frequency mesoscale eddy variability around Madagascar and the large-scale Indian Ocean variability

A connection is shown to exist between the mesoscale eddy activity around Madagascar and the large-scale interannual variability in the Indian Ocean. We use the combined TOPEX/Poseidon-ERS sea surface height (SSH) data for the period 1993–2003. The SSH-fields in the Mozambique Channel and east of Madagascar exhibit a significant interannual oscillation. This is related to the arrival of large-scale anomalies that propagate westward along 10°–15°S in response to the Indian Ocean dipole (IOD) events. Positive (negative) SSH anomalies associated to a positive (negative) IOD phase induce a shift in the intensity and position of the tropical and subtropical gyres. A weakening (strengthening) results in the intensity of the South Equatorial Current and its branches along east Madagascar. In addition, the flow through the narrows of the Mozambique Channel around 17°S increases (decreases) during periods of a stronger and northward (southward) extension of the subtropical (tropical) gyre. Interaction between the currents in the narrows and southward propagating eddies from the northern Channel leads to interannual variability in the eddy kinetic energy of the central Channel in phase with the one in the SSH-field.

Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis

Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.

The impact of uncertainty in satellite data on the assessment of flood inundation models

The performance of flood inundation models is often assessed using satellite observed data; however these data have inherent uncertainty. In this study we assess the impact of this uncertainty when calibrating a flood inundation model (LISFLOOD-FP) for a flood event in December 2006 on the River Dee, North Wales, UK. The flood extent is delineated from an ERS-2 SAR image of the event using an active contour model (snake), and water levels at the flood margin calculated through intersection of the shoreline vector with LiDAR topographic data. Gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. Residuals between the satellite observed data points and those from the reference line are spatially clustered into groups of similar values. We show that model calibration achieved using pattern matching of observed and predicted flood extent is negatively influenced by this spatial dependency in the data. By contrast, model calibration using water elevations produces realistic calibrated optimum friction parameters even when spatial dependency is present. To test the impact of removing spatial dependency a new method of evaluating flood inundation model performance is developed by using multiple random subsamples of the water surface elevation data points. By testing for spatial dependency using Moran’s I, multiple subsamples of water elevations that have no significant spatial dependency are selected. The model is then calibrated against these data and the results averaged. This gives a near identical result to calibration using spatially dependent data, but has the advantage of being a statistically robust assessment of model performance in which we can have more confidence. Moreover, by using the variations found in the subsamples of the observed data it is possible to assess the effects of observational uncertainty on the assessment of flooding risk.

A unified neurofuzzy model for classification

This work proposes a unified neurofuzzy modelling scheme. To begin with, the initial fuzzy base construction method is based on fuzzy clustering utilising a Gaussian mixture model (GMM) combined with the analysis of covariance (ANOVA) decomposition in order to obtain more compact univariate and bivariate membership functions over the subspaces of the input features. The mean and covariance of the Gaussian membership functions are found by the expectation maximisation (EM) algorithm with the merit of revealing the underlying density distribution of system inputs. The resultant set of membership functions forms the basis of the generalised fuzzy model (GFM) inference engine. The model structure and parameters of this neurofuzzy model are identified via the supervised subspace orthogonal least square (OLS) learning. Finally, instead of providing deterministic class label as model output by convention, a logistic regression model is applied to present the classifier’s output, in which the sigmoid type of logistic transfer function scales the outputs of the neurofuzzy model to the class probability. Experimental validation results are presented to demonstrate the effectiveness of the proposed neurofuzzy modelling scheme.

The impact and reach of MOOCs:a developing countries’ perspective

Massively Open Online Courses (MOOCs) are a recent but hugely popular phenomenon in the online learning world. They are hailed by many as a solution for the developing world’s lack of access to education because MOOCs can provide learning opportunities to a massive number of learners from anywhere in the world as long as they can access the course through Internet. However, a close consideration of the ability of learn- ers from most developing countries to make use of MOOCs seems to contradict this rhetoric. This paper discusses features of MOOCs and looks at them from a developing countries’ perspective to conclude that due to a complicated set of conditions (‘access’, language, computer literacy among others) prevailing in developing countries, MOOCs may not be a viable solution for education for a large proportion of people in these ar- eas of the world. The paper further shows the need for more data on the demograph- ics of MOOC participants from developing countries to form a better understanding of MOOCs role in educating people from developing countries.

Improving the spatial distribution of modeled Arctic sea ice thickness

The spatial distribution of ice thickness/draft in the Arctic Ocean is examined using a sea ice model. A comparison of model predictions with submarine observations of sea ice draft made during cruises between 1987 and 1997 reveals that the model has the same deficiencies found in previous studies, namely ice that is too thick in the Beaufort Sea and too thin near the North Pole. We find that increasing the large scale shear strength of the sea ice leads to substantial improvements in the model's spatial distribution of sea ice thickness, and simultaneously improves the agreement between modeled and ERS-derived 1993–2001 mean winter ice thickness.

Modulation of event-related desynchronization during kinematic and kinetic hand movements

Background Event-related desynchronization/synchronization (ERD/ERS) is a relative power decrease/increase of electroencephalogram (EEG) in a specific frequency band during physical motor execution and mental motor imagery, thus it is widely used for the brain-computer interface (BCI) purpose. However what the ERD really reflects and its frequency band specific role have not been agreed and are under investigation. Understanding the underlying mechanism which causes a significant ERD would be crucial to improve the reliability of the ERD-based BCI. We systematically investigated the relationship between conditions of actual repetitive hand movements and resulting ERD. Methods Eleven healthy young participants were asked to close/open their right hand repetitively at three different speeds (Hold, 1/3 Hz, and 1 Hz) and four distinct motor loads (0, 2, 10, and 15 kgf). In each condition, participants repeated 20 experimental trials, each of which consisted of rest (8–10 s), preparation (1 s) and task (6 s) periods. Under the Hold condition, participants were instructed to keep clenching their hand (i.e., isometric contraction) during the task period. Throughout the experiment, EEG signals were recorded from left and right motor areas for offline data analysis. We obtained time courses of EEG power spectrum to discuss the modulation of mu and beta-ERD/ERS due to the task conditions. Results We confirmed salient mu-ERD (8–13 Hz) and slightly weak beta-ERD (14–30 Hz) on both hemispheres during repetitive hand grasping movements. According to a 3 × 4 ANOVA (speed × motor load), both mu and beta-ERD during the task period were significantly weakened under the Hold condition, whereas no significant difference in the kinetics levels and interaction effect was observed. Conclusions This study investigates the effect of changes in kinematics and kinetics on resulting ERD during repetitive hand grasping movements. The experimental results suggest that the strength of ERD may reflect the time differentiation of hand postures in motor planning process or the variation of proprioception resulting from hand movements, rather than the motor command generated in the down stream, which recruits a group of motor neurons.

Vehicle subtype, make and model classification from side profile video

This paper addresses the challenging domain of vehicle classification from pole-mounted roadway cameras, specifically from side-profile views. A new public vehicle dataset is made available consisting of over 10000 side profile images (86 make/model and 9 sub-type classes). 5 state-of-the-art classifiers are applied to the dataset, with the best achieving high classification rates of 98.7% for sub-type and 99.7- 99.9% for make and model recognition, confirming the assertion made that single vehicle side profile images can be used for robust classification.

Estrogens, brain and behavior: studies in fundamental neurobiology and observations related to women's health

Mechanisms and consequences of the effects of estrogen on the brain have been studied both at the fundamental level and with therapeutic applications in mind. Estrogenic hormones binding in particular neurons in a limbic-hypothalamic system and their effects on the electrophysiology and molecular biology of medial hypothalamic neurons were central in establishing the first circuit for a mammalian behavior, the female-typical mating behavior, lordosis. Notably, the ability of estradiol to facilitate transcription from six genes whose products are important for lordosis behavior proved that hormones can turn on genes in specific neurons at specific times, with sensible behavioral consequences. The use of a gene knockout for estrogen receptor alpha (ERalpha) revealed that homozygous mutant females simply would not do lordosis behavior and instead were extremely aggressive, thus identifying a specific gene as essential for a mammalian social behavior. In dramatic contrast, ERbeta knockout females can exhibit normal lordosis behavior. With the understanding, in considerable mechanistic detail, of how the behavior is produced, now we are also studying brain mechanisms for the biologically adaptive influences which constrain reproductive behavior. With respect to cold temperatures and other environmental or metabolic circumstances which are not consistent with successful reproduction, we are interested in thyroid hormone effects in the brain. Competitive relations between two types of transcription factors - thyroid hormone receptors and estrogen receptors have the potential of subserving the blocking effects of inappropriate environmental circumstances on female reproductive behaviors. TRs can compete with ERalpha both for DNA binding to consensus and physiological EREs and for nuclear coactivators. In the presence of both TRs and ERs, in transfection studies, thyroid hormone coadministration can reduce estrogen-stimulated transcription. These competitive relations apparently have behavioral consequences, as thyroid hormones will reduce lordosis, and a TRbeta gene knockout will increase it. In sum, we not only know several genes that participate in the selective control of this sex behavior, but also, for two genes, we know the causal routes. Estrogenic hormones are also the foci of widespread attention for their potential therapeutic effects improving, for example, certain aspects of mood and cognition. The former has an efficient animal analog, demonstrated by the positive effects of estrogen in the Porsolt forced swim test. The latter almost certainly depends upon trophic actions of estrogen on several fundamental features of nerve cell survival and growth. The hypothesis is raised that the synaptic effects of estrogens are secondary to the trophic actions of this type of hormone in the nucleus and nerve cell body.

Differential interaction of estrogen receptor and thyroid hormone receptor isoforms on the rat oxytocin receptor promoter leads to differences in transcriptional regulation

Both the estrogen receptor (ER) and thyroid hormone receptor (TR) are members of the nuclear receptor superfamily. Two isoforms of the ER, alpha and beta, exist. The TRalpha and beta isoforms are products of two distinct genes that are further differentially spliced to give TRalpha1 and alpha2, TRbeta1 and beta2. The TRs have been shown to interfere with ER-mediated transcription from both the consensus estrogen response element (ERE) and the rat preproenkephalin (PPE) promoter, possibly by competing with ER binding to the ERE or by squelching coactivators essential for ER-mediated transcription. The rat oxytocin receptor (OTR) gene is thought to be involved in several facets of reproductive and affiliative behaviors. 17beta-Estradiol-bound ERs upregulate the OTR gene in the ventromedial hypothalamus, a region critical for the induction of lordosis behavior in several species. We investigated the effects of the ligand-binding TR isoforms on the ER-mediated transcription from a physiological promoter of a behaviorally relevant gene such as the OTR. Only ERalpha could induce the OTR gene in two cell lines tested, the CV-1 and the SK-N-BE2C neuroblastoma cell lines. ERbeta was incapable of inducing the gene in either cell line. ERalpha is therefore not equivalent to ERbeta on this physiological promoter. Indeed, in the neural cell line, ERbeta can inhibit ERalpha-mediated induction from the OTR promoter. While the TRalpha1 isoform inhibited ERalpha-mediated induction in the neural cell line, the TRbeta1 isoform stimulated induction, thus demonstrating isoform specificity in the interaction. The use of a DNA-binding mutant, the TR P box mutant, showed that inhibition of ERalpha-mediated induction of the rat OTR gene promoter by the TRalpha1 isoform does not require DNA-binding ability. SRC-1 overexpression relieved TRalpha1-mediated inhibition in both cell lines, suggesting that squelching for coactivators is an important molecular mechanism in TRalpha-mediated inhibition. Such interactions between TR and ER isoforms on the rat OTR promoter provide a mechanism to achieve neuroendocrine integration.

Estrogen receptor-mediated transcription involves the activation of multiple kinase pathways in neuroblastoma cells

While many physiological effects of estrogens (E) are due to regulation of gene transcription by liganded estrogen receptors (ERs), several effects are also mediated, at least in part, by rapid non-genomic actions of E. Though the relative importance of rapid versus genomic effects in the central nervous system is controversial, we showed previously that membrane-limited effects of E, initiated by an estradiol bovine serum albumin conjugate (E2-BSA), could potentiate transcriptional effects of 17beta-estradiol from an estrogen response element (ERE)-reporter in neuroblastoma cells. Here, using specific inhibitors and activators in a pharmacological approach, we show that activation of phosphatidylinositol-3-phosphate kinase (PI3K) and mitogen activated protein kinase (MAPK) pathways, dependent on a Galphaq coupled receptor signaling are important in this transcriptional potentiation. We further demonstrate, using ERalpha phospho-deficient mutants, that E2-BSA mediated phosphorylation of ERalpha is one mechanism to potentiate transcription from an ERE reporter construct. This study provides a possible mechanism by which signaling from the membrane is coupled to transcription in the nucleus, providing an integrated view of hormone signaling in the brain.