Real-time multipurpose reservoir operation: a case study / Exploitation en temps reacuteel d'un reservoir agrave buts multiples: eacutetude d'un cas

A real-time operational methodology has been developed for multipurpose reservoir operation for irrigation and hydropower generation with application to the Bhadra reservoir system in the state of Karnataka, India. The methodology consists of three phases of computer modelling. In the first phase, the optimal release policy for a given initial storage and inflow is determined using a stochastic dynamic programming (SDP) model. Streamflow forecasting using an adaptive AutoRegressive Integrated Moving Average (ARIMA) model constitutes the second phase. A real-time simulation model is developed in the third phase using the forecast inflows of phase 2 and the operating policy of phase 1. A comparison of the optimal monthly real-time operation with the historical operation demonstrates the relevance, applicability and the relative advantage of the proposed methodology.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Robust modelling of noisy ARMA signals

In this paper methods are developed for enhancement and analysis of autoregressive moving average (ARMA) signals observed in additive noise which can be represented as mixtures of heavy-tailed non-Gaussian sources and a Gaussian background component. Such models find application in systems such as atmospheric communications channels or early sound recordings which are prone to intermittent impulse noise. Markov Chain Monte Carlo (MCMC) simulation techniques are applied to the joint problem of signal extraction, model parameter estimation and detection of impulses within a fully Bayesian framework. The algorithms require only simple linear iterations for all of the unknowns, including the MA parameters, which is in contrast with existing MCMC methods for analysis of noise-free ARMA models. The methods are illustrated using synthetic data and noise-degraded sound recordings.

Einfluss des Tag-Nacht-Verhaltens von Fischen auf das Ergebnis hydroakustischer Messungen

Echo integration is an established method for stock estimation. However, this method is not free of errors like every other measuring method. Especially the variation between day and night behaviour of fish may lead to large measuring errors. A new method is represented detecting such systematic errors, exemplified by investigations during the international hydroacoustic survey on the spring spawning herring in the Norwegian Sea. For this method all measured sA-values are sorted by starting time of the measuring unit distance. In order to reduce random influences a moving average over five time intervals is computed. When displaying these values in a diagram makes it is very easy to detect systematic errors based on the differences in day-night behaviour. For both species, herring and blue whiting, stock estimations are calculated based on the measured sA-values and the results of the analysed trawl catches. The influence of the differnt day and night behaviour of herring on the results of its biomass estimation is rather low. For blue whiting the measured values were about three time higher during day time than during night time. The result of this investigation should initiate a change of the evaluation procedure for stock estimation based on hydroacoustic measurements.

Use of SARIMA models to assess data-poor fisheries: a case study with a sciaenid fishery off Portugal

Research on assessment and monitoring methods has primarily focused on fisheries with long multivariate data sets. Less research exists on methods applicable to data-poor fisheries with univariate data sets with a small sample size. In this study, we examine the capabilities of seasonal autoregressive integrated moving average (SARIMA) models to fit, forecast, and monitor the landings of such data-poor fisheries. We use a European fishery on meagre (Sciaenidae: Argyrosomus regius), where only a short time series of landings was available to model (n=60 months), as our case-study. We show that despite the limited sample size, a SARIMA model could be found that adequately fitted and forecasted the time series of meagre landings (12-month forecasts; mean error: 3.5 tons (t); annual absolute percentage error: 15.4%). We derive model-based prediction intervals and show how they can be used to detect problematic situations in the fishery. Our results indicate that over the course of one year the meagre landings remained within the prediction limits of the model and therefore indicated no need for urgent management intervention. We discuss the information that SARIMA model structure conveys on the meagre lifecycle and fishery, the methodological requirements of SARIMA forecasting of data-poor fisheries landings, and the capabilities SARIMA models present within current efforts to monitor the world’s data-poorest resources.

Removing observational noise from fisheries-independent time series data using ARIMA models

Abundance indices derived from fishery-independent surveys typically exhibit much higher interannual variability than is consistent with the within-survey variance or the life history of a species. This extra variability is essentially observation noise (i.e. measurement error); it probably reflects environmentally driven factors that affect catchability over time. Unfortunately, high observation noise reduces the ability to detect important changes in the underlying population abundance. In our study, a noise-reduction technique for uncorrelated observation noise that is based on autoregressive integrated moving average (ARIMA) time series modeling is investigated. The approach is applied to 18 time series of finfish abundance, which were derived from trawl survey data from the U.S. northeast continental shelf. Although the a priori assumption of a random-walk-plus-uncorrelated-noise model generally yielded a smoothed result that is pleasing to the eye, we recommend that the most appropriate ARIMA model be identified for the observed time series if the smoothed time series will be used for further analysis of the population dynamics of a species.

Climatic factors associated with phytoplankton community succession during and after the 1977 drought for the San Francisco Bay-Delta Estuary [abstract]

Principal coordinates analysis and multiple regression analysis were used to determine the environmental factors associated with the decline in phytoplankton production during and after the 1977 drought for the San Francisco Bay-Delta Estuary. Physical, chemical and biological data were collected semimonthly or monthly during the spring-summer between 1973 and 1982 from 15 sampling sites located throughout the Bay-Delta. A decline in phytoplankton community diversity and density during the 1977 drought and subsequent years (1978 through 1981) was described using principal coordinates analysis. The best multiple regression which described the changes in phytoplankton community succession contained the variables water temperature, wind velocity and ortho-phosphate concentration. Together these variables accounted for 61 percent of the variation in the phytoplankton community among years described by principal coordinates analysis. An increase in water temperature, wind velocity and ortho-phosphate concentration within the Bay-Delta, beginning in June 1976 and continuing through 1981, was demonstrated using weighted moving averages. From the strong association between phytoplankton community succession and climatic variables it was hypothesized that the decline in phytoplankton production during and after the 1977 drought was associated with climatic changes within the northeast Pacific.

Trend and growth analysis of marine fish production in Bangladesh

The study based on time series marine fish production data during the period of 1983-1984 to 2007-2008 in Bangladesh. For this growth analysis six deterministic time series models are considered. The estimated best fitting models are the cubic, quadratic and quadratic model is appropriate for industrial marine fish production, artisanal marine fish production and total marine fish production in Bangladesh respectively. The study attempts to provide forecasts of marine fish production in Bangladesh for the year of 2008-09 to 2012-13. The magnitude of instability in marine fish production was attempted by computing the coefficient of variation (CV) and the percentage deviation from three years moving average values. The study revealed that the total marine fish production was observed to be relatively stable (CV being 31.85%) compared to the artisanal marine fish production (CV being 32.04%) and industrial marine fish (CV being 47.20%). For the three components of marine fish production the growth rates were different over different time points. The variation of the growth rates in industrial marine fish production was -21.6% to 13.12%, in artisanal marine fish production was 2.39% to 5.29% and in total marine fish production was 11.23% to 24.85% during the study period.

Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter

The algorithm presented in this paper aims to segment the foreground objects in video (e.g., people) given time-varying, textured backgrounds. Examples of time-varying backgrounds include waves on water, clouds moving, trees waving in the wind, automobile traffic, moving crowds, escalators, etc. We have developed a novel foreground-background segmentation algorithm that explicitly accounts for the non-stationary nature and clutter-like appearance of many dynamic textures. The dynamic texture is modeled by an Autoregressive Moving Average Model (ARMA). A robust Kalman filter algorithm iteratively estimates the intrinsic appearance of the dynamic texture, as well as the regions of the foreground objects. Preliminary experiments with this method have demonstrated promising results.

Quantifying the value of improved wind energy forecasts in a pool-based electricity market

This work illustrates the influence of wind forecast errors on system costs, wind curtailment and generator dispatch in a system with high wind penetration. Realistic wind forecasts of different specified accuracy levels are created using an auto-regressive moving average model and these are then used in the creation of day-ahead unit commitment schedules. The schedules are generated for a model of the 2020 Irish electricity system with 33% wind penetration using both stochastic and deterministic approaches. Improvements in wind forecast accuracy are demonstrated to deliver: (i) clear savings in total system costs for deterministic and, to a lesser extent, stochastic scheduling; (ii) a decrease in the level of wind curtailment, with close agreement between stochastic and deterministic scheduling; and (iii) a decrease in the dispatch of open cycle gas turbine generation, evident with deterministic, and to a lesser extent, with stochastic scheduling.

Temporal variations of IEEE 802.11b signal strengths in an in-building environment

Recent popularity of the IEEE 802.11b Wireless Local Area Networks (WLANs) in a host of current-day applications has instigated a suite of research challenges. The 802.11b WLANs are highly reliable and wide spread. In this work, we study the temporal characteristics of RSSI in the real-working environment by conducting a controlled set of experiments. Our results indicate that a significant variability in the RSSI can occur over time. Some of this variability in the RSSI may be due to systematic causes while the other component can be expressed as stochastic noise. We present an analysis of both these aspects of RSSI. We treat the moving average of the RSSI as the systematic causes and the noise as the stochastic causes. We give a reasonable estimate for the moving average to compute the noise accurately. We attribute the changes in the environment such as the movement of people and the noise associated with the NIC circuitry and the network access point as causes for this variability. We find that the results of our analysis are of primary importance to active research areas such as location determination of users in a WLAN. The techniques used in some of the RF-based WLAN location determination systems, exploit the characteristics of the RSSI presented in this work to infer the location of a wireless client in a WLAN. Thus our results form the building blocks for other users of the exact characteristics of the RSSI.

Quasiexperimental Study of the Effects of Antibiotic Use, Gastric Acid-Suppressive Agents, and Infection Control Practices on the Incidence of Clostridium difficile-Associated Diarrhea in Hospitalized Patients

The objective of this study was to evaluate the effects of antimicrobial drug use, gastric acid-suppressive agent use, and infection control practices on the incidence of Clostridium difficile-associated diarrhea (CDAD) in a 426-bed general teaching hospital in Northern Ireland. The study was retrospective and ecological in design. A multivariate autoregressive integrated moving average (time-series analysis) model was built to relate CDAD incidence with antibiotic use, gastric acid-suppressive agent use, and infection control practices within the hospital over a 5-year period (February 2002 to March 2007). The findings of this study showed that temporal variation in CDAD incidence followed temporal variations in expanded-spectrum cephalosporin use (average delay = 2 months; variation of CDAD incidence = 0.01/100 bed-days), broad-spectrum cephalosporin use (average delay = 2 months; variation of CDAD incidence = 0.02/100 bed-days), fluoroquinolone use (average delay = 3 months; variation of CDAD incidence = 0.004/100 bed-days), amoxicillin-clavulanic acid use (average delay = 1 month; variation of CDAD incidence = 0.002/100 bed-days), and macrolide use (average delay = 5 months; variation of CDAD incidence = 0.002/100 bed-days). Temporal relationships were also observed between CDAD incidence and use of histamine-2 receptor antagonists (H2RAs; average delay = 1 month; variation of CDAD incidence = 0.001/100 bed-days). The model explained 78% of the variance in the monthly incidence of CDAD. The findings of this study highlight a temporal relationship between certain classes of antibiotics, H2RAs, and CDAD incidence. The results of this research can help hospitals to set priorities for restricting the use of specific antibiotic classes, based on the size-effect of each class and the delay necessary to observe an effect.

Robust Estimation of HDR in fMRI using H∞ Filters

Estimation and detection of the hemodynamic response (HDR) are of great importance in functional MRI (fMRI) data analysis. In this paper, we propose the use of three H 8 adaptive filters (finite memory, exponentially weighted, and time-varying) for accurate estimation and detection of the HDR. The H 8 approach is used because it safeguards against the worst case disturbances and makes no assumptions on the (statistical) nature of the signals [B. Hassibi and T. Kailath, in Proc. ICASSP, 1995, vol. 2, pp. 949-952; T. Ratnarajah and S. Puthusserypady, in Proc. 8th IEEE Workshop DSP, 1998, pp. 1483-1487]. Performances of the proposed techniques are compared to the conventional t-test method as well as the well-known LMSs and recursive least squares algorithms. Extensive numerical simulations show that the proposed methods result in better HDR estimations and activation detections.

The impact of antibiotic use on the incidence and resistance pattern of extended-spectrum beta-lactamase-producing bacteria in primary and secondary healthcare settings

Aims: The objective of the present study was to study the relationship between hospital antibiotic use, community antibiotic use and the incidence of extended-spectrum beta-lactamase (ESBL)-producing bacteria in hospitals, while assessing the impact of a fluoroquinolone restriction policy on ESBL-producing bacteria incidence rates. METHODS: The study was retrospective and ecological in design. A multivariate autoregressive integrated moving average (ARIMA) model was built to relate antibiotic use to ESB-producing bacteria incidence rates and resistance patterns over a 5 year period (January 2005-December 2009). Results: Analysis showed that the hospital incidence of ESBLs had a positive relationship with the use of fluoroquinolones in the hospital (coefficient = 0.174, P= 0.02), amoxicillin-clavulanic acid in the community (coefficient = 1.03, P= 0.03) and mean co-morbidity scores for hospitalized patients (coefficient = 2.15, P= 0.03) with various time lags. The fluoroquinolone restriction policy was implemented successfully with the mean use of fluoroquinolones (mainly ciprofloxacin) being reduced from 133 to 17 defined daily doses (DDDs)/1000 bed days (P <0.001) and from 0.65 to 0.54 DDDs/1000 inhabitants/day (P= 0.0007), in both the hospital and its surrounding community, respectively. This was associated with an improved ciprofloxacin susceptibility in both settings [ciprofloxacin susceptibility being improved from 16% to 28% in the community (P <0.001)] and with a statistically significant reduction in ESBL-producing bacteria incidence rates. Discussion: This study supports the value of restricting the use of certain antimicrobial classes to control ESBL, and demonstrates the feasibility of reversing resistance patterns post successful antibiotic restriction. The study also highlights the potential value of the time-series analysis in designing efficient antibiotic stewardship. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.