Predictability of marine ecosystems in a changing climate

Ecosystem predictability is the basis for ecosystem management. To study the influence of climate on the variability and predictability of marine ecosystems, various climate indices are related to ecosystem descriptors. Various aspects influencing ecosystem predictability are being discussed. It is shown that predictability of marine ecosystems is altered by large-scale transitions in the atmosphere. A multivariate climate descriptor is developed to compensate for the increase in non-linearity due to a regime shift in 2001/2002 and the resulting decrease in predictability.Die Vorhersagbarkeit von Ökosystemen ist die Grundlage für erfolgreiches Ökosystemmanagement. Um den Einfluss des Klimas auf die Vorhersagbarkeit mariner Ökosysteme zu untersuchen, werden Klimaindizes mit Deskriptoren mariner Ökosysteme in Beziehung gesetzt. Die die Vorhersagbarkeit von Ökosystemen beeinflussenden Faktoren werden diskutiert. Es wird gezeigt, dass großskalige Veränderungen in der Atmosphäre die Vorhersagbarkeit von Ökosystemen beeinflussen. Ein multivariater Index wird entwickelt um die Abnahme der Vorhersagbarkeit durch einen Regime Shift in 2001/2002 zu kompensieren

Advanced methods for analysing and modelling multivariate palaeoclimatic time series

The separation of natural and anthropogenically caused climatic changes is an important task of contemporary climate research. For this purpose, a detailed knowledge of the natural variability of the climate during warm stages is a necessary prerequisite. Beside model simulations and historical documents, this knowledge is mostly derived from analyses of so-called climatic proxy data like tree rings or sediment as well as ice cores. In order to be able to appropriately interpret such sources of palaeoclimatic information, suitable approaches of statistical modelling as well as methods of time series analysis are necessary, which are applicable to short, noisy, and non-stationary uni- and multivariate data sets. Correlations between different climatic proxy data within one or more climatological archives contain significant information about the climatic change on longer time scales. Based on an appropriate statistical decomposition of such multivariate time series, one may estimate dimensions in terms of the number of significant, linear independent components of the considered data set. In the presented work, a corresponding approach is introduced, critically discussed, and extended with respect to the analysis of palaeoclimatic time series. Temporal variations of the resulting measures allow to derive information about climatic changes ...thesi

Active Acoustics for Monitoring Unreported Leaks in Water Distribution Networks

Water distribution networks (WDNs) are critical infrastructure elements conveying water through thousands of kilometers of pipes. Pipes - one of the most critical elements in such systems - are subjected to various structural and environmental degradation mechanisms, eventually leading to leaks and breaks. Timely detection and localization of such leaks and bursts is crucial to managing the loss of this valuable resource, maintaining hydraulic capacity, and mitigating serious health risks which can potentially arise from such events. Much of the literature on leak detection has focused on passive methods; recording and analyzing acoustic signatures produced by leak(s) from passive piezo acoustic or pressure devices. Passive acoustic methods have received disproportionate attention both in terms of research as well as practical implementation for leak (or, bursts) detection and localization. Despite their popularity, passive methods have shown not to be reliable in detecting and localizing small leaks in full-scale systems, primarily due to acoustic signal attenuation and poor signal-to-noise ratios, especially in plastic materials. In this dissertation, an active method is explored, which uses an acoustic source to generate acoustic signatures inside a pipe network. A combination of active source and hydrophone receivers is demonstrated in this thesis as a viable method for monitoring leaks in water distribution pipes. The dissertation presents experimental results from two layouts of pipes, one a simple straight section and another a more complex network with tees and bends, with an acoustic source at one end, and hydrophones at strategic locations along the pipe. For leak detection, the measured reflected and transmitted energy using hydrophone receivers is used to determine the presence of a leak. To this effect, new leak indicators such as power reflection and transmission coefficients, power spectral density, reflected spectral density, and transmission loss are developed. Experimental results show that the method developed in this thesis can detect leaks robustly and has significant potential for use in pressurized water distribution systems. This thesis also presents a new framework for active method-based localization. Starting with a simple straight section for a proof of concept study and moving to lab-based WDNs, several methods are explored that simultaneously detect and locate a leak. The primary difficulty in detecting and estimating the location of a leak is overcome through a statistical treatment of time delays associated with multiple acoustic paths in a reverberant environment and estimated using two approaches: (i) classical signal decomposition technique (Prony's / matrix pencil method (MPM)) and (ii) a clustering pre-processing approach called mean-shift clustering. The former works on the cross-correlation of acoustic data recorded at two locations, while the latter operates on acoustic sensor data from a single location. Both methods are tested and validated using experimental data obtained from a laboratory testbed and are found to detect and localize leaks in plastic pipes effectively. Finally, time delay estimates obtained from Prony's / MPM are used in conjunction with the multilateration (MLAT) technique and extended Kalman filter (EKF) for localization in more complex WDNs. This study shows that the proposed active technique can detect and reliably localize leaks and has the potential to be applied to complex field-scale WDNs

Modelling longitudinal data on respiratory infections to inform health policy

Detecting the start of an outbreak, quantifying its burden, disentangling the contribution of different pathogens and evaluating the effectiveness of an intervention are research questions common to several infectious diseases. The answers to these questions provide the epidemiological understanding to prevent future outbreaks, by informing public health policies such as drug stockpiling, vaccination regimes or non-medical interventions. We investigate the use of statistical models to quantify burden of respiratory disease and evaluate effectiveness of public health interventions, while accounting for the challenges posed by surveillance data. The observational nature of the available information, affected by confounding, makes causal statements difficult. Improvements to routinely employed methodologies are proposed, employing phenomenological models to estimate a counterfactual, i.e. what what would have happened in the absence of a contributing factor or intervention. We apply these methods to different types of studies, to address specific gaps in the literature. S. pneumoniae is the leading cause of respiratory morbidity and mortality globally, especially in young children and in the elderly. To improve the understanding of factors triggering disease progression, we firstly analyse individual-level information about pneumococcal carriage and lower respiratory tract infection with a multi-state model, using data from a cohort study in Thailand. Secondly, we clarify the role of viral coinfection and meteorological conditions in invasive pneumococcal disease (IPD) incidence using English surveillance data. A novel multivariate linear regression model is proposed to estimate the influenza-specific contribution additional to the seasonal IPD burden across age groups. We then quantify the impact of the currently implemented vaccination policy, by estimating the counterfactual of IPD incidence in absence of vaccination. This allows disentangling serotype replacement from the vaccine effect, making use of a synthetic control approach. Finally, an empirical dynamical modelling strategy is employed to quantify the interaction between influenza and pneumococcus. Counterfactual analysis can also be employed to quantify the burden of novel respiratory pathogens. The last application of this approach is to estimate the excess mortality during the the COVID-19 pandemic in England

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields