Browsing by Author "Daneshkhah, Alireza"
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Item Open Access Approximate uncertainty modeling in risk analysis with vine copulas(Wiley, 2015-09-02) Bedford, Tim; Daneshkhah, Alireza; Wilson, Kevin J.Many applications of risk analysis require us to jointly model multiple uncertain quantities. Bayesian networks and copulas are two common approaches to modeling joint uncertainties with probability distributions. This article focuses on new methodologies for copulas by developing work of Cooke, Bedford, Kurowica, and others on vines as a way of constructing higher dimensional distributions that do not suffer from some of the restrictions of alternatives such as the multivariate Gaussian copula. The article provides a fundamental approximation result, demonstrating that we can approximate any density as closely as we like using vines. It further operationalizes this result by showing how minimum information copulas can be used to provide parametric classes of copulas that have such good levels of approximation. We extend previous approaches using vines by considering nonconstant conditional dependencies, which are particularly relevant in financial risk modeling. We discuss how such models may be quantified, in terms of expert judgment or by fitting data, and illustrate the approach by modeling two financial data sets.Item Open Access Assessing parameter uncertainty on coupled models using minimum information methods(Elsevier Science B.V., Amsterdam., 2014-05-31T00:00:00Z) Bedford, Tim; Wilson, Kevin J.; Daneshkhah, AlirezaProbabilistic inversion is used to take expert uncertainty assessments about observable model outputs and build from them a distribution on the model parameters that captures the uncertainty expressed by the experts. In this paper we look at ways to use minimum information methods to do this, focussing in particular on the problem of ensuring consistency between expert assessments about differing variables, either as outputs from a single model, or potentially as outputs along a chain of models. The paper shows how such a problem can be structured and then illustrates the method with two examples; one involving failure rates of equipment in series systems and the other atmospheric dispersion and deposition.Item Open Access Economic evaluation of mental health effects of flooding using Bayesian networks(MDPI, 2021-07-13) Sedighi, Tabassom; Varga, Liz; Hosseinian-Far, Amin; Daneshkhah, AlirezaThe appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.Item Open Access Implementing precision irrigation in a humid climate - Recent experiences and on-going challenges(Elsevier, 2014-06-22) Daccache, Andre; Knox, Jerry W.; Weatherhead, E. K.; Daneshkhah, Alireza; Hess, TimThere is growing scientific interest in the potential role that precision irrigation (PI) can make towards improving crop productivity, and increasing water and energy efficiency in irrigated agriculture. Most progress has been made in arid and semi-arid climates for use in high value crop production where irrigation costs coupled with concerns regarding water scarcity have stimulated PI innovation and development. In temperate and humid climates where irrigation is supplemental to rainfall, PI is less developed but nevertheless offers scope to make more effective use of rainfall, help reduce the non-beneficial losses associated with irrigation (deep drainage, nitrate leaching) and provide farmers with evidence to demonstrate environmentally sustainable practices to processors and retailers. This paper reports on recent experiences in developing precision irrigation in UK field-scale agriculture, drawing on evidence from field research and modelling studies. By combining data from these sources, a critical evaluation focusing on selected technical, agronomic and engineering challenges that need to be overcome are described, including issues regarding PI scheduling, and the delineation of irrigation management zones to ensure compatibility with existing methods of overhead irrigation. The findings have relevance to other countries where irrigation is supplemental and where precision agriculture is gaining popularity.Item Open Access Mathematical and computational modelling frameworks for integrated sustainability assessment (ISA)(Springer, 2017-02-15) Farsi, Maryam; Hosseinian-Far, Amin; Daneshkhah, Alireza; Sedighi, TabassomSustaining and optimising complex systems are often challenging problems as such systems contain numerous variables that are interacting with each other in a nonlinear manner. Application of integrated sustainability principles in a complex system (e.g., the Earth’s global climate, social organisations, Boeing’s supply chain, automotive products and plants’ operations, etc.) is also a challenging process. This is due to the interactions between numerous parameters such as economic, ecological, technological, environmental and social factors being required for the life assessment of such a system. Functionality and flexibility assessment of a complex system is a major factor for anticipating the systems’ responses to changes and interruptions. This study outlines generic mathematical and computational approaches to solving the nonlinear dynamical behaviour of complex systems. The goal is to explain the modelling and simulation of system’s responses experiencing interaction change or interruption (i.e., interactive disruption). Having this knowledge will allow the optimisation of systems’ efficiency and would ultimately reduce the system’s total costs. Although, many research works have studied integrated sustainability behaviour of complex systems, this study presents a generic mathematical and computational framework to explain the behaviour of the system following interactive changes and interruptions. Moreover, a dynamic adaptive response of the global system over time should be taken into account. This dynamic behaviour can capture the interactive behaviour of components and sub-systems within a complex global system. Such assessment would benefit many systems including information systems. Due to emergence and expansion of big data analytics and cloud computing systems, such life-cycle assessments can be considered as a strategic planning framework before implementation of such information systems.Item Open Access Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model(Elsevier, 2016-06-21) Daneshkhah, Alireza; Remesan, Renji; Chatrabgoun, Omid; Holman, Ian P.This paper highlights the usefulness of the minimum information and parametric pair-copula construction (PCC) to model the joint distribution of flood event properties. Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails. In particular, the minimum information pair-copula model shows greater flexibility and produces better approximation of the joint probability density and corresponding measures have capability for effective hazard assessments. The study demonstrates that any multivariate density can be approximated to any degree of desired precision using minimum information pair-copula model and can be practically used for probabilistic flood hazard assessment.Item Open Access Probabilistic sensitivity analysis of optimized preventive maintenance strategies for deteriorating infrastructure assets(Elsevier, 2017-02-21) Jeffrey, Paul; Stocks, N. J.; Daneshkhah, AlirezaEfficient life-cycle management of civil infrastructure systems under continuous deterioration can be improved by studying the sensitivity of optimised preventive maintenance decisions with respect to changes in model parameters. Sensitivity analysis in maintenance optimisation problems is important because if the calculation of the cost of preventive maintenance strategies is not sufficiently robust, the use of the maintenance model can generate optimised maintenances strategies that are not cost-effective. Probabilistic sensitivity analysis methods (particularly variance based ones), only partially respond to this issue and their use is limited to evaluating the extent to which uncertainty in each input contributes to the overall output's variance. These methods do not take account of the decision-making problem in a straightforward manner. To address this issue, we use the concept of the Expected Value of Perfect Information (EVPI) to perform decision-informed sensitivity analysis: to identify the key parameters of the problem and quantify the value of learning about certain aspects of the life-cycle management of civil infrastructure system. This approach allows us to quantify the benefits of the maintenance strategies in terms of expected costs and in the light of accumulated information about the model parameters and aspects of the system, such as the ageing process. We use a Gamma process model to represent the uncertainty associated with asset deterioration, illustrating the use of EVPI to perform sensitivity analysis on the optimisation problem for age-based and condition-based preventive maintenance strategies. The evaluation of EVPI indices is computationally demanding and Markov Chain Monte Carlo techniques would not be helpful. To overcome this computational difficulty, we approximate the EVPI indices using Gaussian process emulators. The implications of the worked numerical examples discussed in the context of analytical efficiency and organisational learning.Item Open Access Scale impacts on spatial variability in reference evapotranspiration(Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2015-08-17) Hess, Tim; Daccache, Andre; Daneshkhah, Alireza; Knox, Jerry W.Evapotranspiration (ET) is one of the most important components in the hydrological cycle, and a key variable in hydrological modelling and water resources management. However, understanding the impacts of spatial variability in ET and the appropriate scale at which ET data should be incorporated into hydrological models, particularly at the regional scale, is often overlooked. This is in contrast to dealing with the spatial variability in rainfall data where existing guidance is widely available. This paper assesses the impacts of scale on the estimation of reference ET (ETo) by comparing data from individual weather stations against values derived from three national datasets, at varying resolutions. These include the UK Climate Impacts Programme 50 km climatology (UKCP50), the UK Met Office 5 km climatology (UKMO5) and the regional values published in the Agricultural Climate of England and Wales (ACEW). The national datasets were compared against the individual weather station data and the UKMO5was shown to provide the best estimate of ETo at a given site. The potential impacts on catchment modelling were then considered by mapping variance in ETo to show how geographical location and catchment size can have a major impact, with small lowland catchments having much higher variance than those with much larger areas or in the uplands. Some important implications for catchment hydrological modelling are highlighted.Item Open Access Using machine learning algorithms to develop a clinical decision-making tool for COVID-19 inpatients(MDPI, 2021-06-09) Vepa, Abhinav; Saleem, Amer; Rakhshan, Kambiz; Daneshkhah, Alireza; Sedighi, Tabassom; Shohaimi, Shamarina; Omar, Amr; Salari, Nader; Chatrabgoun, Omid; Dharmaraj, Diana; Sami, Junaid; Parekh, Shital; Ibrahim, Mohamed; Raza, Mohammed; Kapila, Poonam; Chakrabarti, PrithwirajBackground: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.