Probabilistic Projections Research Articles

Probabilistic Construction Project Duration Prediction Models for High Rise Building Based on Earned Schedule Method in Jakarta

Probabilistic Construction Project Duration Prediction Models for High Rise Building Based on Earned Schedule Method in Jakarta

Energetic productivity dynamics of global super-giant oilfields

Energy return on investment (EROI) dynamics probabilistic projections of global giant oil fields.

Household projections by the headship rates method: The case of Serbia

Household projections by the headship rates method: The case of Serbia

Patterns of Fertility Decline and the Impact of Alternative Scenarios of Future Fertility Change in sub‐Saharan Africa

Our aim in this chapter is to provide an updated and concise description of the diversity of fertility decline patterns among countries1 in sub- Saharan Africa drawing on the latest series of fertility estimates that take into account many different data sources and that are harmonized with other demographic components (United Nations 2015d). We focus on the level of fertility prior to the start of fertility decline the time period of the fertility transition and the estimated pace of decline. We also explore the implications of different fertility decline patterns for future fertility and population projections in the region. We draw on the distinct patterns of fertility decline among countries worldwide that are advanced in (or have completed) their first fertility transition to construct probabilistic fertility and population projections for sub-Saharan African countries. The illustrative comparisons of projections highlight the demographic impact if future fertility decline in sub-Saharan countries were to accelerate and follow the rapid pace of decline already experienced by a diverse group of countries. (excerpt)

Meeting the Sustainable Development Goals leads to lower world population growth

Here we show the extent to which the expected world population growth could be lowered by successfully implementing the recently agreed-upon Sustainable Development Goals (SDGs). The SDGs include specific quantitative targets on mortality, reproductive health, and education for all girls by 2030, measures that will directly and indirectly affect future demographic trends. Based on a multidimensional model of population dynamics that stratifies national populations by age, sex, and level of education with educational fertility and mortality differentials, we translate these goals into SDG population scenarios, resulting in population sizes between 8.2 and 8.7 billion in 2100. Because these results lie outside the 95% prediction range given by the 2015 United Nations probabilistic population projections, we complement the study with sensitivity analyses of these projections that suggest that those prediction intervals are too narrow because of uncertainty in baseline data, conservative assumptions on correlations, and the possibility of new policies influencing these trends. Although the analysis presented here rests on several assumptions about the implementation of the SDGs and the persistence of educational, fertility, and mortality differentials, it quantitatively illustrates the view that demography is not destiny and that policies can make a decisive difference. In particular, advances in female education and reproductive health can contribute greatly to reducing world population growth.

Extreme Precipitation Indices over China in CMIP5 Models. Part II: Probabilistic Projection

Abstract The present article is the second part of a study on the extreme precipitation indices over China in CMIP5 models that perform a probabilistic projection of future precipitation indices with reference to the period 1986–2005. This is realized with a rank-based weighting method. The ranking of the 25 models is done according to their performance in simulating rainfall indices in present-day climate. Such weights are used to form a weighted ensemble for future climate projection. Results show that, compared to the unweighted raw ensemble, the projection with the weighted scheme is more credible, as the signal-to-noise ratio (SNR) of indices is larger from the weighted ensemble. From the beginning of the mid-twenty-first century, changes of wet indices with probability >0.5 increase significantly, especially over western China and the Yellow–Huai River basin, where the changes of all wet indices are in excess of 10%, the increase of total precipitation (PRCPTOT) can reach up to 20% over western China at the end of twenty-first century, and the SNR of PRCPTOT and precipitation intensity (SDII) is the highest at those two regions. This indicates that the precipitation in those regions has a high reliability to become more extreme. The maximum consecutive dry days (CDD) decreases throughout the north of 30°N, which shows that drought conditions in northern China would be reduced, and they are more likely to increase in southern China. However, the SNR for projection of CDD is less than 1.0 almost everywhere. Such a situation seems related to a strengthening of the East Asian summer monsoon and the associated northward shift of the monsoon front.

Expert elicitation of autocorrelated time series with application to e3 (energy-environment-economic) forecasting models

Explicitly representing uncertainty is recognised as a fundamental requirement of any long-term forecast. We propose and illustrate an expert elicitation protocol for constructing long-term probabilistic projections. Each projection represents a possible realization of a time series with autocorrelation properties, and thus a plausible future evolution of a quantity of interest. We illustrate the approach using two quantities – GDP growth rates and coal prices – that were elicited as part of a project producing baseline forecasts of greenhouse gas emissions in South Africa to 2050. The elicited projections can be used as inputs to deterministic structural models of the energy, economic, and environmental sectors (e3 or energy-environment-economic models), to generate similar probabilistic projections for any desired outputs of the e3 model. An R package for the generation and visualization of these probabilistic projections is provided.

Probabilistic projections of regional climatic changes over the Great Lakes Basin

As the largest surface fresh water system on earth, the Great Lakes is facing the threat of climate change. Understanding how the hydrologic cycle in the Great Lakes region would be affected by human-induced global warming is important for developing informed adaptation strategies. In this study, high-resolution regional climate ensemble simulations based upon the PRECIS modeling system are conducted to project future climatic changes over the Great Lakes Basin. The results show that the Great Lakes Basin is very likely to experience a continuous warming-up throughout the 21st century. Particularly, mean air temperatures will rise by 2.6 °C in the forthcoming decades (i.e., 2030s), 3.8 °C in the middle of the century (i.e., 2050s), and 5.6 °C to the end of the century (i.e., 2080s), respectively. The warming air temperatures are very likely to result in more precipitation over the entire basin. The annual total precipitation over the Great Lakes Basin is projected to increase by 8.9% in the 2030s and 12.2% in the 2050s, while the magnitude of precipitation increase would decline to 7.1% in the 2080s. The slow-down of the precipitation increase from the 2050s to the 2080s indicates a shift from the aggressive increase of precipitation before and in the middle of this century to the eventual decrease by the end of this century, suggesting that a nonlinear response relationship between precipitation and temperature may exist in the Great Lakes Basin and such a relationship is also likely to vary in response to global warming.

Probabilistic Approach for Long-Run Price Projections: Case Study of Concrete and Asphalt

AbstractPractitioners increasingly use pavement management systems for determining the allocation of resources for multidecade investments. One important and uncertain input that will affect decisions in these frameworks is future changes in cost for rehabilitation and reconstruction actions. Despite this, existing paradigms overlook its consideration largely because little research to date has evaluated the performance of forecasting over extended time horizons. Therefore, the contribution of this study is the demonstration (via a case study) of the long-term fidelity of probabilistic price projections relative to current practice. Two paving materials, asphalt and concrete, are projected through a probabilistic hybrid forecasting model that convolves conventional forecasts for underlying constituent prices and a long-term price equilibrium relationship between commodities. Out-of-sample forecasts are conducted to test the performance of the proposed model in estimating future prices in terms of their (1...

A simple method for Bayesian model averaging of regional climate model projections: Application to southeast Australian temperatures

AbstractRecent studies using regional climate models to make probabilistic projections break important new ground. However, they typically lack cross validation, pull the projections toward agreeing models (which can agree due to shared biases), and ignore model skill at reproducing internal variability when weighing the models. Here we conduct the first, to our knowledge, application of Bayesian model averaging (BMA) to make probabilistic projections using regional climate models (RCMs). We weigh the RCMs from the NARCliM project based on their skill at representing temperature over 12 southeast Australian regions in terms of trend, bias, and internal variability. The weights do not depend on model agreement with other models. Using the weighted ensemble, we provide probabilistic seasonal temperature projections. We cross validate the method, and demonstrate that weighted projections are well calibrated and more precise than the unweighted ones. We find considerable differences between the weighted and the unweighted projections in some cases.

Allowances for evolving coastal flood risk under uncertain local sea-level rise

Sea-level rise (SLR) causes estimates of flood risk made under the assumption of stationary mean sea level to be biased low. However, adjustments to flood return levels made assuming fixed increases of sea level are also inaccurate when applied to sea level that is rising over time at an uncertain rate. To accommodate both the temporal dynamics of SLR and their uncertainty, we develop an Average Annual Design Life Level (AADLL) metric and associated Design Life SLR (DL-SLR) allowances. The AADLL is the flood level corresponding to a time-integrated annual expected probability of occurrence (AEP) under uncertainty over the design life of an asset; DL-SLR allowances are the adjustment from 2000 levels that maintain current average probability over the design life. Given non-stationary and uncertain sea-level rise, AADLL flood levels and DL-SLR allowances provide estimates of flood protection heights and offsets for different planning horizons and different levels of confidence in SLR projections in coastal areas. Here we employ probabilistic sea-level rise projections to illustrate the calculation of AADLL flood levels and DL-SLR allowances for a set of long-duration tide gauges along U.S. coastlines.

고위험 사업에서 PERT와 PLET기법 결합에 의한 확률적 사업공기 추정

Abstract : PERT(Project Evaluation and Review Technique) is typical method in order to probabilistically establish the schedule plan for large scale complex project or mega project. PERT technique is impossible to estimate relationship of each activity probabilistically when there are overlapping relationships because it is limited to Finish-to-Start(FS) relationship. In order to overcome this kind of limitation of PERT technique, PLET(Probabilistic Linkage Evaluation Technique) has been suggested to probabilistically estimate project duration for various overlapping relationships for each activity. However, estimating project duration by PLET technique only considers uncertainty of relationship between activities and not activity time, thus it is not the perfect duration estimating method. The main objective of this research is to propose the integration model of PERT and PLET and verify its probabilistic validity. By verifying application of time calculation method of integration model in practical case, this research will suggest probabilistic project duration estimating method in schedule plan of high-risk construction industry.

Probabilistic population projections with migration uncertainty

We produce probabilistic projections of population for all countries based on probabilistic projections of fertility, mortality, and migration. We compare our projections to those from the United Nations' Probabilistic Population Projections, which uses similar methods for fertility and mortality but deterministic migration projections. We find that uncertainty in migration projection is a substantial contributor to uncertainty in population projections for many countries. Prediction intervals for the populations of Northern America and Europe are over 70% wider, whereas prediction intervals for the populations of Africa, Asia, and the world as a whole are nearly unchanged. Out-of-sample validation shows that the model is reasonably well calibrated.

Bayesian estimation of climate sensitivity using observationally constrained simple climate models

One‐dimensional simple climate models (SCMs) play an important role within a hierarchy of climate models. They have largely been used to investigate alternative emission scenarios and estimate global‐mean temperature change. This role has expanded through the incorporation of techniques that include Monte Carlo methods and Bayesian statistics, adding the ability to generate probabilistic temperature change projections and diagnose key uncertainties, including equilibrium climate sensitivity (ECS). The latter is the most influential parameter within this class of models where it is ordinarily prescribed, rather than being an emergent property. A series of recent papers based on SCMs and Bayesian statistical methods have endeavored to estimate ECS by using instrumental observations and results from other more complex models to constrain the parameter space. Distributions for ECS depend on a variety of parameters, such as ocean diffusivity and aerosol forcing, so that conclusions cannot be drawn without reference to the joint parameter distribution. Results are affected by the treatment of natural variability, observational uncertainty, and the parameter space being explored. In addition, the highly simplified nature of SCMs means that they contain a number of implicit assumptions that do not necessarily reflect adequately the true nature of Earth's nonlinear quasi‐chaotic climate system. Differences in the best estimate and range for ECS may be partly due to variations in the structure of the SCMs reviewed in this study, along with the selection of data and the calibration details, including the choice of priors. Further investigations and model intercomparisons are needed to clarify these issues. WIREs Clim Change 2016, 7:461–473. doi: 10.1002/wcc.397This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models

Dynamically-downscaled probabilistic projections of precipitation changes: A Canadian case study

In this study, plausible changes in annual and seasonal precipitation over Ontario, Canada in response to global warming are investigated through a regional climate modeling approach. A high-resolution regional climate model ensemble based upon the Providing REgional Climates for Impacts Studies (PRECIS) model is developed to help explore the possible outcomes of future climate. A Bayesian hierarchical model is then employed to quantify the uncertainties involved in the modeling results and obtain probabilistic projections of precipitation changes at grid point scales. The results show that the projected changes in annual precipitation exhibit a certain degree of spatial variability with the median changes mostly bounded by 0% and 20%, implying that the annual precipitation over Ontario is more likely to increase in the context of global warming. Specifically, the mean changes in annual precipitation for 2030s and 2050s would be ~7.5%, while the annual precipitation for 2080s is likely to increase by an average of ~12.5%. By contrast, the spatial variability of seasonal precipitation changes is more significant, especially for the changes in spring precipitation which may vary from −40% in south and 50% in north. It is reported that there would be a continuous increasing trend in winter, spring, and autumn precipitation from 2030s to 2080s by 5–30%, but summer precipitation is likely to decrease by 5% or even higher to the end of this century. Furthermore, our results suggest that the larger the biases in historical simulations, the more uncertain the future projections will be.

A probabilistic projection of the transient flow equations with random system parameters and internal boundary conditions

ABSTRACTThis paper presents a novel probabilistic approach based on the polynomial chaos expansion that can model the uncertainty propagation from the beginning of a waterhammer simulation and not as an afterthought. Uncertainties are considered in pipe diameter, friction coefficient, and wave speed, as well as internal boundary conditions of leaks and blockages. The polynomial chaos expansion solver results are in an excellent agreement with those calculated by using a model employing the traditional method of characteristics. The probabilistic polynomial chaos approach has the advantage of being robust and more efficient than other non-intrusive methods such as Monte Carlo simulation, which requires thousands of iterations for sharp solutions. The polynomial chaos approach is further extended to solve for randomness in frequency domain using the transfer matrix method with results of comparable accuracy. With further developments, this probabilistic approach can be integrated within existing network modelling software for practical hydraulic engineering problems.

Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood

A model that evaluates the likelihood of coastal inundation (flooding) and dynamical response (adaptation) as sea levels rise shows that, for the northeastern US, about 70% of the coast has some capacity to respond dynamically and alter inundation risk. Sea-level rise (SLR) poses a range of threats to natural and built environments1,2, making assessments of SLR-induced hazards essential for informed decision making3. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30 × 30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.

Does Size Matter? A Study of Risk Perceptions of Global Population Growth.

The global human population now exceeds 7 billion and is projected to reach 10 billion around 2060. While population growth has been associated with certain benefits (e.g., economies of scale, technological advancements), theoretical models, probabilistic projections, and empirical evidence also indicate that this growth could increase the likelihood of many adverse events (e.g., climate change, resource shortages) and the impact of these events, as more people are exposed to the outcomes. While concerns about these issues are well-documented in the academic literature, there is little evidence concerning the public's perceptions of the risks associated with global population growth (GPG) and how these perceptions are likely to influence related decisions. To address these issues, we conducted a U.K.-based study that examined respondents' risk perceptions of GPG, their willingness to embrace mitigation/precautionary behaviors, and reasons for variations in these two factors. We found that GPG is perceived as a moderate-to-high risk, with concerns about the increased likelihood of resource shortages, ecological damage, and violent conflict being foremost. Respondents believed that the worst effects of GPG would arrive around 2050 and would be experienced by the world's poorest people. Respondents who perceived greater levels of risk from GPG were generally those who indicated a greater willingness to embrace mitigation behaviors (e.g., reduce resource consumption) and preventative actions (e.g., support political action to limit growth). We discuss how our findings might be utilized to better manage the potential challenges associated with GPG and we suggest several directions for further research.

Design Live-Load Factor Calibration for Michigan Highway Bridges

AbstractIn this study, a reliability-based calibration of live-load factors for bridge design specific to the state of Michigan was conducted. Two years of high-frequency weigh-in-motion (WIM) data from 20 representative statewide sites were analyzed, and load effects were generated for bridge spans from 6 to 122 m (20 to 400 ft), considering simple and continuous moments and shears, and single-lane and two-lane effects. Seventy-five-year statistics for maximum live load were then estimated with probabilistic projection. Bridge girders considered for the calibration included composite steel, prestressed concrete, side-by-side and spread box beams, and special long-span structural members. In some cases, it was found that Michigan load effects were greater than those previously assumed, often requiring higher load factors than those in current use. Moreover, significant variation in the required load factor was found, potentially resulting in significant inconsistencies in reliability if a single load fact...

BayesPop: Probabilistic Population Projections.

We describe bayesPop, an R package for producing probabilistic population projections for all countries. This uses probabilistic projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sample from the joint posterior predictive distribution of future age- and sex-specific population counts, fertility rates and mortality rates, as well as future numbers of births and deaths. It provides graphical ways of summarizing this information, including trajectory plots and various kinds of probabilistic population pyramids. An expression language is introduced which allows the user to produce the predictive distribution of a wide variety of derived population quantities, such as the median age or the old age dependency ratio. The package produces aggregated projections for sets of countries, such as UN regions or trading blocs. The methodology has been used by the United Nations to produce their most recent official population projections for all countries, published in the World Population Prospects.