Accuracy Of Future Predictions Research Articles

Seasonal dynamics of global marine heatwaves over the last four decades

Marine heatwaves (MHWs), prolonged periods of abnormally high sea temperature, have greater devastating impacts on marine ecosystem services and socioeconomic systems than gradual long-term ocean warming. Despite growing evidence of increases in MHW frequency, duration, and intensity, their interseasonal variations remain unclear. Using satellite-derived daily sea surface temperature (SST) data from 1982 to 2022, this work reveals a strong seasonality in MHWs. Typically, the highest cumulative intensity, characterizing total impacts on ecosystems, occurs during the local warm seasons in most oceans, leading to a significant interseasonal difference between warm and cold seasons. The interseasonal difference is predominantly driven by air-sea heat flux, rather than oceanic horizontal advection and vertical process. An increase in these interseasonal differences is observed in mid and high latitudes, with a significant increase in the warm season and a weaker trend in the cold season. In the Equatorial Pacific and Western Equatorial Indian Ocean, intense MHWs are primarily exacerbated by the El Niño-Southern Oscillation (ENSO), which also determines interseasonal variations in MHWs. Understanding the seasonality of MHWs can help better formulate corresponding policies to reduce economic and ecological losses caused by these events and can improve the accuracy of future predictions.

Big data in Earth science: Emerging practice and promise.

Improvements in the number and resolution of Earth- and satellite-based sensors coupled with finer-resolution models have resulted in an explosion in the volume of Earth science data. This data-rich environment is changing the practice of Earth science, extending it beyond discovery and applied science to new realms. This Review highlights recent big data applications in three subdisciplines-hydrology, oceanography, and atmospheric science. We illustrate how big data relate to contemporary challenges in science: replicability and reproducibility and the transition from raw data to information products. Big data provide unprecedented opportunities to enhance our understanding of Earth's complex patterns and interactions. The emergence of digital twins enables us to learn from the past, understand the current state, and improve the accuracy of future predictions.

Retrieval of Surface Energy Fluxes Considering Vegetation Changes and Aerosol Effects

The exchange of moisture and energy between the land and the atmosphere plays a crucial role in terrestrial hydrological cycle and climate change. However, existing studies on the retrieval of surface water and heat flux tend to overlook the dynamic changes in surface vegetation and atmospheric aerosols, which directly affect surface energy and indirectly alter various meteorological factors, including cloud, precipitation, and temperature. In this study, we assess the machine-learning retrieval method for surface fluxes that takes into account vegetation changes and aerosol effects, using FLUXNET observations and remote sensing data to retrieve latent heat flux (LE) and sensible heat flux (H). We constructed four sets of deep neural network models: (a) The first set considers only meteorological factors, (b) the second set considers meteorological factors and aerosols, (c) the third set considers meteorological factors and vegetation changes, and (d) the fourth set comprehensively considers meteorological factors, aerosols, and vegetation changes. All model performances were evaluated using statistical indicators. ERA5 reanalysis and remote sensing data were used to drive the models and retrieve daily H and LE. The retrieved results were validated against ground observation sites that were not involved in model training or the FLUXCOM product. The results show that the model that considers meteorological factors, aerosols, and vegetation changes has the smallest errors and highest correlation for retrieving H and LE (RH = 0.85, RMSEH = 24.88; RLE = 0.88, RMSELE = 22.25). The ability of the four models varies under different vegetation types. In terms of seasons, the models that consider meteorological factors and vegetation changes, as well as those that comprehensively consider meteorological factors, aerosols, and vegetation changes, perform well in retrieving the surface fluxes. As for spatial distribution, when atmospheric aerosols are present in the region, the model that considers both meteorological factors and aerosols retrieves higher values of H compared to the model that considers only meteorological factors, while the LE values are relatively lower. The model that considers meteorological factors and vegetation changes, as well as the model that comprehensively considers meteorological factors, aerosols, and vegetation changes, retrieves lower values in most regions. Through the validation of independent observation sites and FLUXCOM products, we found that the model, considering meteorological factors, aerosols, and vegetation changes, was generally more accurate in the retrieval of surface fluxes. This study contributes to improving the retrieval and future prediction accuracy of surface fluxes in a changing environment.

Rethinking tourism: where is it going and how will it impact people and planet? A personal perspective

PurposeThis paper aims to discuss the complexity of predicting the future and of making tourism futuristic predictions. It argues that tourism cannot be separated from the world context in which it operates and explores the various impacts of tourism on people and planet both in a near and more distant future.Design/methodology/approachThis paper revises several reports and research by world economic and tourism authorities as they provide arguments for the study of trends and the complexity of the evolution of the travel and tourism industry.FindingsThere is no certain way to know what the future will bring to the tourism industry or how it will interact with society and the physical environment. What the author does know is that from the beginning of time, events do not occur in isolation and that travel and tourism will impact everything that it touches. It is impossible to make exact predictions as to what tourism will be like in the near and distant future, what the author does know is that humans will want to travel, to explore and to learn, and this desire will interact not only with tourism but with the course of history.Originality/valueBy exploring the evolution of tourism in the context of science, the author cannot even be certain where tourism will occur, if only on the same planet, within the other planets of the solar system or in the vastness of space. Science fiction predicts the latter, but only the unfolding of history will teach of the accuracy of future predictions and how the future of travel will be.

Quantifying the contribution of temperature, salinity, and climate change to sea level rise in the Pacific Ocean: 2005-2019

In recent decades, Pacific Ocean’s steric sea level anomaly (SSLA) has shown prominent patterns among global sea level variations. With ongoing global warming, the frequency and intensity of climate and sea level changes have increased, particularly in the tropical Pacific region. Therefore, it is crucial to comprehend the overall trends and mechanisms governing volumetric sea level changes in the Pacific. To accurately quantify the spatiotemporal evolution characteristics of density-driven sea level change in the Pacific Ocean (PO) from 2005 to 2019, we decomposed temperature and salinity into linear trends, interannual variations, seasonal variations, and residual terms using the STL (seasonal-trend decomposition based on loess) method. To evaluate the influence of ocean temperature, salinity, and climate change on density-driven sea level change and its underlying mechanisms, we decompose temperature as well as salinity changes through into the Heaving (vertical displacements of isopycnal surfaces) and Spicing (density-compensated temperature and salinity change) modes. The findings reveal an average steric sea level rise rate of 0.34 ± 0.16 mm/yr in the PO from 2005 to 2019. Thermosteric sea-level accounts for 82% of this rise, primarily due to seawater temperature rise at depths of 0-700 m caused by Heaving mode changes. Accelerated SSLA increase via the thermosteric effect has been connected to interactions between greater Ekman downwelling from surface winds, radiation forcing linked to global greenhouse gases, and changes in the Pacific warm currents triggered by El Niño-Southern Oscillation (ENSO) episodes. Although salinity is affected by the Subantarctic Mode Water (SAMW) and the Antarctic Intermediate Water (AAIW) in the southern Indian Ocean, however the significance of salinity in sea level change is little compared to the role played by thermocline shift. This study offers a substantial contribution to the field, providing robust data and technical support, and facilitating a deeper understanding of the mechanisms underlying the effects of temperature and salinity on sea level changes during periods of rapid climate change, thus enhancing the accuracy of future predictions regarding sea level rise.

Calibration interval scenario approach in spatial modeling of land cover change in East Kalimantan from 2016 to 2036

Spatial modeling can be used to predict future land cover changes based on past and present conditions. However, it is not yet known to what extent this model can be used to predict the future with reliable accuracy. Therefore, by using multi-temporal land cover data, this study aims to build an optimal model based on the calibration interval scenario. The optimal model is then used to predict and analyze changes in land cover in East Kalimantan in 2016–2036. 11 classified multi-temporal land cover maps from the Landsat Time Series using Random Forest in Google Earth Engine are used to model 14 calibration interval scenarios. A land Change Modeler is used to model and predict land cover change with 14 driving variables. The results of the classification of multi-temporal land cover maps show a good level of accuracy, with an Overall Accuracy value of 71.43–85.14% and a Kappa value of 0.667–0.827. Then 2016–2021 is one of the best scenarios with 5-year intervals where the accuracy of future predictions can still be relied upon for up to three prediction iterations. The calibration interval scenario approach in spatial modeling in East Kalimantan can be relied upon to show a decrease in forest cover from 2016 to 2021, with a deforestation rate of 651 km2/year. The prediction of land cover in 2036 estimates that the remaining forest cover area in East Kalimantan is 69.203 km2. It is believed that topography is the most influential variable driving land cover change in East Kalimantan.

Association between the number of visual fields and the accuracy of future prediction in eyes with retinitis pigmentosa

PurposeTo evaluate the minimum number of visual fields (VFs) required to precisely predict future VFs in eyes with retinitis pigmentosa (RP).MethodsA series of 12 VFs (Humphrey Field Analyzer 10–2 test...

Climatic variables and ecological modelling data for birds, amphibians and reptiles in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain).

BackgroundClimate change has been widely accepted as one of the major threats for global biodiversity and understanding its potential effects on species distribution is crucial to optimise conservation planning in future scenarios under global change. Integrating detailed climatic data across spatial and temporal scales into species distribution modelling can help to predict potential changes in biodiversity. Consequently, this type of data can be useful for developing efficient biodiversity management and conservation planning. The provision of such data becomes even more important in highly biodiverse regions, currently suffering from climatic and landscape changes. The Transboundary Biosphere Reserve of Meseta Ibérica (BRMI; Portugal-Spain) is one of the most relevant reserves for wildlife in Europe. This highly diverse region is of great ecological and socio-economical interest, suffering from synergistic processes of rural land abandonment and climatic instabilities that currently threaten local biodiversity.Aiming to optimise conservation planning in the Reserve, we provide a complete dataset of historical and future climate models (1 x 1 km) for the BRMI, used to build a series of distribution models for 207 vertebrate species. These models are projected for 2050 under two climate change scenarios. The climatic suitability of 52% and 57% of the species are predicted to decrease under the intermediate and extreme climatic scenarios, respectively. These models constitute framework data for improving local conservation planning in the Reserve, which should be further supported by implementing climate and land-use change factors to increase the accuracy of future predictions of species distributions in the study area.New informationHerein, we provide a complete dataset of state-of-the-art historical and future climate model simulations, generated by global-regional climate model chains, with climatic variables resolved at a high spatial resolution (1 × 1 km) over the Transboundary Biosphere Reserve of Meseta Ibérica. Additionally, a complete series of distribution models for 207 species (168 birds, 24 reptiles and 15 amphibians) under future (2050) climate change scenarios is delivered, which constitute framework data for improving local conservation planning in the reserve.

Non-Adherence Tree Analysis (NATA)-An adherence improvement framework: A COVID-19 case study.

Poor medication adherence is a global phenomenon that has received a significant amount of research attention yet remains largely unsolved. Medication non-adherence can blur drug efficacy results in clinical trials, lead to substantial financial losses, increase the risk of relapse and hospitalisation, or lead to death. The most common methods of measuring adherence are post-treatment measures; that is, adherence is usually measured after the treatment has begun. What the authors are proposing in this multidisciplinary study is a new technique for predicting the factors that are likely to cause non-adherence before or during medication treatment, illustrated in the context of potential non-adherence to COVID-19 antiviral medication. Fault Tree Analysis (FTA), allows system analysts to determine how combinations of simple faults of a system can propagate to cause a total system failure. Monte Carlo simulation is a mathematical algorithm that depends heavily on repeated random sampling to predict the behaviour of a system. In this study, the authors propose a new technique called Non-Adherence Tree Analysis (NATA), based on the FTA and Monte Carlo simulation techniques, to improve adherence. Firstly, the non-adherence factors of a medication treatment lifecycle are translated into what is referred to as a Non-Adherence Tree (NAT). Secondly, the NAT is coded into a format that is translated into the GoldSim software for performing dynamic system modelling and analysis using Monte Carlo. Finally, the GoldSim model is simulated and analysed to predict the behaviour of the NAT. NATA is dynamic and able to learn from emerging datasets to improve the accuracy of future predictions. It produces a framework for improving adherence by analysing social and non-social adherence barriers. Novel terminologies and mathematical expressions have been developed and applied to real-world scenarios. The results of the application of NATA using data from six previous studies in relation to antiviral medication demonstrate a predictive model which suggests that the biggest factor that could contribute to non-adherence to a COVID-19 antiviral treatment is a therapy-related factor (the side effects of the medication). This is closely followed by a condition-related factor (asymptomatic nature of the disease) then patient-related factors (forgetfulness and other causes). From the results, it appears that side effects, asymptomatic factors and forgetfulness contribute 32.44%, 22.67% and 18.22% respectively to discontinuation of medication treatment of COVID-19 antiviral medication treatment. With this information, clinicians can implement relevant interventions and measures and allocate resources appropriately to minimise non-adherence.

Forecasting environmental factors and zooplankton of Bakreswar reservoir in India using time series model

Time-series models have vast advantages in the study of dynamic systems, especially if the aims are to determine structure and stability of population or finding regime shifts in dynamic characters of ecological systems. These models can also be used with precise goals for extracting specific demographic functions and their impacts. Auto Regressive Integrated Moving-Average or ARIMA models are one of the most general class of time-series forecasting models. In the present study, eight different environmental factors were chosen as the target groups for studying time series variations, that ranged from physico-chemical (e.g. air and water temperature, humidity, etc.) to biological (zooplankton) factors. Most of the ARIMA models were able to capture the trends of variation in the observed data. However, some linear trends were also observed in few of the forecasted series (increasing for some and decreasing for others). To improve on these forecasts, a hybrid ARIMA-ANN method was utilized which successfully increased the accuracy of future predictions showing seasonal variations in the forecasted values.

Multi-institutional Clinical Tool for Predicting High-risk Lesions on 3 Tesla Multiparametric Prostate Magnetic Resonance Imaging

BackgroundMultiparametric magnetic resonance imaging (mpMRI) for prostate cancer detection without careful patient selection may lead to excessive resource utilization and costs. ObjectiveTo develop and validate a clinical tool for predicting the presence of high-risk lesions on mpMRI. Design, setting, and participantsFour tertiary care centers were included in this retrospective and prospective study (BiRCH Study Collaborative). Statistical models were generated using 1269 biopsy-naive, prior negative biopsy, and active surveillance patients who underwent mpMRI. Using age, prostate-specific antigen, and prostate volume, a support vector machine model was developed for predicting the probability of harboring Prostate Imaging Reporting and Data System 4 or 5 lesions. The accuracy of future predictions was then prospectively assessed in 214 consecutive patients. Outcome measurements and statistical analysisReceiver operating characteristic, calibration, and decision curves were generated to assess model performance. Results and limitationsFor biopsy-naïve and prior negative biopsy patients (n=811), the area under the curve (AUC) was 0.730 on internal validation. Excellent calibration and high net clinical benefit were observed. On prospective external validation at two separate institutions (n=88 and n=126), the machine learning model discriminated with AUCs of 0.740 and 0.744, respectively. The final model was developed on the Microsoft Azure Machine Learning platform (birch.azurewebsites.net). This model requires a prostate volume measurement as input. ConclusionsIn patients who are naïve to biopsy or those with a prior negative biopsy, BiRCH models can be used to select patients for mpMRI. Patient summaryIn this multicenter study, we developed and prospectively validated a calculator that can be used to predict prostate magnetic resonance imaging (MRI) results using patient age, prostate-specific antigen, and prostate volume as input. This tool can aid health care professionals and patients to make an informed decision regarding whether to get an MRI.

A model comparison algorithm for increased forecast accuracy of dengue fever incidence in Singapore and the auxiliary role of total precipitation information

ABSTRACTMany time-series models for disease counts utilise information from environmental variables. We focus on weekly dengue fever (DF) incidence rates in Singapore and demonstrate the strong negative correlation between an appropriately time-lagged total weekly rainfall and DF incidence. A Bayesian neural network time-series model for predicting DF incidence which utilizes rainfall data is introduced. A comparison is made between this neural network model and a time-series model which does not use any covariate information. An easily implementable method for choosing between the models which optimizes future prediction accuracy is suggested as well. We note that our proposed comparison method is applicable to any competing time-series models. This algorithm is demonstrated through examples of comparisons between pairs of different time-series models.

Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction

Automated driving requires decision making in dynamic and uncertain environments. The uncertainty from the prediction originates from the noisy sensor data and from the fact that the intention of human drivers cannot be directly measured. This problem is formulated as a partially observable Markov decision process (POMDP) with the intended route of the other vehicles as hidden variables. The solution of the POMDP is a policy determining the optimal acceleration of the ego vehicle along a preplanned path. Therefore, the policy is optimized for the most likely future scenarios resulting from an interactive, probabilistic motion model for the other vehicles. Considering possible future measurements of the surrounding cars allows the autonomous car to incorporate the estimated change in future prediction accuracy in the optimal policy. A compact representation results in a low-dimensional state-space. Thus, the problem can be solved online for varying road layouts and number of vehicles. This is done with a point-based solver in an anytime fashion on a continuous state-space. Our evaluation is threefold: At first, the convergence of the algorithm is evaluated and it is shown how the convergence can be improved with an additional search heuristic. Second, we show various planning scenarios to demonstrate how the introduction of different considered uncertainties results in more conservative planning. At the end, we show online simulations for the crossing of complex (unsignalized) intersections. We can demonstrate that our approach performs nearly as good as with full prior information about the intentions of the other vehicles and clearly outperforms reactive approaches.

Accurate and precise prediction of insulin sensitivity variance in critically ill patients

Abstract Background Glycaemic control (GC) in critical care can reduce mortality and improve clinical outcomes. Model based GC allows personalised and effective predictive control. Improving accuracy and precision of patient specific models, parameter identification, and resulting blood glucose predictions would enable more effective model-based GC algorithms. Methods Glycaemic data from 30 critically ill patient episodes was used to fit a model of glucose dynamics. In this model, insulin sensitivity (SI) is identified through time using b-spline basis functions. The number of basis functions, M, relative to the number of data points in each data set, N, determines the level of parameterisation of the model. By fitting the model to all 30 data sets, the identified SI profiles could be used to build stochastic maps of SI changes over time. These maps show how SI is expected to change based on the current SI and the overall behaviour of this cohort. Thus, for a given time step into the future, SI prediction distributions can be found. Stochastic maps were made for varying basis function degree (d) (d ∊ {0, 1, 2}), and ratio of M to N (M:N ∊ {0.3, 0.65, 0.85, 1}). Using small subsets of each data set, many trials were carried out to compare SI prediction distributions to the distribution of the true SI values at the next measurement, for each combination of M and d. The main aim was to observe how the model parameterisation affected the predictive ability of the model. Outcomes from an Akaike Information Criterion (AIC) analysis were compared to the prediction analysis. AIC is a method of model comparison that assesses which of the given models provides the best trade-off between goodness-of-fit and model complexity, based on the expected measurement noise. For each basis function order, an AIC analysis was used to select the best M:N ratio of the four options tested. Results Increasing the parameterisation of the model resulted in lower model-data residuals and thus wider stochastic maps, as SI changed at a faster rate to enable the model to more closely fit to the data. Therefore, increasing M:N resulted in wider prediction distributions. In all cases, when M:N = 1, the prediction distributions were too wide, and when M:N = 0.3 the prediction distributions were too narrow. Increasing the basis function order resulted in tighter prediction distributions, and allowed more accurate predictions to be made with a higher M:N ratio. The ratios that gave the most accurate predictions were M:N = 0.65 when d = 0, M:N = 0.85 when d = 1, and M:N = 1 when d = 2. In contrast, the AIC analysis found that an M:N ratio of approximately 0.45 was optimal in all cases. Conclusions This study presented a new strategy for observing how the level of parameterisation and basis function order affects accuracy of future predictions of SI variability. Applications based on the findings of this research with a larger cohort could lead to improved predictive capability in GC algorithms. Accurate predictions would ultimately allow model-based GC algorithms to be implemented more effectively, increasing clinician confidence and improving patient outcomes.

Genetic Epidemiology of Glucose-6-Phosphate Dehydrogenase Deficiency in the Arab World.

A systematic search was implemented using four literature databases (PubMed, Embase, Science Direct and Web of Science) to capture all the causative mutations of Glucose-6-phosphate dehydrogenase (G6PD) deficiency (G6PDD) in the 22 Arab countries. Our search yielded 43 studies that captured 33 mutations (23 missense, one silent, two deletions, and seven intronic mutations), in 3,430 Arab patients with G6PDD. The 23 missense mutations were then subjected to phenotypic classification using in silico prediction tools, which were compared to the WHO pathogenicity scale as a reference. These in silico tools were tested for their predicting efficiency using rigorous statistical analyses. Of the 23 missense mutations, p.S188F, p.I48T, p.N126D, and p.V68M, were identified as the most common mutations among Arab populations, but were not unique to the Arab world, interestingly, our search strategy found four other mutations (p.N135T, p.S179N, p.R246L, and p.Q307P) that are unique to Arabs. These mutations were exposed to structural analysis and molecular dynamics simulation analysis (MDSA), which predicting these mutant forms as potentially affect the enzyme function. The combination of the MDSA, structural analysis, and in silico predictions and statistical tools we used will provide a platform for future prediction accuracy for the pathogenicity of genetic mutations.

3D-QSAR and SVM Prediction of BRAF-V600E and HIV Integrase Inhibitors: A Comparative Study and Characterization of Performance with a New Expected Prediction Performance Metric

The results of directly comparing the prediction accuracy of optimized 3D Quantitative Structure-Activity Relationship (3D-QSAR) models and linear Support Vector Machine (SVM) classifiers to identify small molecule inhibitors of the BRAF-V600E and HIV Integrase targets are reported. Performance comparisons were carried out using 303 compounds (68 active) against BRAF-V600E and 204 compounds (159 active) against HIV Integrase. A SVM prediction accuracy of 95% (BRAF-V600E) and 100% (HIV Integrase) and 3D-QSAR prediction accuracy of 76% (BRAFV600E) and 82% (HIV Integrase) was observed. To help explain the better performance of SVM in the comparison reported here and to help assess the degree to which a SVM or 3D-QSAR model is likely to perform best for other targetligands of interest a new EPP (Expected Predictive Performance) metric is introduced. How EPP can be used to help predict future performance of SVM and 3D-QSAR models by quantifying the degree of similarity between candidate compounds and training data is also demonstrated. Results show that the EPP metric is capable of predicting future prediction accuracy of SVM and 3D-QSAr models within 7% of actual performance.

How biotic interactions may alter future predictions of species distributions: future threats to the persistence of the arctic fox in Fennoscandia

AbstractAim With climate change, reliable predictions of future species geographic distributions are becoming increasingly important for the design of appropriate conservation measures. Species distribution models (SDMs) are widely used to predict geographic range shifts in response to climate change. However, because species communities are likely to change with the climate, accounting for biotic interactions is imperative. A shortcoming of introducing biotic interactions in SDMs is the assumption that biotic interactions remain the same under changing climatic factors, which is disputable. We explore the performance of SDMs while including biotic interactions.Location Fennoscandia, Europe.Methods We investigate the appropriateness of the inclusion of biotic factors (predator pressure and prey availability) in assessing the future distribution of the arctic fox (Alopex lagopus) in Fennoscandia by means of SDM, using the algorithm MaxEnt.Results Our results show that the inclusion of biotic interactions enhanced the accuracy of SDMs to predict the current arctic fox distribution, and we argue that the accuracy of future predictions might also be enhanced. While the range of the arctic fox is predicted to have decreased by 43% in 2080 because of temperature‐related variables, projected increases in predator pressure and reduced prey availability are predicted to constrain the potential future geographic range of the arctic fox in Fennoscandia 13% more.Main conclusions The results indicate that, provided one has a good knowledge of past changes and a clear understanding of interactions in the community involved, the inclusion of biotic interactions in modelling future geographic ranges of species increases the predictive power of such models. This likely has far‐reaching impacts upon the design and implementation of possible conservation and management plans. Control of competing predators and supplementary feeding are suggested as necessary management actions to preserve the Fennoscandian arctic fox population in the face of climate change.

Mortality Projections using Generalized Additive Models with applications to annuity values for the Irish population

AbstractGeneralized Additive Models (GAMs) with age, period and cohort as possible covariates are used to predict future mortality improvements for the Irish population. The GAMs considered are the 1-dimensional age + period and age + cohort models and the 2-dimensional age-period and age-cohort models. In each case thin plate regression splines are used as the smoothing functions. The generalized additive models are compared with the P-Spline (Currie et al., 2004) and Lee-Carter (Lee & Carter, 1992) models included in version 1.0 of the Continuous Mortality Investigation (CMI) library of mortality projections. Using the Root Mean Square Error to assess the accuracy of future predictions, the GAMs outperform the P-Spline and Lee-Carter models over intervals of 25 and 35 years in the age range 60 to 90. The GAMs allow intuitively simple models of mortality to be specified whilst also providing the flexibility to model complex relationships between the covariates. The majority of morality improvements derived from the projections of future Irish mortality yield annuity values at ages 60, 65, 70 and 80 in 2007 in the range of annuity values calculated, assuming a 2 to 4 percent annual compound improvement in mortality rates for both males and females.

Mapping Water-Table Depths Over Time to Assess Desiccation of Groundwater-Dependent Ecosystems in the Netherlands

During the past decades, groundwater-dependent ecosystems in the Netherlands have been threatened by a decline in the water-table level. However, information on water-table depths and changes in water-table depths is insufficient and outdated. For policy evaluation, spatially explicit and detailed information on water-table depths is required, especially in areas with groundwater-dependent ecosystems including wetlands. Some 35,000 observations of seasonal fluctuation characteristics of water-table depths in nature conservation areas were made since 1980 during soil surveys. These observations were derived from characteristics of the soil profile or from measurements in boreholes. These observations were used in a space-time geostatistical analysis to map the seasonal fluctuation of water-table depths between 1980 and 2007. First, systematic differences between different estimation modes used through the years were corrected. Next, observations were correlated with area-wide available ancillary data using multiple linear regression. Simple kriging was used to interpolate the resulting space-time residuals. Maps of the predicted mean spring water-table depths and the accuracy of these predictions were used to identify areas where the water-table is deeper than favorable. Changes of water-table depths on the national scale over the past 25 years were assessed, and recommendations were made to increase the accuracy of future predictions.

Measurement of stress-dependent permeability in coal and its influence on coalbed methane production : Puri, R; Seidle, J P In SituV16, N3, 1992, P183–202

It is known that coal permeability measured in the laboratory is highly sensitive to the net confining pressure applied on the sample. Since many coalbed methane modeling studies assume that the permeability of a coal seam does not change with reservoir pressure depletion, skepticism about the accuracy of future predictions is understandable. This paper presents laboratory-measured permeability reduction during reservoir pressure depletion using a whole-core sample of coal in uniaxial strain at reservoir conditions. These laboratory results have been used in a numerical simulation study to determine the influence of pressure-dependent permeability on coalbed methane production. The productivity of a coalbed methane well is substantially reduced because of the decline in coal permeability during reservoir pressure depletion. More significantly, most of the reduction in gas and water production rates because of pressure-dependent permeability occurs during well start-up. Thereafter, the well productivity loss due to stress dependent permeability is fairly gradual over a very long period of time. The characteristic gas and water production-rate profiles obtained from a numerical simulator are almost the same regardless of whether the coal seam permeability is assumed to be constant or stress-dependent.