Computing Sources of Epileptic Discharges Using the Novel BMA Approach

Study regionAkaki is a headwater catchment of Awash River Basin that hosts the capital city of Ethiopia, Addis Ababa. The area encompasses several agglomerated towns, water supply, and hydropower reservoirs and is characterized by a chain of mountains and floodplains. Due to basin rainfall, and the expansion of urbanized areas, the catchment is frequently affected by flooding. Study focusThis study evaluates dynamic Bayesian Model Averaging (BMA) approach to improve rainfall estimation over the catchment by blending four high-resolution satellite rainfall estimate (SRE) products. Using daily data (2003–2019) observed at thirteen stations as a reference, seven statistical metrics served to assess the point and spatial scale accuracy of the rainfall estimates. New hydrological insightsMain findings from this study are: (i) the blended product outperformed the individual SRE products by notably improving correlation with in-situ observed rainfall, and reducing the error of the estimated rainfall, (ii) the blended and individual SRE products performed better in the highlands than the lowlands of the catchment, and (iii) the amount of daily rainfall during the main-rainy season was mostly overestimated by the individual SRE products but was fairly estimated by the blended product. This study showed the nonexistence of surpassing individual SRE products and emphasized the blending of several products for gaining optimal results from each product.

To study the impact of training sample heterogeneity on the performance of Bayesian model averaging (BMA), two BMA experiments were performed on probabilistic quantitative precipitation forecasts (PQPFs) in the northern China region in July and August of 2010 generated from an 11-member short-range ensemble forecasting system. One experiment, as in many conventional BMA studies, used an overall training sample that consisted of all available cases in the training period, while the second experiment used stratified sampling BMA by first dividing all available training cases into subsamples according to their ensemble spread, and then performing BMA on each subsample. The results showed that ensemble spread is a good criterion to divide ensemble precipitation cases into subsamples, and that the subsamples have different statistical properties. Pooling the subsamples together forms a heterogeneous overall sample. Conventional BMA is incapable of interpreting heterogeneous samples, and produces unreliable PQPF. It underestimates the forecast probability at high-threshold PQPF and local rainfall maxima in BMA percentile forecasts. BMA with stratified sampling according to ensemble spread overcomes the problem reasonably well, producing sharper predictive probability density functions and BMA percentile forecasts, and more reliable PQPF than the conventional BMA approach. The continuous ranked probability scores, Brier skill scores, and reliability diagrams of the two BMA experiments were examined for all available forecast days, along with a logistic regression experiment. Stratified sampling BMA outperformed the raw ensemble and conventional BMA in all verifications, and also showed better skill than logistic regression in low-threshold forecasts.

Application of Bayesian model averaging in modeling long-term wind speed distributions

The development of high resolution mapping models of forest attributes based on employing machine or deep learning techniques has increasingly accelerated in the last couple of years. The consequence of this is the widespread availability of multiple sources of information, which can either lead to a potential confusion, or to a possibility to get an "extended” insight into the state of our forests by interpreting these sources jointly. This contribution aims at addressing the latter, by relying on the Bayesian model averaging (BMA) approach.BMA is a method that can be used in building a consensus from an ensemble of different model predictions. It can be seen as weighted mean of different predictions with weights reflecting the predictive performances of different models, or as a finite mixture model which estimates the probability that each observation from the independent validation dataset has been generated by one of the models belonging to the ensemble. BMA can thus be used to diagnose and understand the difference in the predictions and to possibly interpret them.The predictions in our case are the forest canopy height estimations for the metropolitan France coming from 5 different AI models [1-5], while the independent validation dataset comes from the French National Forest Inventory (NFI) disposing with some 6000 plots per year, distributed across the territory of interest. For every plot we have several measurements/estimations of the forest canopy height out of which the following two are considered in this study: h_m – the maximum total height (from the tree's base level to the terminal bud of the tree's main stem) measured within the plot, and h_dom – the average height of the seven largest dominant trees per hectare.In this contribution we present for every considered plot the dominant model with respect to both references i.e. the model having the highest probability to be the one generating measurements/estimations at NFI plot (h_m and h_dom). We present as well as the respective inter-model and the intra-model variance estimations, allowing us to propose a series of hypotheses concerning the established differences between predictions of individual models in function of their specificities.[1] Schwartz, M., et al.: FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach, Earth Syst. Sci. Data, 15, 4927–4945, 2023, https://doi.org/10.5194/essd-15-4927-2023[2] Lang, N., et al.: A high-resolution canopy height model of the Earth, Nat Ecol Evol 7, 1778–1789, 2023. https://doi.org/10.1038/s41559-023-02206-6[3] Morin, D. et al.: Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process, Remote Sens., 14, 2079. 2022, https://doi.org/10.3390/rs14092079[4] Potapov, P., et al.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sensing of Environment, 253, 2021, https://doi.org/10.1016/j.rse.2020.112165.[5] Liu, S. et al.: The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe, Sci. Adv. 9, eadh4097, 2023, 10.1126/sciadv.adh4097.

Bayesian model averaging (BMA) is a statistical way of post-processing forecasts ensembles to create predictive probability density functions (pdfs) for weather quantities. BMA has been proposed as way of correcting underdispersion in ensemble forecasts. The output of ensemble BMA is a weighted average of pdfs centered on the individual bias-corrected ensemble forecast. The BMA weights are posterior probabilities of the models generating the forecast, and show the relative contribution of the component models to the prediction over the training period. This paper focuses on short range forecasting of daily maximum temperature. Forecasting temperature, we have approximated the conditional pdf by normal distribution centered at a linear function of the forecast. Since we are not able to get an ensemble of forecasts for Ethiopia, we have used the previous eight years from 2003 to 2010 data as ensemble and 2011 as observation. The BMA approach yields predictive distribution for temperature thatwere much better calibrated than those based on naive averaging of the forecasts.

This paper briefly discusses the nature, the causes and the role of predictive uncertainty in flood forecasting and proposes a novel approach to its estimation. Following the definition of predictive uncertainty, its importance in the decision process is highlighted in relation to the different sources of errors (model, parameter, observations, boundary conditions) that affect flood forecasting. Moreover, the paper briefly analyses the importance of using a full predictive uncertainty, obtained by marginalising the parameter uncertainty, instead of the predictive uncertainty conditional to a single parameter set. Finally, a new Model Conditional Processor (MCP) for the assessment of predictive uncertainty is then proposed as an alternative to the Hydrologic Uncertainty Processor (HUP) introduced by Krzysztofowicz as well as to the Bayesian Model Averaging (BMA) approach due to Raftery et al. The new MCP approach, which aims at assessing, and possibly reducing, predictive uncertainty, allows combination of the observations with one or several models’ forecasts in a multi‐Normal space, by transforming observations and model forecasts in a multivariate Normal space by means of the Normal Quantile Transform. The results of the new approach are shown for the case of the River Po in Italy, and compared with the results obtainable both with the HUP and the BMA.

Integrated dynamic framework for predicting urban flooding and providing early warning

Applying Bayesian Model Averaging to mechanistic models: An example and comparison of methods

To accurately determine the frequency and nature of the family history of seizures in patients with benign childhood epilepsy with centrotemporal spikes (BECTS). Participants with BECTS were recruited from the electroencephalography (EEG) laboratories of three pediatric centers and by referral. Pedigrees were constructed for up to three degrees of relatedness for each proband. All available affected and unaffected individuals underwent phenotyping using a validated seizure questionnaire. The proportion of affected relatives according to degree of relatedness was calculated and phenotypic patterns were analyzed. Fifty-three probands with BECTS had a mean age of seizure onset at 7.8 years (range 2-12 years). Thirty-four (64%) of 53 patients were male. For 51 participants, pedigrees were available for three degrees of relatedness. Fifty-seven (2.7%) of 2,085 relatives had a history of seizures: Twenty-one (9.8%) of 214 first-degree, 15 (3%) of 494 second-degree, and 21 (1.5%) of 1,377 third-degree relatives. Febrile seizures were the most frequent phenotype, occurring in 26 of 57 affected relatives. There were 34 relatives with epilepsy: 6.5% (14 of 214) first-degree, 1.8% (9 of 494) second-degree, and 0.8% (11 of 1,377) third-degree relatives. Of 21 affected first-degree relatives: 8 of 21 had febrile seizures (FS), 4 had BECTS, 2 had epilepsy-aphasia spectrum disorder, one had temporal lobe epilepsy with hippocampal sclerosis, 2 had focal epilepsy of unknown cause, 2 had genetic generalized epilepsies, and 3 had miscellaneous. The frequency of epilepsies in relatives and the heterogeneous syndromes observed suggest that BECTS has a genetic component consistent with complex inheritance. Focal epilepsies are the most common seizure disorder observed in relatives, especially BECTS and epilepsy-aphasia spectrum disorder. Additional acquired or environmental factors are likely to be necessary for expression of the seizure disorder.

With the rise and development of smart infrastructures, there has been a great demand for installing automatic monitoring systems on bridges, which are key members of transportation networks. In this regard, utilizing the data collected by the sensors mounted on the vehicles passing over the bridge can reduce the costs of the monitoring systems, compared with the traditional systems where fixed sensors are mounted on the bridge. This paper presents an innovative framework for determining the response and for identifying modal characteristics of the bridge, utilizing only the accelerometer sensors on the moving vehicle passing over it. In the proposed approach, the acceleration and displacement response of some virtual fixed nodes on the bridge is first determined using the acceleration response of the vehicle axles as the input. An inverse problem solution approach based on a linear and a novel cubic spline shape function provides the preliminary estimations of the bridge's displacement and acceleration responses, respectively. Since the inverse solution approach is only capable of determining the response signal of the nodes with high accuracy in the vicinity of the vehicle axles, a new moving-window signal prediction method based on auto-regressive with exogenous time series models (ARX) is proposed to complete the responses in the regions with large errors (invalid regions). The mode shapes and natural frequencies of the bridge are identified using a novel approach that integrates the results of singular value decomposition (SVD) on the predicted displacement responses and frequency domain decomposition (FDD) on the predicted acceleration responses. To evaluate the proposed framework, various numerical but realistic models for a single-span bridge under the effect of a moving mass are considered; the effects of different levels of ambient noise, the number of axles of the passing vehicle, and the effect of its speed on the accuracy of the method are investigated. The results show that the proposed method can identify the characteristics of the three main modes of the bridge with high accuracy.

ABSTRACTMonetary policy has been usually analyzed in the context of small macroeconomic models where central banks are allowed to exploit a limited amount of information. Under these frameworks, researchers typically derive the optimality of aggressive monetary rules, contrasting with the observed policy conservatism and interest rate smoothing. This paper allows the central bank to exploit a wider information set, while taking into account the associated model uncertainty, by employing Bayesian model averaging with Markov chain model composition. In this enriched environment, we derive the optimality of smoother and more cautious policy rates, together with clear gains in macroeconomic efficiency.

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 56:33-49 (2013) - DOI: https://doi.org/10.3354/cr01140 Model skill measures in probabilistic regional climate projections for Ireland Aideen Foley1,2,*, Rowan Fealy1, John Sweeney1 1Department of Geography, NUI Maynooth, Co. Kildare, Ireland 2Present address: Department of Land Economy, University of Cambridge, Cambridge CB3 9EP, UK *Email: amf62@cam.ac.uk ABSTRACT: In the present study, a range of regional climate models have been used to test approaches to Bayesian model averaging (BMA), particularly the quantification of model weights/Bayesian priors. The results of skill assessments were used to inform probabilistic future projections of Irish climate using a BMA approach in order to evaluate how different approaches to skill assessment, based on representation of climate means, or of a large-scale driver (the NAO), or a combination thereof, may influence the final climate projection. Results indicate that means-based skill assessments may not always provide a useful indication of model skill and that further analyses are required to assess a model’s ability to simulate the dynamics of the climate system. While this research illustrates that the use of metrics derived from the model predicted NAO impacts on the regional projection, it also supports the inclusion of other large-scale model diagnostics. When used to weight model projections to produce ensemble climate projections, the choice of skill metric may have an impact on the shape of the probability distribution and the most probable outcome of future climate predictions. The present study demonstrates that when working with probabilistic outputs of ensemble climate modelling experiments, awareness of the approaches used to evaluate models and the techniques used to combine them to formulate ensemble projections are integral in enabling robust responses to the potential changes in climate represented by models. KEY WORDS:Regional climate modelling · Model assessment · Ireland · NAO · Bayesian model averaging Full text in pdf format PreviousNextCite this article as: Foley A, Fealy R, Sweeney J (2013) Model skill measures in probabilistic regional climate projections for Ireland. Clim Res 56:33-49. https://doi.org/10.3354/cr01140 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 56, No. 1. Online publication date: February 21, 2013 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2013 Inter-Research.

Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach

Determining the most influential parameters affecting the reservoir flow responses is a vital step in the integrated reservoir studies for evaluation and analyses. More specifically, removing the non-influencing parameters leads to reach the optimal process design. The conventional procedure to determine the most sensitive parameters combines regression analysis with analysis of variance. However, that approach produces one reduced regression model after eliminating the non-influential parameters (deterministic approach). In this paper, Bayesian Model Averaging (BMA) was applied to stochastically identify the geological parameters that control the immiscible CO2-assisted gravity drainage process performance in a multilayer heterogeneous sandstone oil reservoir in South Rumaila oil field. After achieving acceptable history matching within approximately 57 year of production, the CO2-assisted gravity drainage injection was evaluated in 10 year future prediction. In that process, vertical wells were placed at the top of the reservoir for CO2 injection to formulate a gas cap that make oil drains down towards the bottom of reservoir. Above the oil-water contact, a series of horizontal production wells were installed to produce oil. The main geological parameters that controls the immiscible CO2 flooding are horizontal permeability, anisotropy ratio (Kv/Kh), and porosity that were investigated for their impact by layers or multilayer. Many low-discrepancy designed simulations were created by the Latin Hypercube Design and evaluated by the reservoir flow simulation to disregard the no-impact parameters using the linear BMA approach. Among multi- candidate models, BMA select the best model of optimal subset variables based on the highest posterior probability. BMA led to quantify accurate impact of each geological control on the CO2 EOR process. Based on the concept of the CO2-assisted gravity drainage process, it was concluded that horizontal permeability has a higher impact of the reservoir flow response than vertical permeability (Kv/Kh) with no effect of porosity.

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25o to 0.05o while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.