Joint Probability Model Research Articles

Flood risk and overtopping assessment for an exposed coastal road

The coastal village of Vík is located on the South Coast of Iceland. The area is subjected to the full force of extratropical cyclones of the North Atlantic. The village beach is not a stable phenomenon, after a nearby volcanic eruption in 1918 it accreted by 500 m over 50 years but has eroded at similar rate since 1970, which poses increased flood risk in the area. There are plans to move the current ring road, that lies through the village, with one possibility of it running along the coast. A study was conducted using multivariate extreme value modelling to determine the desired coastal defences needed for the new road. The method in question uses a multivariate joint probability model to simulate variables that cause coastal flooding events. It generates a large sample of extreme ocean events based on a 52-year time series of hindcast data. The large sample of extreme events were then used to determine return periods for overtopping events and to determine a desired crest height for coastal defence structures. The study puts forth suggestions of improvements on current coastal defence structures and the design specification for defence structures along a planned road along the coast.

Joint probabilistic modeling approach for harmonic and three-phase unbalanced disturbance sources

Harmonics and three-phase imbalance are two common disturbances in power systems that interact with and affect each other, leading to more complex impacts on power system performance. To comprehensively and accurately analyze the distribution and interaction mechanisms of harmonics and three-phase imbalance, this study investigated the joint emission level of harmonic and three-phase unbalanced disturbance sources (H-TU-DS). This paper proposes a joint probabilistic emission level modeling approach. The power, harmonic, and three-phase unbalanced data of H-TU-DS are multidimensional, and the feature dataset is filtered by calculating multiple correlation coefficients to achieve dimensionality reduction without losing data information. Considering the different operating characteristics of the H-TU-DS, an improved fuzzy C-means (FCM) algorithm is applied to classify operating conditions, enabling a comprehensive and accurate analysis of the joint emission level. Based on the nonparametric kernel density function, joint probability modeling of H-TU-DS is performed using an adaptive bandwidth improvement method, effectively enhancing the accuracy of the modeling process. Finally, the effectiveness and accuracy of the proposed methods for feature dataset filtering, operating condition classification, and joint probability modeling are demonstrated through comparisons with both simulated and actual data. The proposed model facilitates the prediction and assessment of the impact of H-TU-DS on power quality after grid connection, enabling the effective prevention of power-quality problems through advanced treatment.

3D indoor area recognition for personnel security using integrated UWB and barometer approach

Real-time location tracking is essential for personal security in industrial scenes, e.g. monitoring worker safety in power substations. Ultra wideband (UWB) technology is suitable for indoor positioning thanks to its high penetration capability, high ranging accuracy and low power consumption. However, UWB based trilateration positioning requires high workload for field deployment of base stations. Indoor complex topology results in multipath and non-line of sight (NLOS) conditions of UWB signals, and degrades the positioning performance in terms of accuracy and reliability. This paper proposes a three-dimensional (3D) area recognition solution by integrating UWB time of flight (TOF) ranging and barometer measurements. The proposed solution utilizes a multi-tier distributed joint probabilistic inference model, which accomplishes the indoor 3D area recognition exploiting multiple clustering and prediction algorithms of machine learning. The field experiments showed that the proposed method can achieve an accuracy of 3D area recognition of more than 99.2%. The proposed method improves the computing efficiency by 93%. The errors of improved differential barometric height estimation method are less than 1 m, which means a success rate of 100% for floor identification, given a floor separation of 3–4 m. The proposed solution is suitable for personnel security applications of industrial scenes, which requires reliable real-time area information rather than just coordinates.

Joint probabilistic modeling of extreme wind-wave conditions under typhoon impact and applications to extreme response analysis of floating offshore wind turbines

The joint probabilistic modeling of extreme wind and wave conditions under typhoon impact is a critical task for extreme response analysis of floating offshore wind turbines (FOWTs) deployed in typhoon-prone areas. However, wind and wave observations under typhoon impact are quite limited, which poses great challenges to the probabilistic modeling task. Although some sophisticated programs can be employed to simulate typhoon-induced wave fields, they suffer from unbearable computational burdens. To improve the accuracy and efficiency of joint probabilistic modeling of extreme wind and wave conditions under typhoon impact, an approach that synthesizes the typhoon hazard analysis method and copula-based conditional modeling method is proposed in the present study. The proposed joint distribution model is expressed as the product of the distribution of extreme wind speed and the conditional distribution of significant wave height. The distribution of extreme wind speed is estimated through the typhoon hazard analysis method, and the conditional distribution of significant wave height is modeled by the copula-based method. The effectiveness of the proposed method is verified by comparing with observation data. Applications of the proposed method to extreme response analysis of FOWTs are illustrated as well.

Wind speed multi-step prediction based on the comparison of wind characteristics and error correction: Focusing on periodic thermally-developed winds

To leverage the abundant wind energy resources available in mountainous regions, an increasing number of wind power facilities and associated transportation infrastructure are being constructed in these areas. There are different types of intense winds in mountainous areas and the non-linear characteristics of the different intense winds vary significantly. The periodic thermally-developed winds are the commonly occurring type of intense wind. Based on the comparison of wind characteristics, this research proposes a prediction model for the periodic thermally-developed winds. The research utilizes the Bi-directional Long Short-Term Memory model(Bi-LSTM), which has been optimized by the Bayesian optimization(BO), to predict wind speed. Based on the relationship between wind field characteristics in periodic thermally-developed winds, a regression correction module is proposed using a joint probability model. Additionally, to address the volatility and uncertainty of wind speed, a Gaussian mixture model is employed for probability prediction. The experimental results demonstrate several findings: 1. The BO-Bi-LSTM model accurately and reliably predicts multi-step results for periodic thermally-developed winds in mountainous areas. 2. The proposed correction module, when combined with other models, yields improved wind speed accuracy compared to the uncorrected values. 3. The Gaussian mixture model produces probabilistic prediction results that consider both interval coverage and interval bandwidth simultaneously. 4. The proposed model can successfully predict periodic thermally-developed winds in mountainous areas for practical engineering purposes, resulting in reliable probabilistic prediction outcomes. It is of great significance for the utilization of wind energy resources and the driving safety of vehicles on ancillary structures.

Probabilistic-based seismic fragility analysis of a ground-bridge structure system considering site liquefaction

Bridge structures are vulnerable to severe damage in previous earthquake events. Meanwhile, the earthquake-induced site liquefaction can significantly affect the seismic performance of bridge structures. Seismic fragility analysis is considered as an effective approach for evaluating seismic performance of bridge structural systems exposed to seismic hazards. This study aims to assess seismic fragility of a ground-bridge structure system considering site liquefaction. Based on the probabilistic seismic demand model (PSDM) and joint probabilistic seismic demand model (JPSDM), a holistic framework for fragility analysis considering site liquefaction is proposed to derive the liquefaction fragility as well as the structural system fragility. The system fragility surfaces obtained by the proposed method can reflect the influence of site properties on the damage exceeding probability of bridge structures. The results demonstrate an increasing probability of undergoing high-level damage states for the whole system containing highly loose sand under strong earthquakes, which may provide significant basis for probabilistic-based aseismic design.

Performance-based design of environmental parameters for offshore wind turbine foundations

Taking into account the coupling effects of wind, wave, and current on offshore wind turbines (OWTs), a combined approach utilizing non-linear finite element analysis (FEA), back propagation neural network (BPNN), and probabilistic statistical methodology (PSM) is proposed for accurate environmental parameter estimation in OWT foundation design. The structural bearing performances of OWT foundations are precisely predicted through a combination of FEA and BPNN models, and then serve as constraints for probabilistic statistical analysis. Furthermore, for PSM, a robust multivariate joint probability distribution is constructed leveraging copula functions, which not only maximizes marginal information but also effectively captures the intricate dependencies among ocean environmental parameters. Subsequently, the proposed models, along with traditional univariate analysis, are utilized to estimate the return values of diverse load combinations. The results indicate that, for a given return period, the conditional probability model yields lower wave heights and current velocities compared to univariate analysis, while the joint probability model results in lower values for all ocean parameters, making both approaches more suitable for OWT foundation design. This coupled approach presents a more rational and cost-effective solution compared to univariate analysis, potentially resulting in decreased investment costs.

Framework for assessing the performance of overhead transmission lines under wind-temperature effects

This paper presents a framework for evaluating the wind-induced resistance of overhead transmission lines (OTLs), aiming to overcome the limitations associated with disregarding temperature effects and relying on a single metric for failure assessment. The proposed framework factors in temperature influence, carefully considers applied loads and structural characteristics, and accurately calculates failure probability based on a specific case study. Meteorological data is initially collected to create a probabilistic model of wind speed and temperature distribution, accounting for correlation. Components and systems are graded based on structural characteristics and damage modes. The neural network surrogate model is trained with finite element computations for load-structural response sample sets. The surrogate model, in conjunction with logistic regression, generates samples to determine fragility. Finally, the total structural failure probability and monthly failure likelihood are computed by integrating the joint probability model and fragility. Analyses highlight temperature's significant impact on conductor-ground line safety, validating the proposed framework's rationale.

Factors influencing canine rabies vaccination among dog-owning households in Nigeria

Rabies perpetuates in Nigeria despite initiatives like the Regional Disease Surveillance System Enhancement Project, with evidence indicating suboptimal canine vaccination rates as a contributing factor. To inform effective planning of mass dog vaccination campaigns, it is crucial to understand the factors associated with variation in canine vaccination rates. We conducted a cross-sectional study in 2022 to understand factors associated with canine vaccination. We used stratified random sampling of the streets and dog-owning households to survey 4162 households from three states and the Federal Capital Territory (FCT). We then built a joint probabilistic model to understand factors associated with dog vaccination and non-vaccination. First, we modelled rabies knowledge as a latent variable indirectly measured with several targeted survey questions. This method allowed a respondent's unobserved understanding of rabies to be estimated using their responses to a collection of survey questions that targeted different aspects of rabies epidemiology and took various possible response distributions (i.e., ordinal, categorical, binary). Second, we modelled factors influencing pet owners' decisions to vaccinate their dogs against rabies and barriers to dog vaccination among dog owners whose dogs were not vaccinated against rabies. Posterior distributions revealed that the probability of dog vaccination was positively associated with the owner's latent knowledge of rabies, civil servant service employment, residence in the FCT, ownership of a single dog, providing care to dogs, and a preference for contemporary treatment following a dog bite. Conversely, non-vaccination was positively associated with private employment, residing in Anambra and Enugu states, owning multiple dogs, allowing dogs to search for leftovers, and a preference for traditional treatment after a dog bite. Cost was the primary barrier against vaccination for dog owners in Anambra and Enugu, while mistrust posed a major challenge for those in the FCT. Owners in areas with veterinary establishments cited cost as a barrier, while those without a veterinary establishment cited access as the primary barrier. Our study underscores the need to enhance rabies knowledge, tailor vaccination campaigns to specific demographics, address financial and access barriers, and combat hesitancy to improve rabies vaccination rates in Nigeria.

Fragility Analysis of a Transmission Tower-Line System Subjected to Wind and Ice Loads Considering Fatigue Damage

Disasters such as ice and wind can pose a serious threat to the normal operation of power transmission lines. Wind-induced fatigue damage can further reduce the load-carrying capacity of transmission towers, increasing their fragility to ice and wind disasters. To assess the comprehensive capability of transmission towers to resist extreme ice and wind disasters, this paper proposes a failure probability evaluation framework for transmission towers considering wind-induced fatigue damage under the coupled effect of ice and wind. Taking a certain transmission line in Hunan as an example, the failure probability caused by ice and wind disasters is calculated for different years of service. First, based on the historical meteorological data in three cities in Hunan, a joint probability model of wind speed and wind direction considering their correlation is established using the copula function. Then, based on this probability model, the wind-induced fatigue damage of transmission towers is calculated using the Miner linear damage theory and the S-N curve. Subsequently, the fragility of the tower under the coupled load of ice and wind is calculated for different years of service. Finally, by combining the structural fragility function with the joint probability distribution model of ice thickness and wind speed, the collapse probability of the transmission tower under the action of ice and wind disasters is calculated. The results indicate that the influence of wind-induced fatigue damage cannot be ignored when transmission towers encounter ice and wind disasters. With increasing service time, the ability of transmission towers to resist ice and wind disasters gradually decreases, and the failure probability also increases under extreme ice and wind conditions.

Abstract 3512: Joint probabilistic modeling of multimodal metabolic data with UnitedMet

Abstract Metabolites play a crucial role in the functioning of biological systems. Measurements of metabolite abundances by metabolomics and metabolic pathway activity by isotope-tracing are powerful approaches to study metabolic phenotypes. However, large-scale measurements of metabolite levels and pathway activity remain extremely scarce due to the technical challenges. Motivated by the strength of RNA-metabolite covariation, we present UnitedMet, a Bayesian probabilistic method for end-to-end joint modeling of RNA-sequencing and metabolic datasets that is capable of dimensionality reduction, data integration and metabolic modality prediction. UnitedMet demonstrates high accuracy predicting metabolite levels and isotopologue distributions from gene expression data in human tumor samples. To evaluate UnitedMet’s efficacy in clinical applications, we inferred metabolomic profiles and isotope labeling patterns from the RNA sequencing data of clear cell renal cell carcinoma (ccRCC) patients enrolled in TCGA and clinical trials. Correlation analysis between genetic mutations and predicted metabolic phenotypes revealed that BAP1 mutations were associated with increased contribution of glucose to TCA cycle in ccRCC. Increased TCA cycle activity was found to be associated with disease progression and poorer clinical outcome. UnitedMet, therefore, provides a solution that could be broadly applicable to enhance multimodal metabolomic datasets and facilitates comprehensive study of interplays between metabolic profiles, molecular alterations and human phenotypes. Citation Format: Amy X. Xie, Casey Bradshaw, Christopher Tosh, Wesley Tansey, Ed Reznik. Joint probabilistic modeling of multimodal metabolic data with UnitedMet [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3512.

Second-based queue length estimation with fusing MMW and low penetration rate CAV trajectory data

This paper combines millimeter-wave radar (MMW) data with connected autonomous vehicle (CAV) trajectory data to estimate queue length on a second-by-second basis. Firstly, queued vehicles on multiple lanes with the same traffic movement are mapped to a virtual lane. Then, the presence of CAV or human-driven vehicle (HDV) for any given queueing index is determined. A Bayesian joint probability model is subsequently established for queue length expectation, considering the existence of the queued vehicle type as a condition. The average time headway and dissipation speed distribution are derived from departure timestamps, which allows for the calculation of the prior probability ratio of queue length. Lastly, the maximum likelihood estimation (EM) algorithm is employed for iteratively estimating the CAV penetration rate. Simulation results demonstrate the method offers a compelling trade-off between precision and second-based real-time performance, while field test results further confirm the wide applicability under various traffic conditions.

Probabilistic monitoring of meteorological drought impacts on water quality of major rivers in South Korea using copula models

The impacts of drought range from water supply for humans to ecosystems. Drought affects river water quality by disturbing the hydrological regime in a variety of ways, and can degrade water quality by reducing surface and groundwater availability. In particular, drought-induced low flows, reduced nutrient dilution, and extreme increases in water temperature affect various water quality parameters in streams. Furthermore, the effects of drought on stream water quality may vary from season to season and from stream segment to stream segment, which requires careful investigation. In this study, Environmental Drought Condition Index - water quality (EDCI-wq) is proposed using a bivariate copula joint probability model between meteorological drought index and river water quality. Using this, environmental drought with respect to water quality is defined, and it is confirmed that environmental drought with respect to water quality can be routinely monitored through time series analysis and mapping of the proposed EDCI-wq. In addition, in order to express the environmental drought condition more explicitly to the general public, the environmental drought condition is graded into four classes based on the EDCI-wq. Furthermore, the sensitivity of river water quality to meteorological drought was estimated using the copula joint probability model, which allowed us to identify river segments that are relatively more sensitive to meteorological drought events.

Copula-Based Probabilistic Hazard Assessment Model for Debris Flow Considering the Uncertainties of Multiple Influencing Factors

This paper proposes a probabilistic hazard assessment model for debris flows considering the uncertainties of multiple influencing factors based on copula approaches. Fifty-nine rainfall-induced debris flows occurred between 2001 and 2009 in Taiwan are taken as an illustrative example to validate the proposed approaches. A copula-based probabilistic model is developed to model the joint probability distribution of debris-flow volume V and its influencing factors (e.g., rainfall intensity, RI and landslide area, AL). The developed model is then used to make probabilistic prediction of debris-flow volume for a specific hazard level, and compared with the empirical approaches. The proposed probabilistic model is also used to develop the exceedance probability charts of quantities for a specific debris-flow basin. Results show that the developed V–RI–AL probabilistic model can provide reasonable estimates of debris-flow volume in Taiwan for a specific probability level of 0.94, and show better predictive performance than the empirical relationships by using an independent debris-flow dataset in Taiwan for validation. The developed multivariate joint probabilistic model can also provide the exceedance probability of debris flows through considering the uncertainties of debris flow and its influencing factors, providing a preliminary reference for hazard assessment of the debris flows.

Seismic robustness computational methodology of community building portfolio coupled with water supply network based on probability‐cloud model

AbstractA novel seismic robustness computational methodology is proposed for community building portfolio (CBP) coupled with lifeline networks based on probability‐cloud model. This method consists of three interrelated submodels, respectively, the building fragility analysis model of the main subsystem CPB, the water supply network (WSN) hydraulic simulation analysis model of the secondary subsystem WSN, and the probability‐cloud model. The proposed method can be used to predict the seismic robustness of hierarchical systems. The computational methodology uses a joint probabilistic model combined with building fragility analysis and WSN hydraulic simulation analysis to evaluate the robustness of CPB coupled with WSN. Due to the uncertainty in robustness analysis, a probabilistic‐cloud model procedure considering the uncertainty in seismic robustness is proposed. The procedure enables a qualitative and quantitative integrated analysis of the robustness of CBP coupled with WSN. Finally, a community with 1406 buildings in China is simulated to illustrate the method. The research outcomes can provide a useful reference for the seismic robustness analysis of CBP coupled with WSN.

Are seniors in mountainous areas able to realize their desired trips? A novel approach to estimate trip demand

Public transport services in mountainous areas are limited, and seniors living in such areas experience various inconveniences in their daily lives. Therefore, the planning of adequate services is crucial in improving the quality of life. Conventionally, public transport services are planned based on observed trip demands, which are referred to as realized trip demands in this study. However, this approach is inadequate because seniors living in an area with insufficient public transport services are generally unable to realize all their desired trips. This study proposes a model to predict the desired trip demand based on the analysis of the travel conditions of seniors in mountainous areas. The proposed model predicts the desired trip demands based on both the realized and unrealized trip demands. The bivariate Poisson regression model is applied to the distribution of desired trip demands and mobility constraints, and the joint probability model, which considers the relationship between them, is proposed. Additionally, in the case where respondents are unable to report the unrealized trip demands accurately, an alternative method is implemented to estimate the desired trip demand based only on the information on whether unrealized trip demand exists, and its estimation results are discussed. The analysis results demonstrate that the conventional trip demand model, established using realized trip demands, underestimates the trip demand in the target area and show that the estimated parameters are biased. The conventional trip demand model neglects 30–50% of trip demands in the target area and cannot provide the trip demand required for appropriate transportation planning. The reproducibility of the desired trip demands can be further improved by extending the model to consider overdispersion or the case where unrealized trip demands have a large share of zero trips or both.

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing.

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-art deep-learning-based methods cannot provide interpretability from an energy perspective, affecting the accuracy of the design. Here we propose a new systematic approach, including both a posterior probability and a joint probability parts, to solve the two essential questions once for all. This approach takes the physicochemical property of amino acids into consideration and uses the joint probability model to ensure the convergence between structure and amino acid type. Our results demonstrated that this method could generate feasible, high-confidence sequences with low-energy side conformations. The designed sequences can fold into target structures with high confidence and maintain relatively stable biochemical properties. The side chain conformation has a significantly lower energy landscape without delegating to a rotamer library or performing the expensive conformational searches. Overall, we propose an end-to-end method that combines the advantages of both deep learning and energy-based methods. The design results of this model demonstrate high efficiency, and precision, as well as a low energy state and good interpretability.

Post-processing quantitative precipitation forecasts using the seasonally coherent calibration model

ABSTRACT Skilful precipitation ensemble forecasts are necessary to produce trustworthy hydrologic predictions. Raw quantitative precipitation forecasts (QPFs) from the numerical weather prediction (NWP) models are known to be error-prone. In this study, sub-basin averaged deterministic QPFs with five-day lead times from the European Centre for Medium-Range Weather Forecasts (ECMWF) are post-processed through the Seasonally Coherent Calibration (SCC) model for the Narmada and Godavari River basins of India. The SCC model incorporates seasonal climatology from long observations into forecasts and produces calibrated forecasts based on a joint probability model. The SCC model results are compared with the post-processed forecasts from the state-of-the-art Quantile Mapping (QM) method. The results suggest that the probabilistic ensemble forecasts generated from the SCC model have improved skill throughout five-day lead times. Further, the application of SCC-calibrated precipitation forecasts is demonstrated using the Soil & Water Assessment Tool (SWAT) to generate streamflow forecasts.

Scenario derivation and consequence evaluation of dust explosion accident based on dynamic Bayesian network

In order to clarify the correlation between the evolution path of dust explosion accidents and emergency decision-making, and to accurately predict the disaster damage levels of various disaster bearing bodies. This paper extracts 56 key scenario elements from four aspects, namely state, answer, goal and environment, based on the analysis of typical dust explosion accident cases. At the same time, a general scenario evolution path of dust explosion accident is constructed. Using fuzzy number set theory and dynamic Bayes joint probability model, the accurate solution of scenario state probability was realized. With the help of accident cases and dynamic Bayes approach, the dust explosion consequence prediction index system and evaluation criteria were constructed, covering factors such as dust explosion intensity, casualties, direct economic losses, equipment damage, building damage, environmental damage and other factors. A polyethylene wax dust explosion accident in a city of China was used to verify the dust explosion accident scenario evolution model and consequences prediction model. The predicted results were in good agreement with the actual damage of various carriers of the accident, which indicated that the model could be used for dust explosion accident prediction and disaster loss prediction. The research results provided reference and technical support for the prediction of dust explosion accident evolution direction, emergency aid measures decision and deployment, disaster damage prediction and evaluation.

Load condition determination for efficient fatigue analysis of floating offshore wind turbines using a GF-discrepancy-based point selection method

Fatigue failure is one of the decisive factors to be considered in the structural safety design of floating offshore wind turbines (FOWTs). In the fatigue assessment of FOWTs, adequate simulations are required to account for the variability of long-term environmental load conditions. Although the number of potential environmental conditions can be infinite, only a limited number of representative load conditions are used in practice. This inevitably introduces uncertainty into the fatigue estimation, which is usually referred to as the finite sampling-induced uncertainty. To reduce this uncertainty, numerous simulations are required at the expense of unbearable computation burdens. The present study proposes a method to determine the representative load conditions for efficient fatigue evaluation of FOWTs based on the GF-discrepancy of point set. The copula method is adopted to establish the joint probabilistic model of long-term wind and wave conditions. The in-house developed StoDRAFOWT model and the SN-curve approach are employed for the fatigue evaluation. The fatigue uncertainty due to finite sampling under different load condition selection methods is then assessed. The comparison results indicate that the proposed method can significantly reduce the uncertainty in the fatigue estimation of FOWTs against conventional methods, thus improving the fatigue analysis efficiency of FOWTs.