Probabilistic Prediction Model Research Articles

Supervised probabilistic dynamic-controlled latent-variable model for quality pattern prediction and optimisation

A supervised probabilistic dynamic-controlled latent-variable (SPDCLV) model is proposed for online prediction, as well as real-time optimisation of process quality indicators. Compared to existing probabilistic latent-variable models, the key advantage of the proposed method lies in explicitly modelling the dynamic causality from the manipulated inputs to the quality pattern. This is achieved using a well-designed, dynamic-controlled Bayesian network. Furthermore, the algorithms for expectation-maximisation, forward filtering, and backward smoothing are designed for learning the SPDCLV model. For engineering applications, a framework for pattern-based quality prediction and optimisation is proposed, under which the pattern-filtering and pattern-based soft sensor are explored for online quality prediction. Furthermore, quality optimisation can be realised by directly controlling the pattern to the desired condition. Finally, case studies on both an industrial primary milling circuit and a numerical example illustrate the benefits of the SPDCLV method in that it can fully model the process dynamics, effectively predict and optimise the quality indicators, and monitor the process.

A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction

A Probabilistic Runoff Prediction Model Based on Improved Long Short-Term Memory and Interval Correction

Nomograms for prognosis prediction in esophageal adenocarcinoma: realities and challenges.

Prognostic assessment is of great significance for individualized treatment and care of cancer patients. Although the TNM staging system is widely used as the primary prognostic classifier for solid tumors in clinical practice, the complexity of tumor occurrence and development requires more personalized probability prediction models than an ordered staging system. By integrating clinical, pathological, and molecular factors into digital models through LASSO and Cox regression, a nomogram could provide more accurate personalized survival estimates, helping clinicians and patients develop more appropriate treatment and care plans. Esophageal adenocarcinoma (EAC) is a common pathological subtype of esophageal cancer with poor prognosis. Here, we screened and comprehensively reviewed the studies on EAC nomograms for prognostic prediction, focusing on performance evaluation and potential prognostic factors affecting survival. By analyzing the strengths and limitations of the existing nomograms, this study aims to provide assistance in constructing high-quality prognostic models for EAC patients.

Clustered remote sensing target distribution detection aided by density-based spatial analysis

Small target detection in remote sensing is integral to a range of applications, including smart city systems and emergency rescue operations. However, the challenges posed by weak features and complex backgrounds in remote sensing images have hindered the efficacy of detection. Current models tend to focus on identifying individual targets, resulting in algorithms with larger parameters, slower detection efficiency, and difficulty in striking a balance between false positives and negatives. Given that many tasks do not require precise target location, a more efficient approach involves swiftly predicting target areas with models involving fewer parameters. This paper introduces the concept of group target distribution detection, gathering targets with similar distances and semantic similarities for clustered detection. A Gaussian probability map, formed from target density, is used to train a probability prediction model. We propose a new metric for evaluating this innovative group target distribution detection paradigm and provide a comparative assessment with traditional single-object detection models. In experimental evaluation, our proposed DenseUGE network — employing ResNet34 and ResNet50 as its backbone — surpasses the best baseline method by 3.37% on the AI-TOD dataset using our metrics. Additionally, visualizations demonstrate the ability of our proposed methodology to effectively identify the concentrated distribution of small target groups.

Physics-based natural gradient boosting probabilistic prediction model for seismic failure modes of reinforced concrete columns

A novel physics-based natural gradient boosting (PNGB) probabilistic prediction model for flexure failure (FF), flexure-shear failure (FSF) and shear failure (SF) of reinforced concrete (RC) columns under strong earthquakes has been developed to address the limitations of empirical and machine learning (ML) models, which often fail to learn physical laws, insufficiently consider uncertainties, and exhibit poor generalization performance. Firstly, an improved natural gradient boosting (NGB) probabilistic model of seismic failure modes was created by using natural gradient boosting and gradient boosting regressor algorithms. Then a new hyperparameter optimization method for the improved NGB probabilistic model was proposed based on the ML algorithms and the physical laws. Finally, the predictive performance and engineering practicability of the PNGB probabilistic model were verified compared to empirical and ML models. According to the analysis results, the PNGB model can effectively improve the predictive recall of FF, FSF and SF by about 19–65 %, 20–67 % and 20–56 % respectively compared with empirical models, and by about 1–7 %, 7–43 % and 6–32 % respectively compared with ML models. Moreover, the PNGB model exhibits satisfactory generalization performance and minimal dispersion, which can be used to calibrate the ML models based on the confidence intervals. Furthermore, the result predicted by the PNGB model satisfies the physical laws relating to the seismic failure modes and design parameters, which can further represent the competitive relationships of various seismic failure modes.

Probabilistic fatigue life prediction of notched specimens based on modified stress field intensity method under multiaxial loading

In practical engineering components, due to the existence of non-uniform stress and strain field near the notch, it brings severe challenges to fatigue life prediction when evaluating the integrity of notched components. In this study, a probabilistic fatigue life prediction model for notched specimens was established by coupling the stress field intensity (SFI) method and Weibull distribution. Firstly, the position of the dangerous point is determined by finite element calculation, and the maximum strain energy density plane through the dangerous point is defined as the critical plane. Secondly, from the perspective of 2D features, the traditional SFI method is modified based on the stress distribution on the critical plane, and a new concept of effective stress is proposed to predict the fatigue life of notched specimens by the experimental data of smooth specimens. Finally, a new non-proportional additional hardening factor is established to characterize the influence of material properties and loading path on fatigue life. The experimental data of Q345 low alloy steel and GH4169 nickel base alloy are used to compare and analyze the proposed model. The results show that the predicted life of the proposed model is in good agreement with the experimental life.

Probabilistic and explainable tree-based models for rotational reactionary flight delay prediction

Probabilistic and explainable tree-based models for rotational reactionary flight delay prediction

Stochastic optimisation of district integrated energy systems based on a hybrid probability forecasting model

Day-ahead scheduling optimisation plays a significant role in solving stable operation problems of district integrated energy systems (DIESs) by carrying out scheduling optimisation of key equipment of the system based on forecasting data. However, the stable operation of DIESs is affected by multiple uncertainties from source-load. Day-ahead scheduling optimisation method is designed in this study for DIESs based on “prediction-optimisation” to reduce the impact of source-load uncertainty on the stable operation of system. Considering multidimensional source-load data, hybrid probability forecasting model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-Long Short-Term Memory (LSTM)-Gaussian Process Quantile Regression (GPQR) is introduced to quantify the uncertainty using prediction interval. K-means method is used to generate typical scenarios in optimisation step and these scenarios are fed into stochastic optimisation to obtain day-ahead scheduling optimisation results. The experimental results in case study show that hybrid probability prediction model exhibits good prediction performance, while the day-ahead scheduling optimisation based on stochastic optimisation for system's key equipment has the smallest error in typical summer and winter days compared with the real situation, which is 0.018 and 0.0176, respectively. The “prediction-optimisation” approach proposed in this paper shows significant advantages for improving the performance of day-ahead scheduling optimisation of DIESs.

Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle

The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer–Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices.

Data-driven and knowledge-guided denoising diffusion probabilistic model for runoff uncertainty prediction

The existing medium and long-term runoff prediction methods, which are based on data-driven and knowledge-guided methods, are associated with inherent limitations, and chaotic phenomena in runoff prediction models often leads to oscillation in the prediction error, affecting the robustness of the prediction. A knowledge-guided denoising diffusion probabilistic model (DK-RDDPM) that introduces physical theory to guide constraint quantification and obtain effective runoff uncertainty prediction results is therefore proposed in this study. The main advantage of this model is that the physical randomness in the runoff prediction process can be captured and combined with the Saint-Venant process to guide model optimization and realize more accurate medium- and long-term runoff prediction. The main contributions of this study are the establishment of a dynamic runoff probabilistic prediction model with stochastic quantification characteristics that includes the prediction uncertainty over time, and modelling of the physical constraint boundary of runoff prediction from the perspective of partial differentiation. The effectiveness of the DK-RDDPM was verified by predicting runoff in the Qijiang and Tunxi Basins in China. The results show that: 1) Encoding the physical random uncertainty operator in runoff prediction into the network of the denoising diffusion probabilistic model (DDPM) effectively captures the physically complex implicit randomness of the process, thus reducing the error that results from randomness in runoff prediction. 2) The constraint matrix that is formed using the Saint-Venant equation and the prediction matrix are layered and projected, with the fluctuation range of the constraints in each step adjusted in the optimization direction within a certain random threshold range. 3) The DK-RDDPM shows superior performance to the benchmark models, even under the influence of different noise interference factors.

Novel natural gradient boosting-based probabilistic prediction of physical properties for polypropylene-based composite data

Accurately predicting the physical properties of polypropylene composites is challenging because they are highly complex due to the numerous combinations of materials used in their production. Therefore, several challenges must be addressed to develop a predictive model suitable for applied in commercial industry. This study proposes a probabilistic prediction model for the physical properties of polypropylene composites using NGBoost with categorization. The proposed model simultaneously addressed the data imbalance and uncertainty problems using the NGBoost with categorization method. In the data preprocessing step, categorization was used to classify data for each recipe type so that all types of data could be learned. In the model training step, the model performance was improved through hyperparameter optimization. Subsequently, the probabilistic possibility of the predicted value was presented using the probability density function of NGBoost. As a result, the performance of the model had a R2 of 0.9 or more and a normalized root mean square error of 0.1 or less in all four properties. Therefore, the proposed model simultaneously suggests a predicted physical property and its probability with a high performance using real commercial data. In addition, this model is useful for industrial workers because it saves time and cost for the manufacturing and property testing of PPCs.

Probabilistic rutting model using NGBoost and SHAP: Incorporating other performance indicators

This study focuses on accurately predicting rutting depth (RD) through a probabilistic prediction model, which is crucial for timely road maintenance and traffic safety. Natural Gradient Boosting (NGBoost) technology is employed to achieve this goal as it provides accurate predictions and estimates the uncertainty of predictions. Furthermore, to explain the decision-making process of the proposed prediction model, Shapley Additive Explanations (SHAP) analysis is applied to identify and quantify significant factors affecting RD. Research results show that the NGBoost model surpasses the traditional Gaussian Process and Random Forest model regarding prediction accuracy and performs better regarding certainty of prediction intervals. Meanwhile, SHAP analysis improves the transparency of model decision-making and further validates the model. Explanatory ability elucidates the impact of other characteristics on RD. The two-stage probabilistic prediction model not only enhances the reliability of predictions but also provides an in-depth understanding of the key influencing factors of pavement performance, improving the efficiency and effectiveness of maintenance measures.

A probabilistic framework for external pitting corrosion growth modelling for buried steel pipelines considering soil properties

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power-law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect-specific growth models. The framework is applicable regardless of the availability of matched or non-matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where time-dependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

Experimental study on the flame merging and temperature profile produced by two pool fires beneath the curved ceiling

Compared to single-source fires in tunnels, double-source fires may exhibit more intricate flame merging behavior and higher flame burning rates due to their interaction, thereby escalating the risk of fire propagation within the tunnel. In this investigation, a 1:10 scale model of a horseshoe-shaped tunnel test site was deployed to conduct experiments on lateral double-source fires, scrutinizing flame merging behavior, probability prediction models, and temperature characteristics of tunnel ceiling smoke gas by varying the oil pool size and double-source spacing. Findings from the study suggest that flame merging behavior within the tunnel is contingent upon double-source spacing and heat release rate (HRR), manifesting in three distinct forms: complete merging, intermittent merging, and complete separation. Through the introduction of non-dimensional heat release rate and non-dimensional fire source spacing, a piecewise function has been devised to forecast the probability of flame merging for both fire types. Furthermore, a predictive model for the maximum temperature rise along the longitudinal centerline of a horseshoe tunnel was formulated, drawing from established theoretical frameworks.

Probabilistic prediction models of pounding between adjacent buildings during seismic

In recent years, many researchers have studied the probability of collisions between adjacent buildings under earthquake action and proposed some prediction methods, but their accuracy still needs to be improved. This study adopts a machine learning method based on logistic regression models to propose a probability prediction model for adjacent building collisions during earthquake processes. The main content of this study includes: 1. A logistic regression model was used to analysis the factors affecting the seismic impact of adjacent buildings, and an impact prediction model was established; 2. Verify the effectiveness of the logistic regression model through comparative probability analysis.

Diff-MGR: Dynamic causal graph attention and pattern reproduction guided diffusion model for multivariate time series probabilistic forecasting

Time series probability forecasting provides insight into future evolution and its inherent uncertainty from past data. In order to obtain more accurate forecasting as the reliable basis for future decision-making and planning, a new probabilistic prediction model of multivariate time series Diff-MGR is proposed in this study. Diff-MGR innovatively proposes dynamic causal graph attention blocks and pattern reproduction guided prediction blocks to predict the future patterns. Compared with the existing methods, the former captures dynamic unidirectional information flow by using causal graph structure at different periods to provide a real representation of variable relationships. The latter completes a reliable mapping of historical patterns to the future based on the prior knowledge of pattern reproduction. Furthermore, Diff-MGR proposes a novel noise prediction network that can effectively capture dependencies in predicted future patterns and generate probability distributions of future sequences using a conditional diffusion model. Extensive experiments on several real datasets verify the effectiveness of Diff-MGR's components, and show it outperforms existing models in probabilistic forecasting performance.

Proposal of a practical stability probability model for cut slopes reflecting characteristics of weathering and angle of stratification

This research evaluates the stability of cut slopes, considering the effects of weathering and the angle of stratification. Incorporating both a weathering degree index and a factor of safety, we developed a novel formula to assess the temporal deterioration of cut slopes. Utilizing this formula, logistic regression was conducted to ascertain cut slope stability. We used the Hierarchical Bayesian model to refine the parameters of the logistic regression function, culminating in the formulation of a comprehensive slope stability probability prediction model. The model’s predicted stability probabilities for diverse cut slopes exhibited a compelling congruence with empirical data upon validation. Importantly, this approach provides expressway management authorities with a tool to preliminarily assess whether longstanding in-service cut slopes require intervention or stabilization measures.

Decoding the Atmosphere: Optimising Probabilistic Forecasts with Information Gain

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein.

Two-stage short-term wind power probabilistic prediction using natural gradient boosting combined with neural network

Accurate wind power probabilistic prediction reflects the uncertainty information of wind power generation, which is the foundation for optimal scheduling of power systems. This study proposes a two-stage probabilistic prediction model combining natural gradient boosting and neural network for accurate uncertainty estimation of short-term output in a wind farm. In the first stage, the selected input features containing historical and future information are fed into a neural network for representation learning. In the second stage, the extracted abstract features are concatenated with the original features, and a natural gradient boosting model is employed to acquire short-term probabilistic forecasts. The experimental results using data from two real wind farms indicate that the proposed hybrid model can generate accurate, sharp, and reliable forecasts. After performing the successive day-ahead prediction task for a month in the first wind farm, the average root mean square error and mean absolute error of the proposed model in the point prediction were 0.1330 and 0.1070, respectively, which were 6.21%–57.29% and 2.96%–62.03% lower than those of comparative models. In addition, the model’s forecasting probability density curves demonstrate high reliability; its coverage probability and the mean width percentage of the interval prediction results under the 90% confidence level were 0.9094 and 0.3696, respectively, which were more suitable than those of five other probabilistic prediction models.

Leveraging Transformer-Based Non-Parametric Probabilistic Prediction Model for Distributed Energy Storage System Dispatch

In low-voltage distribution networks, distributed energy storage systems (DESSs) are widely used to manage load uncertainty and voltage stability. Accurate modeling and estimation of voltage fluctuations are crucial to informed DESS dispatch decisions. However, existing parametric probabilistic approaches have limitations in handling complex uncertainties, since they always rely on predefined distributions and complex inference processes. To address this, we integrate the patch time series Transformer model with the non-parametric Huberized composite quantile regression method to reliably predict voltage fluctuation without distribution assumptions. Comparative simulations on the IEEE 33-bus distribution network show that the proposed model reduces the DESS dispatch cost by 6.23% compared to state-of-the-art parametric models.