Relevant bibliographies by topics / Prediction models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Prediction models'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Prediction models"

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Ansah, Kwabena, Ismail Wafaa Denwar, and Justice Kwame Appati. "Intelligent Models for Stock Price Prediction." Journal of Information Technology Research 15, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/jitr.298616.

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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price
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Fang, Yiheng. "Prediction of the Ammonia Nitrogen Content with Improved Grey Model by Markov Chain." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 156–61. http://dx.doi.org/10.54097/zee1cd17.

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Water pollution prediction plays a crucial role in environmental protection and sustainable development. This study proposes an innovative approach to enhance the accuracy of water pollution prediction by combining the grey prediction model (GM) with Markov chain analysis. This research focuses on predicting the concentration of ammonia nitrogen (NH3-N) in Dongting Lake, a significant water body. Grey prediction models (GM) are utilized to forecast NH3-N content, addressing the challenge posed by incomplete or insufficient data. However, due to the dynamic nature of water quality indicators, G
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Siemens, Angela, Spencer J. Anderson, S. Rod Rassekh, Colin J. D. Ross, and Bruce C. Carleton. "A Systematic Review of Polygenic Models for Predicting Drug Outcomes." Journal of Personalized Medicine 12, no. 9 (2022): 1394. http://dx.doi.org/10.3390/jpm12091394.

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Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The searc
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Ben Shoham, Ofir, and Nadav Rappoport. "CPLLM: Clinical prediction with large language models." PLOS Digital Health 3, no. 12 (2024): e0000680. https://doi.org/10.1371/journal.pdig.0000680.

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We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. We utilized quantization and fine-tuned the LLM using prompts. For diagnostic predictions, we predicted whether patients would be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical medical records. We compared our results to various baselines, including Retain and Med-BERT, the latter of which is the current state-of-the-art model for diseas
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Ikenna, Ukabuiro, and Stella Agomah. "Prediction Models for Forex Data Exchange System." Prediction Models for Forex Data Exchange System 8, no. 12 (2024): 4. https://doi.org/10.5281/zenodo.10453255.

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Foreign exchange prediction is of important interest to investors and individual traders in financial industries in other to maximize profits and reduces  losses. However owing to some factors and the non- linearity of the FX markets especially in a developing  economy like Nigeria, generating suitable, accurate and appropriate FX predictions becomes difficult for the traders of the market. This study utilized models that include various machine learning algorithm over a trend analysis and pattern of its prediction. The model results on the currency pair of United States(USD) over Ni
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Busari, Ibrahim, Debabrata Sahoo, R. Daren Harmel, and Brian E. Haggard. "A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems." Journal of Natural Resources and Agricultural Ecosystems 1, no. 2 (2023): 63–76. http://dx.doi.org/10.13031/jnrae.15647.

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Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine
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Afshartous, David, and Jan de Leeuw. "Prediction in Multilevel Models." Journal of Educational and Behavioral Statistics 30, no. 2 (2005): 109–39. http://dx.doi.org/10.3102/10769986030002109.

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Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y*j in thej th group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are demonstrated. In addition, the prediction rules are assessed by means of a Monte Carlo study that extensively covers both the sample size and parameter space. Specifically, the sample size space concerns the various combinations of Level 1 (individual) and Level 2
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Kappen, Teus H., and Linda M. Peelen. "Prediction models." Current Opinion in Anaesthesiology 29, no. 6 (2016): 717–26. http://dx.doi.org/10.1097/aco.0000000000000386.

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Lekea, Angella, and Wynand J. vdM Steyn. "Performance of Pavement Temperature Prediction Models." Applied Sciences 13, no. 7 (2023): 4164. http://dx.doi.org/10.3390/app13074164.

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Appropriate asphalt binder selection is dependent on the correct determination of maximum and minimum pavement temperatures. Temperature prediction models have been developed to determine pavement design temperatures. Accordingly, accurate temperature prediction is necessary to ensure the correct design of climate-resilient pavements and for suitable pavement overlay design. Research has shown that the complexity of the model, input variables, geographical location among others affect the accuracy of temperature prediction models. Calibration has also proved to improve the accuracy of the pred
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Geweke, John, and Gianni Amisano. "Prediction with Misspecified Models." American Economic Review 102, no. 3 (2012): 482–86. http://dx.doi.org/10.1257/aer.102.3.482.

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The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard
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Dissertations / Theses on the topic "Prediction models"

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Haider, Peter. "Prediction with Mixture Models." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2014/6961/.

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Learning a model for the relationship between the attributes and the annotated labels of data examples serves two purposes. Firstly, it enables the prediction of the label for examples without annotation. Secondly, the parameters of the model can provide useful insights into the structure of the data. If the data has an inherent partitioned structure, it is natural to mirror this structure in the model. Such mixture models predict by combining the individual predictions generated by the mixture components which correspond to the partitions in the data. Often the partitioned structure is latent
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Vaidyanathan, Sivaranjani. "Bayesian Models for Computer Model Calibration and Prediction." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435527468.

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Charraud, Jocelyn, and Saez Adrian Garcia. "Bankruptcy prediction models on Swedish companies." Thesis, Umeå universitet, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185143.

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Bankruptcies have been a sensitive topic all around the world for over 50 years. From their research, the authors have found that only a few bankruptcy studies have been conducted in Sweden and even less on the topic of bankruptcy prediction models. This thesis investigates the performance of the Altman, Ohlson and Zmijewski bankruptcy prediction models. This research investigates all Swedish companies during the years 2017 and 2018.  This study has the intention to shed light on some of the most famous bankruptcy prediction models. It is interesting to explore the predictive abilities and usa
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Rice, Nigel. "Multivariate prediction models in medicine." Thesis, Keele University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314647.

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Brefeld, Ulf. "Semi-supervised structured prediction models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15748.

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Das Lernen aus strukturierten Eingabe- und Ausgabebeispielen ist die Grundlage für die automatisierte Verarbeitung natürlich auftretender Problemstellungen und eine Herausforderung für das Maschinelle Lernen. Die Einordnung von Objekten in eine Klassentaxonomie, die Eigennamenerkennung und das Parsen natürlicher Sprache sind mögliche Anwendungen. Klassische Verfahren scheitern an der komplexen Natur der Daten, da sie die multiplen Abhängigkeiten und Strukturen nicht erfassen können. Zudem ist die Erhebung von klassifizierten Beispielen in strukturierten Anwendungsgebieten au
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Asterios, Geroukis. "Prediction of Linear Models: Application of Jackknife Model Averaging." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297671.

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When using linear models, a common practice is to find the single best model fit used in predictions. This on the other hand can cause potential problems such as misspecification and sometimes even wrong models due to spurious regression. Another method of predicting models introduced in this study as Jackknife Model Averaging developed by Hansen & Racine (2012). This assigns weights to all possible models one could use and allows the data to have heteroscedastic errors. This model averaging estimator is compared to the Mallows’s Model Averaging (Hansen, 2007) and model selection by Bayesi
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Shrestha, Rakshya. "Deep soil mixing and predictive neural network models for strength prediction." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607735.

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Grant, Stuart William. "Risk prediction models in cardiovascular surgery." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/risk-prediction-models-in-cardiovascular-surgery(1befbc5d-2aa6-4d24-8c32-e635cf55e339).html.

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Objectives: Cardiovascular disease is the leading cause of mortality and morbidity in the developed world. Surgery can improve prognosis and relieve symptoms. Risk prediction models are increasingly being used to inform clinicians and patients about the risks of surgery, to facilitate clinical decision making and for the risk-adjustment of surgical outcome data. The importance of risk prediction models in cardiovascular surgery has been highlighted by the publication of cardiovascular surgery outcome data and the need for risk-adjustment. The overall objective of this thesis is to advance risk
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Jones, Margaret. "Point prediction in survival time models." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340616.

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Monsch, Matthieu (Matthieu Frederic). "Large scale prediction models and algorithms." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84398.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Operations Research Center, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 129-132).<br>Over 90% of the data available across the world has been produced over the last two years, and the trend is increasing. It has therefore become paramount to develop algorithms which are able to scale to very high dimensions. In this thesis we are interested in showing how we can use structural properties of a given problem to come up with models applicable in practice, while keeping most of the value of a l
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Books on the topic "Prediction models"

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Steyerberg, E. W. Clinical Prediction Models. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-77244-8.

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Steyerberg, Ewout W. Clinical Prediction Models. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16399-0.

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Auerbach, Jonathan Lyle. Some Statistical Models for Prediction. [publisher not identified], 2020.

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R, Wilcock Peter, and Iverson Richard Matthew, eds. Prediction in geomorphology. American Geophysical Union, 2002.

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R, Wilcock Peter, Iverson Richard Matthew, AGU Fall Meeting, and American Geophysical Union, eds. Prediction in geomorphology. American Geophysical Union, 2003.

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Rick, Archer, and U.S. Army Research Institute for the Behavioral and Social Sciences., eds. Improving soldier factors in prediction models. U.S. Army Research Institute for the Behavioral and Social Sciences, 2002.

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Building and Fire Research Laboratory (U.S.) and Factory Mutual Research Corporation, eds. Prediction of fire dynamics. The Institute, 1997.

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Shailer, G. E. P. Experimental failure prediction models for small companies. Dept. of Economics and Industrial Economics, 1986.

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1952-, Hadorn David C., United States. Health Care Financing Administration., and Rand/UCLA/Harvard Center for Health Care Financing Policy Research., eds. Assessing the performance of mortality prediction models. RAND, 1993.

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Folwell, Raymond J. Price prediction models for Washington fresh asparagus. Washington State University, College of Agriculture and Home Economics Research Center, 1995.

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Book chapters on the topic "Prediction models"

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Bacmeister, Julio T. "Weather Prediction Models weather prediction model." In Encyclopedia of Sustainability Science and Technology. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_362.

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Deistler, Manfred, and Wolfgang Scherrer. "Prediction." In Time Series Models. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13213-1_2.

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Lefebvre, Cedric W., Jay P. Babich, James H. Grendell, et al. "Prediction Models." In Encyclopedia of Intensive Care Medicine. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-00418-6_2077.

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Hofmann, Ulrich. "Prediction Models." In Internet Modeling with Julia. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-44692-5_7.

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Kuttruff, Heinrich, and Michael Vorländer. "Prediction models." In Room Acoustics, 7th ed. CRC Press, 2024. http://dx.doi.org/10.1201/9781003389873-10.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.

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AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or
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Faraway, Julian J. "Prediction." In Linear Models with Python, 10th ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351053419-4.

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Faraway, Julian J. "Prediction." In Linear Models with R, 3rd ed. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003449973-5.

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den Brinker, Albertus C., and Harm J. W. Belt. "Using Kautz Models in Model Reduction." In Signal Analysis and Prediction. Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_13.

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Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.

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AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that proces
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Conference papers on the topic "Prediction models"

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Santana, Vinicius V., Carine M. Rebello, Erbet A. Costa, et al. "Recurrent Deep Learning Models for Multi-step Ahead Prediction: Comparison and Evaluation for Real Electrical Submersible Pump (ESP) System." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.107762.

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Predicting processes� future behavior based on past data is vital for automatic control and dynamic optimization in engineering. Recent advances in deep learning, particularly Artificial Neural Networks, have improved predictions in various engineering fields. Recurrent Neural Networks (RNNs) are well-suited for time series data, as they naturally evolve through dynamic systems with recurrent updates. Despite their high predictive power, RNNs may underperform if their training ignores the model's future application. In Model Predictive Control, for example, the model evolves over time using on
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Zahan, Nusrat, Sidike Paheding, Noha Ismail, and Thomas Oommen. "Synthetic data augmentation with generative models for improved classification of mine tailings impoundments." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3053654.

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Jadoon, Usman Khan, Ismael D�az, and Manuel Rodr�guez. "A Comparative Study of Aspen Plus and Machine Learning Models for Syngas Prediction in Biomass-Plastic Waste Co-gasification." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.174749.

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The co-gasification of biomass and plastic waste offers a promising pathway for sustainable syngas production, necessitating precise prediction of its composition to optimize efficiency. This study compares the performance of Aspen Plus models, including the thermodynamic equilibrium model (TEM) and restricted thermodynamic equilibrium model (RTM), with machine learning (ML) techniques, focusing on the support vector regression (SVR) for syngas prediction during steam and air co-gasification. Aspen Plus simulations provided valuable mechanistic insights, while the ML model demonstrated superio
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Sharma, Madhuri, Seema Bushra, Abdul Wadood Siddiqui, Puja Kumari, R. Dhanusha, and Deepak Jain. "Futuristic Health Prediction Models." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911699.

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Lee, Ju-Hyung, Joohan Lee, and Andreas F. Molisch. "Generative vs. Predictive Models in Massive MIMO Channel Prediction." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10943090.

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Jackson, Ryan, Michael Jump, and Peter Green. "Towards Gaussian Process Models of Complex Rotorcraft Dynamics." In Vertical Flight Society 74th Annual Forum & Technology Display. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12828.

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Physical law based models (also known as white box models) are widely applied in the aerospace industry, providing models for dynamic systems such as helicopter flight simulators. To meet the criteria of real-time simulation, simplifications to the underlying physics sometimes have to be applied, leading to errors in the model's predictions. Grey-box models use both physics-based and data-based models. They have potential to reduce the difference between a simulator's and real rotorcraft's response. In the current work, a preliminary step to the grey-box approach, a machine learnt data-based,
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Nunkesser, Robin. "Highly Interpretable Prediction Models for SNP Data." In 16th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013137600003911.

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Zou, Qiaosha, and Yuan Xie. "Compact Models and Model Standard for 2.5D and 3D Integration." In SLIP (System Level Interconnect Prediction). ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633955.

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Ben-Haim, Yakov, and Franc¸ois M. Hemez. "Robustness, Fidelity and Prediction-Looseness of Models." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58008.

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Assessment of the credibility of a mathematical or numerical model of an engineering system must combine three components: (1) The fidelity of the model to test data. (2) The robustness, of model fidelity, to lack of understanding of the underlying processes. (3) The prediction looseness of the model. ‘Prediction looseness’ is the range of predictions of models which are equivalent in terms of fidelity. The main result of this paper is that high fidelity, high robustness, and small prediction looseness are mutually incompatible. A model with high fidelity to data and high robustness to imperfe
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Brockhoff, Tobias, Malte Heithoff, Istvan Koren, et al. "Process Prediction with Digital Twins." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00032.

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Reports on the topic "Prediction models"

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successfu
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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental
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Holzenthal, Elizabeth, and Bradley Johnson. Comparison of run-up models with field data. Engineer Research and Development Center (U.S.), 2024. https://doi.org/10.21079/11681/49470.

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Run-up predictions are inherently uncertain, owing to ambiguities in phase-averaged models and inherent complexities of surf and swash-zone hydrodynamics. As a result, different approaches, ranging from simple algebraic expressions to computationally intensive phase-resolving models, have been used in attempt to capture the most relevant run-up processes. Studies quantifiably comparing these methods in terms of physical accuracy and computational speed are needed as new observation technologies and models become available. The current study tests the capability of the new swash formulation of
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Srikant, Rayadurgam, and Bruce Hajek. Reduced-Complexity Models for Network Performance Prediction. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada435841.

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Chung, C. F., and J. M. Shaw. Quantitative prediction models for landslide hazard assessment. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/210202.

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Murphy, D. D., W. M. Thomas, W. M. Evanco, and W. W. Agresti. Procedures for Applying Ada Quality Prediction Models. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada264730.

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Iskandarani, Mohamed, Omar Knio, Ashwanth Srinivasan, and William C. Thacker. Quantifying Prediction Fidelity in Ocean Circulation Models. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada590693.

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Iskandarani, Mohamed, Omar Knio, Ashwanth Srinivasan, and William C. Thacker. Quantifying Prediction Fidelity in Ocean Circulation Models. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada601423.

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10

Wang, Yong-Yi, and Jiawei Wang. PR350-233804-R01 Comprehensive Review of SSWC Assessment. Pipeline Research Council International, Inc. (PRCI), 2025. https://doi.org/10.55274/r0000118.

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Abstract:
Selective seam weld corrosion (SSWC) is a form of corrosion attack that preferentially occurs along the weld bond line/fusion zone of linepipes. SSWC is an integrity threat mainly for vintage pipes, particularly those manufactured before 1970 using ERW (DC-ERW and LF-ERW) and flash welding. SSWC has resulted in multiple pipeline failures. Assessing the significance of SSWC, e.g., producing a reasonably accurate prediction of burst pressure of a pipeline segment containing SSWC, remains a challenge for the pipeline industry. This project consists of four major parts: - Review prior work on burs
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