Relevant bibliographies by topics / "decision tree" model

Contents

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

Academic literature on the topic '"decision tree" model'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic ""decision tree" model"

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Guidotti, Riccardo, Anna Monreale, Mattia Setzu, and Giulia Volpi. "Generative Model for Decision Trees." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21116–24. http://dx.doi.org/10.1609/aaai.v38i19.30104.

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Decision trees are among the most popular supervised models due to their interpretability and knowledge representation resembling human reasoning. Commonly-used decision tree induction algorithms are based on greedy top-down strategies. Although these approaches are known to be an efficient heuristic, the resulting trees are only locally optimal and tend to have overly complex structures. On the other hand, optimal decision tree algorithms attempt to create an entire decision tree at once to achieve global optimality. We place our proposal between these approaches by designing a generative mod
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Naveen Kumar, Nallamothu. "Model of Decision Tree for Email Classification." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1502–5. http://dx.doi.org/10.21275/sr22722110223.

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Muhammad Sani, Anas, Ahmad Salihu BenMusa, and Muhammad Haladu. "In-Depth Study of Decision Tree Model." International Journal of Science and Research (IJSR) 10, no. 11 (2021): 705–9. https://doi.org/10.21275/mr211102051237.

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Kang, Donggil, WenXing Yu, and HyungJun Cho. "Decision Tree for Mode Estimation." Korean Data Analysis Society 25, no. 3 (2023): 903–11. http://dx.doi.org/10.37727/jkdas.2023.25.3.903.

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Decision trees are one of the data mining techniques that make predictions by recursively partitioning data structures based on split rules. Since the analysis results can be understood through the tree structure, it has the advantage of having high interpretation power as well as predictive power. In addition, it is used in many fields because it is able to identify nonlinear relationships between response and predictor variables. However, if the purpose of it is to predict the mode of the response variable, there is a limitation in that the previously proposed decision tree cannot be applied
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Tsehay, Admassu Assegie, Kumar Napa Komal, Thulasi Thiyagu, Kalyan Kumar Angati, Jeyanthiran Thiruvarasu Vasantha Priya Maran, and Dhamodaran Vigneswari. "Scalability and performance of decision tree for cardiovascular disease prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2540–45. https://doi.org/10.11591/ijai.v13.i3.pp2540-2545.

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As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The study evaluated the perf
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Pathan, Shabana, and Sanjeev Kumar Sharma. "Design an Optimal Decision Tree based Algorithm to Improve Model Prediction Performance." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (2023): 127–33. http://dx.doi.org/10.17762/ijritcc.v11i6.7295.

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Performance of decision trees is assessed by prediction accuracy for unobserved occurrences. In order to generate optimised decision trees with high classification accuracy and smaller decision trees, this study will pre-process the data. In this study, some decision tree components are addressed and enhanced. The algorithms should produce precise and ideal decision trees in order to increase prediction performance. Additionally, it hopes to create a decision tree algorithm with a tiny global footprint and excellent forecast accuracy. The typical decision tree-based technique was created for c
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Jeon, Youtaek, and HyungJun Cho. "Model based hybrid decision tree." Journal of the Korean Data And Information Science Society 30, no. 3 (2019): 515–24. http://dx.doi.org/10.7465/jkdi.2019.30.3.515.

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Li, Jiawei, Yiming Li, Xingchun Xiang, Shu-Tao Xia, Siyi Dong, and Yun Cai. "TNT: An Interpretable Tree-Network-Tree Learning Framework using Knowledge Distillation." Entropy 22, no. 11 (2020): 1203. http://dx.doi.org/10.3390/e22111203.

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Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and decision-making. In this paper, we propose a Tree-Network-Tree (TNT) learning framework for explainable decision-making, where the knowledge is alternately transferred between the tree model and DNNs. Specifically, the proposed TNT learning framework exerts the advantages of different models at differ
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Susdarwono, Endro Tri, and Ananda Setiawan. "PENERAPAN TEORI KEPUTUSAN DALAM MODEL PENGAMBILAN KEPUTUSAN TERKAIT MASALAH EKONOMI PERTAHANAN." Jurnal Ilmu Manajemen dan Akuntansi Terapan (JIMAT) 11, no. 2 (2020): 258–70. http://dx.doi.org/10.36694/jimat.v11i2.239.

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The shift in global paradigm and threat perspective has led to a wide variety of possible risks and uncertainties. This situation also occurs in the defense economy, so understanding the basic principles of risk and uncertainty is important, especially in a decision-making process. There are several elements and concepts that are usually used in all decision models. Almost all models, whether complex or simple, can be formulated using a standard structure and solved by using general evaluation procedures. For decisions involving a series of decisions and relating to various basic sequential co
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Dobashi, Nao, Shota Saito, Yuta Nakahara, and Toshiyasu Matsushima. "Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction." Entropy 23, no. 6 (2021): 768. http://dx.doi.org/10.3390/e23060768.

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This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an explanatory variable, and discuss statistical optimality for prediction based on the Bayes decision theory. The optimal prediction based on the Bayes decision theory is given by weighting all the model trees in the model tree candidate set, where the model tree candidate set is a set of model trees
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Dissertations / Theses on the topic ""decision tree" model"

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Kyselý, Ondřej. "Alokační model projektu Miss Sport." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-75197.

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The goal of the diploma thesis is to create allocation model for Miss Sport project. This project is a platform, which allows effective association of sponsors and female athletes, who are members of the project. It results in decision tree, whose biggest advantage is in transparency and rate of decision making. One of the objectives is to analyze most important criteria, which are necessary to segment female athletes. One part is a list of aspects, which are important to sponsorship, but they are not included in allocation model directly. Research target is focused on evaluation of attractive
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Monjaras, Alvaro, Enrique Bcndezu, and Carlos Raymundo. "Decision Tree Model to Support the Successful Selection of a Database Engine for Novice Database Administrators." Institute of Electrical and Electronics Engineers Inc, 2019. http://hdl.handle.net/10757/656346.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>There are currently several types of databases that have different ways of manipulating data that affects the performance of transactions when dealing with the information stored. And it is very important for companies to manage information fast, so they do not lose any operation because of a bad performance of a database, in the same way, they need to operate fast while keeping the integrity of the information. Likewise, every database categor
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Liu, Liang. "Reconstructing posterior distributions of a species phylogeny using estimated gene tree distributions." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155754980.

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Gálvez, Chambilla Melissa Beatriz, and Cornejo Katherine Briggite Flores. "Modelo predictivo de deserción universitaria de la carrera de Ingeniería Informática en la Universidad Ricardo Palma." Bachelor's thesis, Universidad Ricardo Palma, 2015. http://cybertesis.urp.edu.pe/handle/urp/1272.

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La deserción universitaria se ha convertido en un problema importante a ser investigado. La tasa de deserción ha llegado a constituir uno de los principales indicadores de eficiencia interna dentro de cualquier institución de educación. Investigar las causas de la deserción con metodologías adecuadas que permitan predecir esta, contribuye a la toma de decisiones dentro de la gestión de la unidad académica. El objetivo del presente proyecto consiste en investigar y proponer una metodología que permita identificar en forma automática a los estudiantes con mayor riesgo de deserción de las carrer
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ANSELMI, ANDREA. "Un sistema di monitoraggio e di supporto alle decisioni per la gestione sostenibile dell'oliveto." Doctoral thesis, Università Cattolica del Sacro Cuore, 2023. http://hdl.handle.net/10280/135601.

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L'obiettivo della mia ricerca è stato quello di sviluppare modelli previsionali delle principali parassiti dell’olivo. Sempre nel settore della difesa antiparassitaria di questa coltura, sono emerse inoltre pesanti criticità legate alla pratica applicazione dei trattamenti fitosanitari, oltre che per le grandi dimensioni delle piante e la difficile accessibilità che caratterizzano moltissimi oliveti, anche per la carente regolazione funzionale delle irroratrici e per il poco diffuso loro controllo strumentale. Pertanto è stato sviluppato anche un sistema di supporto alla taratura degli atomizz
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Wan, Hao. "Tutoring Students with Adaptive Strategies." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/36.

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Adaptive learning is a crucial part in intelligent tutoring systems. It provides students with appropriate tutoring interventions, based on students’ characteristics, status, and other related features, in order to optimize their learning outcomes. It is required to determine students’ knowledge level or learning progress, based on which it then uses proper techniques to choose the optimal interventions. In this dissertation work, I focus on these aspects related to the process in adaptive learning: student modeling, k-armed bandits, and contextual bandits. Student modeling. The main o
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Araya, Yeheyies. "Detecting Swiching Points and Mode of Transport from GPS Tracks." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-91320.

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In recent years, various researches are under progress to enhance the quality of the travel survey. These researches were mainly performed with the aid of GPS technology. Initially the researches were mainly focused on the vehicle travel mode due to the availability of GPS technology in vehicle. But, nowadays due to the accessible of GPS devices for personal uses, researchers have diverted their focus on personal mobility in all travel modes. This master’s thesis aimed at developing a mechanism to extract one type of travel survey information particularly travel mode from collected GPS dataset
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Chida, Anjum A. "Protein Tertiary Model Assessment Using Granular Machine Learning Techniques." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/65.

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The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same
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James, Kyle. "DNA-MAP, a knowledge-based decision support system for Australian Defence Force forensic ancestry prediction." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213211/1/Kyle_James_Thesis.pdf.

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Development of a Knowledge-Based Decision Support System to predict ancestry of the remains of missing World War Two soldiers in South-East Asia. By utilizing biological and historical information provided by the user, ancestry is assigned based on complex statistical analyses searching for distinctive patterns in the DNA that distinguish between the Australian and Japanese populations. Important features taken into consideration are the detection of a rare event, the effect of sample size and the impact of natural variation.
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Lengál, Ondřej. "Efektivní knihovna pro práci s konečnými stromovými automaty." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237232.

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Numerous computer systems use dynamic control and data structures of unbounded size. These data structures have often the character of trees or they can be encoded as trees with some additional pointers. This is exploited by some currently intensively studied techniques of formal verification that represent an infinite number of states using a finite tree automaton. However, currently there is no tree automata library implementation that would provide an efficient and flexible support for such methods. Thus the aim of this Mas- ter's Thesis is to provide such a library. The present paper first
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Books on the topic ""decision tree" model"

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Drechsler, Rolf. Binary decision diagrams: Theory and implementation. Kluwer Academic Publishers, 1998.

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ADT 2011 (2011 Piscataway, N.J.). Algorithmic decision theory: Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011 : proceedings. Springer, 2011.

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Paarsch, Harry J. Deciding between the common and private value paradigms in empirical models of auctions. Forest Economics and Policy Analysis Research Unit, University of British Columbia, 1989.

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Bessonov, Aleksey. The study of criminal activity using artificial intelligence. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2195488.

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The monograph describes the technology of building digital crime models, including the preparation of data on criminal acts for study using mathematical statistics and artificial intelligence methods, the features of studying such data through various artificial intelligence methods, including neural networks, gradient boosting, decision trees, random forest, clustering, etc. Special attention is paid to the use of mathematical statistics and artificial intelligence methods in the study of serial crimes in science and practice. It is intended for scientists and practitioners of law enforcement
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Randall, William D. Software reusability: A decision tree model. 1988.

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Improved Decision-making in Data Mining: A Heuristic Rule Induction Approach to Decision Tree Creation and Model Selection. VDM Verlag Dr. Müller, 2008.

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Boeira, Fernando A. Decision Models for Business: Decision Trees. Blurb, 2015.

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Kim, Hyunsoo. PAC learning: A decision tree with pruning. 1992.

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Chikalov, Igor. Average Time Complexity of Decision Trees. Springer, 2013.

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Chikalov, Igor. Average Time Complexity of Decision Trees. Springer, 2012.

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Book chapters on the topic ""decision tree" model"

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Karabadji, Nour El Islem, Hassina Seridi, Ilyes Khelf, and Lakhdar Laouar. "Decision Tree Selection in an Industrial Machine Fault Diagnostics." In Model and Data Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33609-6_13.

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Lee, Cheng-Few, John Lee, Jow-Ran Chang, and Tzu Tai. "Binomial Option Pricing Model Decision Tree Approach." In Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38867-0_25.

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Edlin, Richard, Christopher McCabe, Claire Hulme, Peter Hall, and Judy Wright. "Building a Decision Tree Cost Effectiveness Model." In Cost Effectiveness Modelling for Health Technology Assessment. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15744-3_3.

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Lee, John, Jow-Ran Chang, Lie-Jane Kao, and Cheng-Few Lee. "Binomial Option Pricing Model Decision Tree Approach." In Essentials of Excel VBA, Python, and R. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-14283-3_5.

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Han, Jianchao, Juan C. Rodriguez, and Mohsen Beheshti. "Discovering Decision Tree Based Diabetes Prediction Model." In Advances in Software Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10242-4_9.

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Wahiba, Ben Abdessalam, and Ben El Fadhl Ahmed. "New Fuzzy Decision Tree Model for Text Classification." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26690-9_28.

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Minz, Sonajharia, and Rajni Jain. "Rough Set Based Decision Tree Model for Classification." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45228-7_18.

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D. M., Basavarajaiah, and Bhamidipati Narasimha Murthy. "Random Forest and Concept of Decision Tree Model." In Design of Experiments and Advanced Statistical Techniques in Clinical Research. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8210-3_3.

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Mandale, Samuel Kumbu, and Bernard Shibwabo Kasamani. "A Decision Tree-Based Model for Tender Evaluation." In Innovations in Smart Cities Applications Volume 5. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94191-8_10.

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Uko, Unyime Emmanuel, and Yi Wang. "Telemedicine in Healthcare Management; A Decision-Tree Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2625-0_49.

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Conference papers on the topic ""decision tree" model"

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Gu, Bill, Richard Kania, Ming Gao, and Wayne Feil. "Development of SCC Susceptibility Model Using Decision Tree Approach." In CORROSION 2005. NACE International, 2005. https://doi.org/10.5006/c2005-05479.

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Abstract Stress corrosion cracking (SCC) on pipelines has been extensively studied over the past three to four decades. Various models have been developed to predict where and how fast SCC occurs on pipelines. However, due to the complexity of SCC, no general models are currently available to accurately predict SCC on pipelines. Models developed based on operating experience for one geographic location has often performed poorly in another region. For example, the SCC soils model developed in the past predicts that low-pH SCC will occur in poorly drained, anaerobic soils; however, in the same
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Lynh, Duong Huyen, and Tran Hai Nam. "Empowering Die Selection in V-Bending: Insights from Decision Tree Algorithms." In 2024 International Conference on Machining, Materials and Mechanical Technologies. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-epv4bq.

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This study presents the application of tree-based algorithms to predict springback in the V-bending process of sheet metals, particularly for SUS304 material. V-bending, a critical process in metal forming, often faces challenges due to springback, which affects dimensional accuracy and product quality. Using virtual experiments conducted via ANSYS software, the study evaluates the influence of variables such as die angle, die radius, material thickness, and punch displacement on springback. Four tree-based algorithms—Decision Trees, Random Forest, Gradient Boosting Machines (GBM), and Extra T
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Zhong, Guoan, Kecheng Xiang, and Qianqun Mo. "Weather Forecast Model Based on Decision Tree and Neural Network." In 2024 4th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS). IEEE, 2024. http://dx.doi.org/10.1109/acctcs61748.2024.00096.

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Yu, Fengyi, Zhen Liu, Siyuan Ma, and Zhenyu Xu. "Time Series Data Prediction based on the Decision Tree Model." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796171.

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Ouacifi, Malika, Karim Ferroudji, Fouad Chebbara, Mounir Amir, Mohamed Lashab, and Raed A. Abd-Alhameed. "A Decision Tree Model for Inset-Fed Microstrip Patch Antenna." In 2024 1st International Conference on Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech). IEEE, 2024. https://doi.org/10.1109/ecte-tech62477.2024.10851155.

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Chi, Meiqi. "Optimization of Prediction Model Based on Gradient Boosting Decision Tree." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10860220.

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Wang, Luheng, Mingyang Chen, Zhenxiang Zhao, and Liye Zhang. "Detection of Mine Precursor Signals Based on Decision Tree Model." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898480.

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Zadem, Amira, Samia Chettouh, Saadia Saadi, and Hamida Debz. "Prediction of Employees’ Burnout Level Using a Decision Tree Model." In 2024 International Conference of the African Federation of Operational Research Societies (AFROS). IEEE, 2024. https://doi.org/10.1109/afros62115.2024.11037209.

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Cavor, Ivana. "Decision Tree Model for Email Classification." In 2021 25th International Conference on Information Technology (IT). IEEE, 2021. http://dx.doi.org/10.1109/it51528.2021.9390143.

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Abo-Tabik, Maryam, Nicholas Costen, John Darby, and Yael Benn. "Decision Tree Model of Smoking Behaviour." In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2019. http://dx.doi.org/10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00311.

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Reports on the topic ""decision tree" model"

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Kwon, Theresa Hyunjin, Erin Cho, and Youn-Kyung Kim. Identifying Sustainable Style Consumers with Decision Tree Predictive Model. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1366.

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Song, So Young, Erin Cho, Youn-Kyung Kim, and Theresa Hyunjin Kwon. Clothing Communication via Social Media: A Decision Tree Predictive Model. Iowa State University, Digital Repository, 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-102.

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Zaman, Md Mostafa, Theresa Hyunjin Kwon, Katrina Laemmerhirt, and Youn-Kyung Kim. Profiling Second-hand Clothing Shoppers with Decision Tree Predictive Model. Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-407.

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van den Berg, F., A. Tiktak, T. Hoogland, et al. An improved soil organic matter map for GeoPEARL_NL : Model description of version 4.4.4 and consequence for the Dutch decision tree on leaching to groundwater. Wageningen Environmental Research, 2017. http://dx.doi.org/10.18174/424920.

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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accident
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Giovando, Jeremy, Wyatt Reis, Rose Shillito, et al. Post-wildfire curve number estimates for the Southern Rocky Mountains in Colorado, USA. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48652.

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The curve number method first developed by the US Department of Agriculture Soil Conservation Service (now the Natural Resources Conservation Service) is often used for post-wildfire runoff assessments. These assessments are critical for land and emergency managers making decisions on life and property risks following a wildfire event. Three approaches (i.e., historical event observations, linear regression model, and regression tree model) were used to help estimate a post-wildfire curve number from watershed and wildfire parameters. For the first method, we used runoff events from 102 burned
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Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

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This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.
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Ayers, Dotson, and Alexander. L52332 Offshore Pipeline Damage Emergency Response Guidelines. Pipeline Research Council International, Inc. (PRCI), 2012. http://dx.doi.org/10.55274/r0010016.

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Subsea pipelines and flow lines are periodically subjected to damaging events such as anchor impacts that result in massive pipeline movements, dropped object damage, internal/external corrosion damage, etc. Knowing how to assess these damage events is often challenging, especially considering the potential for product release. The cost of production shut-ins can be significant and avoiding un-necessary shut-ins is desirable. While most pipeline operators have company-level procedures and programs in place for responding to pipeline emergencies, at the current time there is no single resource
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 sam
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Letcher, Theodore, Kent Sparrow, and Sandra LeGrand. Establishing a series of dust event case studies for East Asia. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47824.

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Dust aerosols have a wide range of effects on air quality, health, land-management decisions, aircraft operations, and sensor data interpretations. Therefore, the accurate simulation of dust plume initiation and transport is a priority for operational weather centers. Recent advancements have improved the performance of dust prediction models, but substantial capability gaps remain when forecasting the specific location and timing of individual dust events, especially extreme dust outbreaks. Operational weather forecasters and US Army Engineer Research and Development Center (ERDC) researchers
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