Journal articles: 'Predictive modelling'

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Riemann, Hans P., and K. R. Davey. "Predictive modelling." Letters in Applied Microbiology 14, no. 4 (April 1992): 127–28. http://dx.doi.org/10.1111/j.1472-765x.1992.tb00666.x.

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Farkas, J. "Predictive modelling." International Journal of Food Microbiology 23, no. 3-4 (November 1994): v. http://dx.doi.org/10.1016/0168-1605(94)90154-6.

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Savage, Neil. "Modelling: Predictive yield." Nature 501, no. 7468 (September 2013): S10—S11. http://dx.doi.org/10.1038/501s10a.

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Vora, Deepali, and Kamatchi Iyer. "Evaluating the Effectiveness of Machine Learning Algorithms in Predictive Modelling." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 197. http://dx.doi.org/10.14419/ijet.v7i3.4.16773.

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Predictive modelling is a statistical technique to predict future behaviour. Machine learning is one of the most popular methods for predicting the future behaviour. From the plethora of algorithms available it is always interesting to find out which algorithm or technique is most suitable for data under consideration. Educational Data Mining is the area of research where predictive modelling is most useful. Predicting the grades of the undergraduate students accurately can help students as well as educators in many ways. Early prediction can help motivating students in better ways to select their future endeavour. This paper presents the results of various machine learning algorithms applied to the data collected from undergraduate studies. It evaluates the effectiveness of various machine learning algorithms when applied to data collected from undergraduate studies. Two major challenges are addressed as: choosing the right features and choosing the right algorithm for prediction.

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Lehman, John T., Lars Hakanson, and Robert H. Peters. "Predictive Limnology: Methods for Predictive Modelling." Ecology 78, no. 1 (January 1997): 326. http://dx.doi.org/10.2307/2266003.

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Oden, J. Tinsley. "Adaptive multiscale predictive modelling." Acta Numerica 27 (May 1, 2018): 353–450. http://dx.doi.org/10.1017/s096249291800003x.

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The use of computational models and simulations to predict events that take place in our physical universe, or to predict the behaviour of engineered systems, has significantly advanced the pace of scientific discovery and the creation of new technologies for the benefit of humankind over recent decades, at least up to a point. That ‘point’ in recent history occurred around the time that the scientific community began to realize that true predictive science must deal with many formidable obstacles, including the determination of the reliability of the models in the presence of many uncertainties. To develop meaningful predictions one needs relevant data, itself possessing uncertainty due to experimental noise; in addition, one must determine model parameters, and concomitantly, there is the overriding need to select and validate models given the data and the goals of the simulation.This article provides a broad overview of predictive computational science within the framework of what is often called the science of uncertainty quantification. The exposition is divided into three major parts. In Part 1, philosophical and statistical foundations of predictive science are developed within a Bayesian framework. There the case is made that the Bayesian framework provides, perhaps, a unique setting for handling all of the uncertainties encountered in scientific prediction. In Part 2, general frameworks and procedures for the calculation and validation of mathematical models of physical realities are given, all in a Bayesian setting. But beyond Bayes, an introduction to information theory, the maximum entropy principle, model sensitivity analysis and sampling methods such as MCMC are presented. In Part 3, the central problem of predictive computational science is addressed: the selection, adaptive control and validation of mathematical and computational models of complex systems. The Occam Plausibility Algorithm, OPAL, is introduced as a framework for model selection, calibration and validation. Applications to complex models of tumour growth are discussed.

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Peppler-Lisbach, Cord. "Predictive modelling of historical and recent land-use patterns." Phytocoenologia 33, no. 4 (November 19, 2003): 565–90. http://dx.doi.org/10.1127/0340-269x/2003/0033-0565.

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Rossiter, J. A., and B. Kouvaritakis. "Modelling and implicit modelling for predictive control." International Journal of Control 74, no. 11 (January 2001): 1085–95. http://dx.doi.org/10.1080/00207170110054129.

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KAWAI, Kimio, Anthony BEAUCAMP, Noriyuki IMAIZUMI, Masatoshi SAKURAI, and Yoshimi TAKEUCHI. "1902 Modelling of Drill Shapes by a Novel Predictive System." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _1902–1_—_1902–4_. http://dx.doi.org/10.1299/jsmelem.2015.8._1902-1_.

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Ma, Jungmok. "Data driven TRL Transition Predictions for Early Technology Development in Defence." Defence Science Journal 71, no. 6 (October 22, 2021): 777–83. http://dx.doi.org/10.14429/dsj.71.16771.

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This paper proposes the framework of TRL (Technology Readiness Level) transition predictions for early technology development in defense. Though predicting future TRLs is an important planning tool, it has been studied less actively than the other critical issues on TRL, and previous studies mostly have resorted to domain experts. The proposed framework is data-driven and utilises both explanatory and predictive modelling techniques. As a case study, the proposed framework is applied to real technology development data from DTiMS (Defense Technology InforMation Service) which is identified as a key resource. The result of explanatory modelling shows that the two predictor variables, TRL before R&D and project cost, are statistically significant for future TRLs. Also, popular predictive models are fitted and compared with various performance indices using 10-fold cross validation. The two selected predictive models are linear regression and support vector machine models with the lowest prediction errors.

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Huang, H. "Predictive modelling of nanorods synthesis." Journal of Physics: Conference Series 107 (March 1, 2008): 012006. http://dx.doi.org/10.1088/1742-6596/107/1/012006.

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Hedges, A., and M. Cole. "Predictive modelling—or is it?" Letters in Applied Microbiology 13, no. 5 (November 1991): 217–19. http://dx.doi.org/10.1111/j.1472-765x.1991.tb00612.x.

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Cawley, Gavin C., Gareth J. Janacek, Malcolm R. Haylock, and Stephen R. Dorling. "Predictive uncertainty in environmental modelling." Neural Networks 20, no. 4 (May 2007): 537–49. http://dx.doi.org/10.1016/j.neunet.2007.04.024.

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Lombardo, Luigi, Thomas Opitz, Francesca Ardizzone, Fausto Guzzetti, and Raphaël Huser. "Space-time landslide predictive modelling." Earth-Science Reviews 209 (October 2020): 103318. http://dx.doi.org/10.1016/j.earscirev.2020.103318.

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Cullis, I., M. Hinton, S. Gilbert, P. Church, D. Porter, T. Andrews, W. Proud, and A. Pullen. "Towards predictive modelling for concrete." International Journal of Impact Engineering 35, no. 12 (December 2008): 1478–83. http://dx.doi.org/10.1016/j.ijimpeng.2008.07.050.

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Richards, A. B. "Predictive modelling of airblast overpressure." Mining Technology 122, no. 4 (December 2013): 215–20. http://dx.doi.org/10.1179/147490013x13639459465619.

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C., Burton, Rosenbaum M., and Stevens R. "Sedimentological considerations for predictive modelling." Bulletin of Engineering Geology and the Environment 61, no. 2 (May 1, 2002): 129–36. http://dx.doi.org/10.1007/s100640100128.

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Nik Nurul Hafzan, Mat Yaacob, Deris Safaai, Mat Asiah, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment." MATEC Web of Conferences 255 (2019): 03002. http://dx.doi.org/10.1051/matecconf/201925503002.

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Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events. Higher education institutions nowadays are under increasing pressure to respond to national and global economic, political and social changes such as the growing need to increase the proportion of students in certain disciplines, embedding workplace graduate attributes and ensuring that the quality of learning programs are both nationally and globally relevant. However, in higher education institution, there are significant numbers of students that stop their studies before graduation, especially for undergraduate students. Problem related to stopping out student and late or not graduating student can be improved by applying analytics. Using analytics, administrators, instructors and student can predict what will happen in future. Administrator and instructors can decide suitable intervention programs for at-risk students and before students decide to leave their study. Many different machine learning techniques have been implemented for predictive modelling in the past including decision tree, k-nearest neighbour, random forest, neural network, support vector machine, naïve Bayesian and a few others. A few attempts have been made to use Bayesian network and dynamic Bayesian network as modelling techniques for predicting at- risk student but a few challenges need to be resolved. The motivation for using dynamic Bayesian network is that it is robust to incomplete data and it provides opportunities for handling changing and dynamic environment. The trends and directions of research on prediction and identifying at-risk student are developing prediction model that can provide as early as possible alert to administrators, predictive model that handle dynamic and changing environment and the model that provide real-time prediction.

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Dr. Jyoti Nair and Dr. JK Sachdeva. "Predictive Modelling for Financial Distress amongst Manufacturing Companies in India." Journal of Global Economy 18, no. 4 (December 26, 2022): 261–76. http://dx.doi.org/10.1956/jge.v18i4.665.

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This study develops a model predicting financial distress amongst manufacturing companies in India using logistic regression. 18 financial ratios of 574 companies from 34 industries in manufacturing sector were examined for the period 2005 – 2019 to develop and validate the model. The study can be considered as one of the very few which has examined the financial distress indicators of manufacturing sector in India. EBIT margin, Interest coverage, quick ratio, Cash flow from operations to Sales, Debtors Turnover, Working Capital to Total Assets, Fixed Assets to Total Assets are important determinants of financial health of a business. This study provides useful insights to business managers and lenders to review and monitor financial soundness of business. The findings can also help policy makers to design policies and programs to support distressed industries in India. This study also addresses the urgent need for a country specific model for distress prediction. The model developed shows high predictive ability. Key words: Financial distress, Indian manufacturing sector, distress prediction, logistic regression, distress indicators.

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Varis, Olli. "A canonical approach to diagnostic and predictive modelling of phytoplankton communities." Archiv für Hydrobiologie 122, no. 2 (September 9, 1991): 147–66. http://dx.doi.org/10.1127/archiv-hydrobiol/122/1991/147.

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Ikeuchi, Daiki, Alejandro Vargas-Uscategui, Xiaofeng Wu, and Peter C. King. "Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing." Materials 12, no. 17 (September 2, 2019): 2827. http://dx.doi.org/10.3390/ma12172827.

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Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

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Koolhof, Iain S., Simon M. Firestone, Silvana Bettiol, Michael Charleston, Katherine B. Gibney, Peter J. Neville, Andrew Jardine, and Scott Carver. "Optimising predictive modelling of Ross River virus using meteorological variables." PLOS Neglected Tropical Diseases 15, no. 3 (March 9, 2021): e0009252. http://dx.doi.org/10.1371/journal.pntd.0009252.

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Background Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia. Methodology/Principal findings We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model’s ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance. Conclusions/Significance We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.

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Boukabou, A., and N. Mansouri. "Neural Predictive Control of Unknown Chaotic Systems." Nonlinear Analysis: Modelling and Control 10, no. 2 (April 25, 2005): 95–106. http://dx.doi.org/10.15388/na.2005.10.2.15125.

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In this work, a neural networks is developed for modelling and controlling a chaotic system based on measured input-output data pairs. In the chaos modelling phase, a neural network is trained on the unknown system. Then, a predictive control mechanism has been implemented with the neural networks to reach the close neighborhood of the chosen unstable fixed point embedded in the chaotic systems. Effectiveness of the proposed method for both modelling and prediction-based control on the chaotic logistic equation and Hénon map has been demonstrated.

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Devaraj, Nisha Kumari, and Ameer Al Mubarak Hamzah. "Predictive modelling of arsenate (As(V)) adsorption onto surface-engineered magnetite nanoparticles." F1000Research 10 (December 9, 2021): 1264. http://dx.doi.org/10.12688/f1000research.73260.1.

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Background: Since adsorption is a complex process, numerous models and theories have been devised to gain general understanding of its underlying mechanisms. The interaction between the adsorbates and adsorbents can be identified via modelling of the adsorption data with different adsorption isotherms as well as kinetic models. Many studies are also focused on developing predictive modelling techniques to facilitate accurate prediction of future adsorption trends. Methods: In this study, a predictive model was developed based on a multiple linear regression technique using existing data of As(V) adsorption onto several coated and uncoated magnetite samples. To understand the mechanisms and interactions involved, the data was first modelled using either Temkin or Freundlich linear isotherms. The predicted value is a single data point extension from the training data set. Subsequently, the predicted outcome and the experimental values were compared using multiple error functions to assess the predictive model’s performance. Results: In addition, certain values were compared to that obtained from the literature, and the results were found to have low error margins. Conclusion: To further gauge the effectiveness of the proposed model in accurately predicting future adsorption trends, it should be further tested on different adsorbent and adsorbate combinations.

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Tyralis, Hristos, and Georgia Papacharalampous. "Quantile-Based Hydrological Modelling." Water 13, no. 23 (December 3, 2021): 3420. http://dx.doi.org/10.3390/w13233420.

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Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necessary). To alleviate possible limitations related to these specific attributes, in this work we propose the calibration of the hydrological model by using the quantile loss function. By following this methodological approach, one can directly simulate pre-specified quantiles of the predictive distribution of streamflow. As a proof of concept, we apply our method in the frameworks of three hydrological models to 511 river basins in the contiguous US. We illustrate the predictive quantiles and show how an honest assessment of the predictive performance of the hydrological models can be made by using proper scoring rules. We believe that our method can help towards advancing the field of hydrological uncertainty.

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Kumar, Pawan, and Joy Prakash Misra. "Modelling of Tool Wear for Ti64 Turning Operation." Materials Science Forum 969 (August 2019): 750–55. http://dx.doi.org/10.4028/www.scientific.net/msf.969.750.

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In this study, an attempt has been made to develop a predictive model for tool nose wear. A well planned experimental design was utilized for this purpose using the design of experiment approach. From this research work, it was found that cutting speed (s), feed (f) and their interaction having the main effect on cutting tool performance. Using ANOVA analysis significance and contribution of each machining parameter and their interaction is also analyzed. Hence, a predictive model was developed to predict tool nose wear by using the various machining parameters and its adequacy was also checked for the prediction purpose.

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Alam, MD Erfanul, Dazhong Wu, and Andrew K. Dickerson. "Predictive modelling of drop ejection from damped, dampened wings by machine learning." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2241 (September 2020): 20200467. http://dx.doi.org/10.1098/rspa.2020.0467.

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The high frequency, low amplitude wing motion that mosquitoes employ to dry their wings inspires the study of drop release from millimetric, forced cantilevers. Our mimicking system, a 10-mm polytetrafluoroethylene cantilever driven through ±1 mm base amplitude at 85 Hz, displaces drops via three principal ejection modes: normal-to-cantilever ejection, sliding and pinch-off. The selection of system variables such as cantilever stiffness, drop location, drop size and wetting properties modulates the appearance of a particular ejection mode. However, the large number of system features complicate the prediction of modal occurrence, and the transition between complete and partial liquid removal. In this study, we build two predictive models based on ensemble learning that predict the ejection mode, a classification problem, and minimum inertial force required to eject a drop from the cantilever, a regression problem. For ejection mode prediction, we achieve an accuracy of 85% using a bagging classifier. For inertial force prediction, the lowest root mean squared error achieved is 0.037 using an ensemble learning regression model. Results also show that ejection time and cantilever wetting properties are the dominant features for predicting both ejection mode and the minimum inertial force required to eject a drop.

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Islam, Raihan Ul, Xhesika Ruci, Mohammad Shahadat Hossain, Karl Andersson, and Ah-Lian Kor. "Capacity Management of Hyperscale Data Centers Using Predictive Modelling." Energies 12, no. 18 (September 6, 2019): 3438. http://dx.doi.org/10.3390/en12183438.

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Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

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Melo, Rafael C., Julio E. Normey-Rico, and Jean-Marie Farines. "TCP modelling and predictive congestion control." IFAC Proceedings Volumes 42, no. 14 (2009): 72–77. http://dx.doi.org/10.3182/20090901-3-ro-4009.00009.

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Chen, YiPing. "Predictive modelling for EAST divertor operation." Physics of Plasmas 18, no. 6 (June 2011): 062506. http://dx.doi.org/10.1063/1.3596717.

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Choy, S. C., and J. C. Marshall. "Predictive modelling for river health assessment." SIL Proceedings, 1922-2010 27, no. 2 (October 2000): 828–33. http://dx.doi.org/10.1080/03680770.1998.11901354.

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Herwig, Ralf. "Predictive network modelling with toxicogenomics data." Toxicology Letters 229 (September 2014): S5. http://dx.doi.org/10.1016/j.toxlet.2014.06.043.

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Wang, Qi-Zhi, and Dong-Joo Lee. "Predictive modelling for rubber-toughened polymers." Mechanics of Materials 31, no. 11 (November 1999): 705–16. http://dx.doi.org/10.1016/s0167-6636(99)00034-4.

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Koutsoumanis, Kostas, Tom McMeekin, and Paw Dalgaard. "Introduction to predictive modelling special issue." International Journal of Food Microbiology 128, no. 1 (November 2008): 1. http://dx.doi.org/10.1016/j.ijfoodmicro.2008.09.001.

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Guild, F. J., and W. Bonfield. "Predictive modelling of hydroxyapatite-polyethylene composite." Biomaterials 14, no. 13 (October 1993): 985–93. http://dx.doi.org/10.1016/0142-9612(93)90190-d.

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Na, Yong-Su, and J. Y. Kim. "Predictive modelling of advanced tokamak scenarios." Computer Physics Communications 177, no. 1-2 (July 2007): 134. http://dx.doi.org/10.1016/j.cpc.2007.02.014.

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Ablitt, N., J. Gao, L. Stegger, J. Keegan, D. Firmin, and G. Yang. "Predictive cardiac motion modelling and correction." International Congress Series 1256 (June 2003): 1179–84. http://dx.doi.org/10.1016/s0531-5131(03)00520-x.

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Mckinlay, Steve T. "Evidence, Explanation and Predictive Data Modelling." Philosophy & Technology 30, no. 4 (January 6, 2017): 461–73. http://dx.doi.org/10.1007/s13347-016-0248-9.

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Jean, C., M. Jankovic, J. Stal-Le Cardinal, and J.-C. Bocquet. "Predictive modelling of telehealth system deployment." Journal of Simulation 9, no. 2 (May 2015): 182–94. http://dx.doi.org/10.1057/jos.2014.27.

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Pearson, Mitchell, Glen Livingston Jr, and Robert King. "An exploration of predictive football modelling." Journal of Quantitative Analysis in Sports 16, no. 1 (March 26, 2020): 27–39. http://dx.doi.org/10.1515/jqas-2019-0075.

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AbstractPredictive football modelling has become progressively popular over the last two decades. Due to this, numerous studies have proposed different types of statistical models to predict the outcome of a football match. This study provides a review of three different models published in the academic literature and then implements these on recent match data from the top football leagues in Europe. These models are then compared utilising the rank probability score to assess their predictive capability. Additionally, a modification is proposed which includes the travel distance of the away team. When tested on football leagues from both Australia and Russia, it is shown to improve predictive capability according to the rank probability score.

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Leicester, H. J., and E. R. Carson. "Patient Predictive Modelling in Intensive Care." IFAC Proceedings Volumes 27, no. 1 (March 1994): 225–26. http://dx.doi.org/10.1016/s1474-6670(17)46213-6.

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Roupas, Peter. "Predictive modelling of dairy manufacturing processes." International Dairy Journal 18, no. 7 (July 2008): 741–53. http://dx.doi.org/10.1016/j.idairyj.2008.03.009.

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Balla, Aikaterini, Gerasimos Pavlogeorgatos, Despoina Tsiafakis, and George Pavlidis. "Locating Macedonian tombs using predictive modelling." Journal of Cultural Heritage 14, no. 5 (September 2013): 403–10. http://dx.doi.org/10.1016/j.culher.2012.10.011.

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FERGUSSON, ANNA, and MAXINE PFANNKUCH. "INTRODUCING HIGH SCHOOL STATISTICS TEACHERS TO PREDICTIVE MODELLING BY EXPLORING DYNAMIC MOVIE RATINGS DATA: A FOCUS ON TASK DESIGN." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (July 4, 2022): 8. http://dx.doi.org/10.52041/serj.v21i2.49.

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With the advent of data science, recommendations for teaching statistical modelling include adopting a greater focus on prediction. However, there has been minimal research about the design of tasks for teaching predictive modelling from a data science. Therefore, a design-based research approach was used to develop a new web-based task that explored: accessing and using dynamic movie ratings data from an API; developing a model to generate prediction intervals; and modifying and running provided R code in the browser. The task was implemented within a face-to-face teaching experiment involving six high school statistics teachers. Analysis of the teacher responses to the task identified four key task design features that appeared to stimulate development of statistical and computational ideas related to predictive modelling and APIs.

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Freitas, Elisabete, Joaquim Tinoco, Francisco Soares, Jocilene Costa, Paulo Cortez, and Paulo Pereira. "Modelling Tyre-Road Noise with Data Mining Techniques." Archives of Acoustics 40, no. 4 (December 1, 2015): 547–60. http://dx.doi.org/10.1515/aoa-2015-0054.

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Abstract The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V 4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

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Franklin, Janet. "Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients." Progress in Physical Geography: Earth and Environment 19, no. 4 (December 1995): 474–99. http://dx.doi.org/10.1177/030913339501900403.

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Predictive vegetation mapping can be defined as predicting the geographic distribution of the vegetation composition across a landscape from mapped environmental variables. Comput erized predictive vegetation mapping is made possible by the availability of digital maps of topography and other environmental variables such as soils, geology and climate variables, and geographic information system software for manipulating these data. Especially important to predictive vegetation mapping are interpolated climatic variables related to physiological tolerances, and topographic variables, derived from digital elevation grids, related to site energy and moisture balance. Predictive vegetation mapping is founded in ecological niche theory and gradient analysis, and driven by the need to map vegetation patterns over large areas for resource conservation planning, and to predict the effects of environmental change on vegetation distributions. Predictive vegetation mapping has advanced over the past two decades especially in conjunction with the development of remote sensing-based vegetation mapping and digital geographic information analysis. A number of statistical and, more recently, machine-learning methods have been used to develop and implement predictive vegetation models.

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Battilani, P., and M. Camardo Leggieri. "Predictive modelling of aflatoxin contamination to support maize chain management." World Mycotoxin Journal 8, no. 2 (January 1, 2015): 161–70. http://dx.doi.org/10.3920/wmj2014.1740.

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The aim of this work was to design the potential support given by predictive models to maize management in a chain vision aimed at minimising aflatoxin contamination and human and animal exposure. There are some predictive models available but only AFLA-maize, which is a mechanistic model, is suitable for aflatoxin risk prediction worldwide. Weather data are the mandatory input for aflatoxin risk prediction and the output depends strictly on data sources, thus being influenced by both the time and distance scale of meteorological data. A user friendly summary interface of output data from predictive models is represented by risk maps in which the spatial gradient of the risk is highlighted. Actual (day by day throughout the maize growing season), historical (collected in the past) and future (predicted) data can be considered from single weather stations, a network of weather stations or a data base to support a single farm, a group of farms or an area, respectively. Past scenarios are the output generated by historical data, predictions related to actual data describe the risk situation of the current growing season and future data support the prediction of future scenarios. Model predictions cannot really support operational decisions throughout the maize growing season, but they are useful approaching crop ripening, when it is suggested that early harvest is associated with high risk and a switch from contaminated grain to non-food/ feed use can be decided. Scenarios generated by historical data can support preseason decisions with more careful management in high risk areas, while climate change scenarios are mainly destined for strategic actions deputed to institutions and policy makers. Model predictions destined for all the actors in the chain (farmers, extension services, stakeholders, politicians, institutions and researchers) can further support crop management, being also useful as communication and risk management tools.

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Pavinich, W. A., W. L. Server, and T. J. Griesbach. "Radiation Embrittlement Mechanistic Modelling." Applied Mechanics Reviews 46, no. 5 (May 1, 1993): 162–70. http://dx.doi.org/10.1115/1.3120326.

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The mechanistic models of radiation embrittlement for reactor vessel steels are reviewed and direction for improving these models is provided. Improvement in these mechanistic models will lead to predictive expressions for parameters of engineering interest. This paper provides the initial direction of modelling efforts that will improve existing or develop new predictive equations for damage attenuation, temperature effects, thermal annealing, the effect of post weld heat treatment, transition behavior and upper shelf behavior of reactor vessel steels.

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Jha, R. S., Navani Niharika Jha, and Mandar M. Lele. "Predictive Modelling of Grate Combustion and Boiler Dynamics." Mathematical Modelling of Engineering Problems 9, no. 1 (February 28, 2022): 233–44. http://dx.doi.org/10.18280/mmep.090129.

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Pressure and air to fuel ratio control are extremely difficult in coal-fired grate boilers due to a significant lag in combustion. This leads to suboptimal operation of the boiler and poor efficiency of the plant. This also leads to higher level fluctuation. Fluctuation in pressure, water level and oxygen level are quite evident in the operation of coal-fired grate boilers in fluctuating load conditions. The present work aims to develop a predictive and dynamic simulation model of a coal-fired grate boiler for the prediction of pressure, and water level in fluctuating load conditions and its extension for the prediction of oxygen level. A data-driven approach has been used for the prediction of heat release, distribution of heat transfer, circulation analysis and airflow through the various dampers. This model has been integrated with the boiler dynamics model of a hybrid boiler. Errors in pressure and water level are measured for training data and the multi-objective optimisation method is used for the minimisation of errors. The Batch Gradient Descent method has been used for the minimisation of errors. The proposed integrated model is used for the estimation of heat release and the rate of combustion. Stochiometric combustion calculation is used to predict oxygen level by using the predicted value of airflow and rate of combustion. Root mean squared error is calculated for oxygen level and minimised by the Batch Gradient Descent algorithm. The model has good accuracy in the prediction of boiler pressure and water level and can be extended to improve the boiler controls of a solid fuel fired reciprocating grate boiler in extremely fluctuating load conditions.

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Carpita, Maurizio, Enrico Ciavolino, and Paola Pasca. "Exploring and modelling team performances of the Kaggle European Soccer database." Statistical Modelling 19, no. 1 (January 10, 2019): 74–101. http://dx.doi.org/10.1177/1471082x18810971.

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This study explores a big and open database of soccer leagues in 10 European countries. Data related to players, teams and matches covering seven seasons (from 2009/2010 to 2015/2016) were retrieved from Kaggle, an online platform in which big data are available for predictive modelling and analytics competition among data scientists. Based on both preliminary data analysis, experts’ evaluation and players’ position on the football pitch, role-based indicators of teams’ performance have been built and used to estimate the win probability of the home team with the binomial logistic regression (BLR) model that has been extended including the ELO rating predictor and two random effects due to the hierarchical structure of the dataset. The predictive power of the BLR model and its extensions has been compared with the one of other statistical modelling approaches (Random Forest, Neural Network, k-NN, Naïve Bayes). Results showed that role-based indicators substantially improved the performance of all the models used in both this work and in previous works available on Kaggle. The base BLR model increased prediction accuracy by 10 percentage points, and showed the importance of defence performances, especially in the last seasons. Inclusion of both ELO rating predictor and the random effects did not substantially improve prediction, as the simpler BLR model performed equally good. With respect to the other models, only Naïve Bayes showed more balanced results in predicting both win and no-win of the home team.

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Journal articles on the topic 'Predictive modelling'

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