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Double weighted k nearest neighbours for binary classification of high dimensional genomic data (2025)
Journal Article
Ali, A., Khan, Z., Du, H., & Aldahmani, S. (2025). Double weighted k nearest neighbours for binary classification of high dimensional genomic data. Scientific Reports, 15(1), Article 12681. https://doi.org/10.1038/s41598-025-97505-2

High dimensional gene expression datasets consist of a large number of genes, many of which do not play a significant role in classifying tissue samples. The high dimensional nature of this type of data, characterized by a large number of gene featur... Read More about Double weighted k nearest neighbours for binary classification of high dimensional genomic data.

An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning (2025)
Presentation / Conference Contribution
Liu, J., Kazemtabrizi, B., Du, H., Matthews, P., & Sun, H. (2024, November). An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning. Presented at 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, USA

With the increasing integration of renewable energy sources into the power grid, accurate and reliable ultra-short-term forecasting of wind power is critical for optimizing grid stability and energy efficiency, especially for a highly dynamic and var... Read More about An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning.

Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model (2024)
Journal Article
Domingo, D., Royapoor, M., Du, H., Boranian, A., Walker, S., & Goldstein, M. (2024). Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model. Energies, 17(16), Article 4014. https://doi.org/10.3390/en17164014

Energy models require accurate calibration to deliver reliable predictions. This study offers statistical guidance for a systematic treatment of uncertainty before and during model calibration. Statistical emulation and history matching are introduce... Read More about Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model.

Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks (2021)
Journal Article
Du, H., Sun, W., Goldstein, M., & Harrison, G. (2021). Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks. IEEE Access, 9, 118472-118483. https://doi.org/10.1109/access.2021.3105935

In power systems modelling, optimization methods based on certain objective function(s) are widely used to provide solutions for decision makers. For complex high-dimensional problems, such as network hosting capacity evaluation of intermittent renew... Read More about Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks.

Beyond Strictly Proper Scoring Rules: The Importance of Being Local (2021)
Journal Article
Du, H. (2021). Beyond Strictly Proper Scoring Rules: The Importance of Being Local. Weather and Forecasting, 36(2), 457-468. https://doi.org/10.1175/waf-d-19-0205.1

The evaluation of probabilistic forecasts plays a central role both in the interpretation and in the use of forecast systems and their development. Probabilistic scores (scoring rules) provide statistical measures to assess the quality of probabilist... Read More about Beyond Strictly Proper Scoring Rules: The Importance of Being Local.

Designing Multimodel Applications with Surrogate Forecast Systems (2020)
Journal Article
Smith, L. A., Du, H., & Higgins, S. (2020). Designing Multimodel Applications with Surrogate Forecast Systems. Monthly Weather Review, 148(6), 2233-2249. https://doi.org/10.1175/mwr-d-19-0061.1

Probabilistic forecasting is common in a wide variety of fields including geoscience, social science, and finance. It is sometimes the case that one has multiple probability forecasts for the same target. How is the information in these multiple nonl... Read More about Designing Multimodel Applications with Surrogate Forecast Systems.

Multi-model cross-pollination in time (2017)
Journal Article
Du, H., & Smith, L. A. (2017). Multi-model cross-pollination in time. Physica D: Nonlinear Phenomena, 353-354, 31-38. https://doi.org/10.1016/j.physd.2017.06.001

The predictive skill of complex models is rarely uniform in model-state space; in weather forecasting models, for example, the skill of the model can be greater in the regions of most interest to a particular operational agency than it is in “remote”... Read More about Multi-model cross-pollination in time.

Rising Above Chaotic Likelihoods (2017)
Journal Article
Du, H., & Smith, L. A. (2017). Rising Above Chaotic Likelihoods. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 246-258. https://doi.org/10.1137/140988784

Berliner [J. Amer. Statist. Assoc., 86 (1991), pp. 983--952] identified a number of difficulties in using the likelihood function within the Bayesian paradigm which arise both for state estimation and for parameter estimation of chaotic systems. Even... Read More about Rising Above Chaotic Likelihoods.

Towards improving the framework for probabilistic forecast evaluation (2015)
Journal Article
Smith, L. A., Suckling, E. B., Thompson, E. L., Maynard, T., & Du, H. (2015). Towards improving the framework for probabilistic forecast evaluation. Climatic Change, 132(1), 31-45. https://doi.org/10.1007/s10584-015-1430-2

The evaluation of forecast performance plays a central role both in the interpretation and use of forecast systems and in their development. Different evaluation measures (scores) are available, often quantifying different characteristics of forecast... Read More about Towards improving the framework for probabilistic forecast evaluation.

Probabilistic skill in ensemble seasonal forecasts (2014)
Journal Article
Smith, L. A., Du, H., Suckling, E. B., & Niehörster, F. (2015). Probabilistic skill in ensemble seasonal forecasts. Quarterly Journal of the Royal Meteorological Society, 141(689), 1085-1100. https://doi.org/10.1002/qj.2403

Simulation models are widely employed to make probability forecasts of future conditions on seasonal to annual lead times. Added value in such forecasts is reflected in the information they add, either to purely empirical statistical models or to sim... Read More about Probabilistic skill in ensemble seasonal forecasts.

Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario (2014)
Journal Article
Du, H., & Smith, L. A. (2014). Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario. Journal of the Atmospheric Sciences, 71(2), 469-482. https://doi.org/10.1175/jas-d-13-032.1

State estimation lies at the heart of many meteorological tasks. Pseudo-orbit-based data assimilation provides an attractive alternative approach to data assimilation in nonlinear systems such as weather forecasting models. In the perfect model scena... Read More about Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario.

Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models (2014)
Journal Article
Du, H., & Smith, L. A. (2014). Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models. Journal of the Atmospheric Sciences, 71(2), 483-495. https://doi.org/10.1175/jas-d-13-033.1

Data assimilation and state estimation for nonlinear models is a challenging task mathematically. Performing this task in real time, as in operational weather forecasting, is even more challenging as the models are imperfect: the mathematical system... Read More about Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models.

Laplace’s Demon and the Adventures of His Apprentices (2014)
Journal Article
Frigg, R., Bradley, S., Du, H., & Smith, L. A. (2014). Laplace’s Demon and the Adventures of His Apprentices. Philosophy of Science, 81(1), 31-59. https://doi.org/10.1086/674416

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (... Read More about Laplace’s Demon and the Adventures of His Apprentices.

Parameter estimation through ignorance (2012)
Journal Article
Du, H., & Smith, L. A. (2012). Parameter estimation through ignorance. Physical review E: Statistical, nonlinear, and soft matter physics, 86(1), Article 016213. https://doi.org/10.1103/physreve.86.016213

Dynamical modeling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate the adequacy of the mathematical structure of our models. Despite the imp... Read More about Parameter estimation through ignorance.

Exploiting dynamical coherence: A geometric approach to parameter estimation in nonlinear models (2010)
Journal Article
Smith, L. A., Cuéllar, M. C., Du, H., & Judd, K. (2010). Exploiting dynamical coherence: A geometric approach to parameter estimation in nonlinear models. Physics Letters A, 374(26), 2618-2623. https://doi.org/10.1016/j.physleta.2010.04.032

Parameter estimation in nonlinear models is a common task, and one for which there is no general solution at present. In the case of linear models, the distribution of forecast errors provides a reliable guide to parameter estimation, but in nonlinea... Read More about Exploiting dynamical coherence: A geometric approach to parameter estimation in nonlinear models.