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The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. A supporting open-source software, Wavelet System Prediction (WASP), can be found under page of Software.
Expertise in Hydro-climatology
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ARC linkage grant and Department of Industry, NSW, Australia
Published in ACS ES&T Engineering, 2023
This paper is about using Machine Learning for phosphate adsorption prediction.
Recommended citation: Wu, Yangyang; Li, Yingze; Jiang, Ze; Xu, Ziyang; Yang, Mingbao; Ding, Jiahui; Zhang, Changyong. (2023). "Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents." ACS ES&T Engineering, acsestengg.3c00001. https://doi.org/10.1021/acsestengg.3c00001
Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems.
Published:
Recommended citation: Jiang, Z., Haberlandt, U., and Sharma, A.: Enhanced daily streamflow simulation for future climates using a non-parametric method with variable selection and transformation, Asia Oceania Geosciences Society (AOGS) 2024, South Korea, 23 June-28 June 2024, HS13-A009. Download talk here
Undergraduate course, School of Civil and Environmental Engineering, UNSW Sydney, 2024