Ze Jiang
Expertise in Hydro-climatology
Expertise in Hydro-climatology
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Singapore-MIT Alliance project and World Bank project
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Public Utilities Board (PUB) project
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PUB-TMSI-Monash University project
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ARC linkage grant and Department of Industry, NSW, Australia
Published in Neural Information Processing Systems (NeurIPS), 2022
This paper addresses the problem of model collapse under very few labels (one label per class) and proposes superclass mining for potential discriminative information to aid model training.
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The open-source R package NPRED is used to identify the meaningful predictors to the response from a large set of potential predictors.
The open-source software WASP is used for system modeling and prediction.
Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems.
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Recommended citation: Jiang, Z., Molkenthin, F., & Sieker, H. (2016). Urban Surface Characteristics Study Using Time-area Function Model: A Case Study in Saudi Arabia. 12th International Conference on Hydroinformatics HIC 2016, Poster, Songdo Convensia Convention Center, 21-26 August 2016, FP-10.
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Recommended citation: Jiang, Z., Raghavan, S. V., Hur, J., Sun, Y., and Liong, S.-Y.: Impacts of Climate Change on Rice Crop Yields in Vietnam, Asia Oceania Geosciences Society (AOGS) 2017, Suntec Singapore Convention & Exhibition Centre, 6-11 August 2017, IG04-A026. Download talk here
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Recommended citation: Jiang, Z., Sharma, A., and Johnson, F.: Drought prediction for improved water resource management: A wavelet-based system prediction approach, STAHY 2019, Wentian Building, Hohai University, Nanjing, 19-20 October 2019. Download talk here
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Recommended citation: Jiang, Z., Sharma, A., and Johnson, F.: A wavelet-based method to analyse sustained hydrological anomalies under climate change, MODSIM 2019, National Convention Centre Canberra, 1-6 December 2019, K22. Download talk here
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Recommended citation: Sharma, A., Jiang, Z., and Johnson, F.: Forecasting drought revisited - the importance of spectral transformations to dominant atmospheric predictor variables, EGU General Assembly 2020, Online, 4-8 May 2020, EGU2020-12334, https://doi.org/10.5194/egusphere-egu2020-12334, 2020. Download talk here
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Recommended citation: Jiang, Z., Sharma, A. and Johnson, F., Hydro-climatological forecasting: A view from the spectral domain. In AGU Fall Meeting 2020. San Francisco, CA, USA, H184-07. Link Download PPT Video
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Recommended citation: Jiang, Z., Sharma, A., and Johnson, F.: Advanced wavelet-based variance transformation algorithms for ENSO forecasting over long lead times, MODSIM 2021, The University of Sydney and International Convention Centre, 5-10 December 2021, K5. Video
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Recommended citation: Jiang, Z., & Johnson F.: Applications of the wavelet-based method for postprocessing rainfall forecasts – Implications for urban flood forecasting, Asia Oceania Geosciences Society (AOGS) 2022, Singapore, 1-5 August 2022, HS37-A006. Download talk here
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Recommended citation: Jiang, Z., Sharma, A., & Johnson F.: Hydrologic forecasting over long lead times: A wavelet-based variance transformation approach, Asia Oceania Geosciences Society (AOGS) 2022, Singapore, 1-5 August 2022, HS34-A011. Download talk here
Undergraduate course, School of Civil and Environmental Engineering, UNSW Sydney, 2019
Masters course, School of Civil and Environmental Engineering, UNSW Sydney, 2019