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You searched for +publisher:"University of New South Wales" +contributor:("Marshall, Lucy, Civil & Environmental Engineering, Faculty of Engineering, UNSW"). Showing records 1 – 5 of 5 total matches.

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University of New South Wales

1. Wu, Xia. Incorporating observation uncertainty in environmental modelling.

Degree: Civil & Environmental Engineering, 2020, University of New South Wales

 Despite improvements in data collection and observing technologies, observation error in environmental data is still considerable and its precise quantification is important for improved environmental(more)

Subjects/Keywords: Residual error model; Uncertainty quantification; Model calibration; Hydrologic modelling

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APA (6th Edition):

Wu, X. (2020). Incorporating observation uncertainty in environmental modelling. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/70128

Chicago Manual of Style (16th Edition):

Wu, Xia. “Incorporating observation uncertainty in environmental modelling.” 2020. Doctoral Dissertation, University of New South Wales. Accessed September 27, 2020. http://handle.unsw.edu.au/1959.4/70128.

MLA Handbook (7th Edition):

Wu, Xia. “Incorporating observation uncertainty in environmental modelling.” 2020. Web. 27 Sep 2020.

Vancouver:

Wu X. Incorporating observation uncertainty in environmental modelling. [Internet] [Doctoral dissertation]. University of New South Wales; 2020. [cited 2020 Sep 27]. Available from: http://handle.unsw.edu.au/1959.4/70128.

Council of Science Editors:

Wu X. Incorporating observation uncertainty in environmental modelling. [Doctoral Dissertation]. University of New South Wales; 2020. Available from: http://handle.unsw.edu.au/1959.4/70128


University of New South Wales

2. Pathiraja, Sahani. Improving Data Assimilation Algorithms for Enhanced Environmental Predictions.

Degree: Civil & Environmental Engineering, 2018, University of New South Wales

 Data Assimilation (DA) methods provide a means of combining model output with observations based on their respective uncertainties. They are considered an invaluable tool in… (more)

Subjects/Keywords: Uncertainty quantification; Data assimilation; Forecasting; Hydrology; Meteorology

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APA (6th Edition):

Pathiraja, S. (2018). Improving Data Assimilation Algorithms for Enhanced Environmental Predictions. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/59579 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49002/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Pathiraja, Sahani. “Improving Data Assimilation Algorithms for Enhanced Environmental Predictions.” 2018. Doctoral Dissertation, University of New South Wales. Accessed September 27, 2020. http://handle.unsw.edu.au/1959.4/59579 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49002/SOURCE02?view=true.

MLA Handbook (7th Edition):

Pathiraja, Sahani. “Improving Data Assimilation Algorithms for Enhanced Environmental Predictions.” 2018. Web. 27 Sep 2020.

Vancouver:

Pathiraja S. Improving Data Assimilation Algorithms for Enhanced Environmental Predictions. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2020 Sep 27]. Available from: http://handle.unsw.edu.au/1959.4/59579 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49002/SOURCE02?view=true.

Council of Science Editors:

Pathiraja S. Improving Data Assimilation Algorithms for Enhanced Environmental Predictions. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/59579 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49002/SOURCE02?view=true


University of New South Wales

3. Abu Shoaib, Syed. The relative importance and characteristics of uncertainty in hydrology.

Degree: Civil & Environmental Engineering, 2018, University of New South Wales

 Quantifying uncertainty in hydrologic models is essential for their effective use and assessment. This thesis presents methods that can be used to estimate and evaluate… (more)

Subjects/Keywords: Hydrolgy; Uncertainty; Model structures; Input

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APA (6th Edition):

Abu Shoaib, S. (2018). The relative importance and characteristics of uncertainty in hydrology. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/59637

Chicago Manual of Style (16th Edition):

Abu Shoaib, Syed. “The relative importance and characteristics of uncertainty in hydrology.” 2018. Doctoral Dissertation, University of New South Wales. Accessed September 27, 2020. http://handle.unsw.edu.au/1959.4/59637.

MLA Handbook (7th Edition):

Abu Shoaib, Syed. “The relative importance and characteristics of uncertainty in hydrology.” 2018. Web. 27 Sep 2020.

Vancouver:

Abu Shoaib S. The relative importance and characteristics of uncertainty in hydrology. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2020 Sep 27]. Available from: http://handle.unsw.edu.au/1959.4/59637.

Council of Science Editors:

Abu Shoaib S. The relative importance and characteristics of uncertainty in hydrology. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/59637


University of New South Wales

4. Kang, Tae Ho. The cognitive classification of model deficiency in hydrology.

Degree: Civil & Environmental Engineering, 2018, University of New South Wales

 Predicting and understanding a hydrologic system requires modelling. The modelling can result in minimising casualties and socio-economic damages from catastrophic disasters such as typhoon, flooding,… (more)

Subjects/Keywords: Uncertainty; Hydrology; Model deficiency

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kang, T. H. (2018). The cognitive classification of model deficiency in hydrology. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60438

Chicago Manual of Style (16th Edition):

Kang, Tae Ho. “The cognitive classification of model deficiency in hydrology.” 2018. Doctoral Dissertation, University of New South Wales. Accessed September 27, 2020. http://handle.unsw.edu.au/1959.4/60438.

MLA Handbook (7th Edition):

Kang, Tae Ho. “The cognitive classification of model deficiency in hydrology.” 2018. Web. 27 Sep 2020.

Vancouver:

Kang TH. The cognitive classification of model deficiency in hydrology. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2020 Sep 27]. Available from: http://handle.unsw.edu.au/1959.4/60438.

Council of Science Editors:

Kang TH. The cognitive classification of model deficiency in hydrology. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/60438


University of New South Wales

5. Tang, Yating. Prior information and multi-objective analysis in Bayesian ecohydrological modeling.

Degree: Civil & Environmental Engineering, 2017, University of New South Wales

 This thesis presents a Bayesian multi-objective calibration approach associated with uncertainty analysis in ecohydrological modeling. This work focuses on three main objectives: (1) the development… (more)

Subjects/Keywords: ecohydrological model; Bayesian ecohydrological modeling; Bayesian

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tang, Y. (2017). Prior information and multi-objective analysis in Bayesian ecohydrological modeling. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60071 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Tang, Yating. “Prior information and multi-objective analysis in Bayesian ecohydrological modeling.” 2017. Doctoral Dissertation, University of New South Wales. Accessed September 27, 2020. http://handle.unsw.edu.au/1959.4/60071 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true.

MLA Handbook (7th Edition):

Tang, Yating. “Prior information and multi-objective analysis in Bayesian ecohydrological modeling.” 2017. Web. 27 Sep 2020.

Vancouver:

Tang Y. Prior information and multi-objective analysis in Bayesian ecohydrological modeling. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2020 Sep 27]. Available from: http://handle.unsw.edu.au/1959.4/60071 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true.

Council of Science Editors:

Tang Y. Prior information and multi-objective analysis in Bayesian ecohydrological modeling. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/60071 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true

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