ORCID Profile
0000-0001-7809-7832
Current Organisations
University of Adelaide
,
University of Saskatchewan
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Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2016
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2018
Publisher: Elsevier BV
Date: 2017
Publisher: MDPI AG
Date: 08-06-2020
DOI: 10.3390/W12061646
Abstract: Understanding changes in precipitation extremes is critical for designing mitigation measures for the potential implications of a warming climate. This study assessed changes in the magnitude and frequency of precipitation extremes over Vietnam using high-quality gridded daily precipitation observations from 1980 to 2010. The annual maxima precipitation was analyzed to detect historical changes in the magnitude of precipitation extremes, while the number of heavy precipitation events, defined using the peak-over-threshold approach, was used to assess changes in the frequency of precipitation extremes. We found a strong signal of changes in the frequency of heavy precipitation, with 28.3% of Vietnam’s landmass exhibiting significant increasing trends. The magnitude of annual maxima precipitation shows a mixed pattern of changes, with less than 10% of Vietnam’s landmass exhibiting significant (both increasing and decreasing) trends. To identify possible mechanisms driving changes in precipitation, we assessed the relationship between inter-annual variations in precipitation extremes and climate variability represented by the teleconnection patterns of the Northern Hemisphere. Using five climate indices, we found that teleconnections across the Indian and Pacific Oceans have implied large control over the characteristics of precipitation extremes across Vietnam, with up to 30% of Vietnam’s landmass exhibiting a significant relationship.
Publisher: American Society of Civil Engineers (ASCE)
Date: 2023
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2023
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2022
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1016/J.WATRES.2022.118828
Abstract: In water pipeline systems, monitoring and predicting hydraulic transient events are important to ensure the proper operation of pressure control devices (e.g., pressure reducing valves) and prevent potential damages to the network infrastructure. Simulating transient pressures using traditional numerical methods, however, require a complete model with known boundary and initial conditions, which is rarely able to obtain in a real system. This paper proposes a new physics-based and data-driven method for targeted transient pressure reconstruction without the need of having a complete pipe system model. The new method formulates a physics-informed neural network (PINN) by incorporating both measured data and physical laws of the transient flow in the training process. This enables the PINN to learn and explore hidden information of the hydraulic transient (e.g., boundary conditions and wave d ing characteristics) that is embedded in the measured data. The trained PINN can then be used to predict transient pressures at any location of the pipeline. Results from two numerical and one experimental case studies showed a high accuracy of the pressure reconstruction using the proposed approach. In addition, a series of sensitivity analyses have been conducted to determine the optimal hyperparameters in the PINN and to understand the effects of the sensor configuration on the model performance.
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2017
Publisher: American Geophysical Union (AGU)
Date: 03-2020
DOI: 10.1029/2019WR025436
No related grants have been discovered for Nhu Cuong Do.