Parallel computing in GIS
Parallel computing speeds up programs, allowing the usage of large-scale and high-resolution data in a reasonable time. 
Posts by Collection
portfolio
Deep learning model for hydraulic engineering
Cutting-edge deep learning models are employed to predict riverbed topography for engineering applications. 
Python packages for geospatial data
Python packages are develped to automatically load and process geospatial data related to river hydraulics. 
publications
Investigating the Effectiveness and Optimal Spatial Arrangement of Low-Impact Development Facilities
Published in Journal of Hydrology, 2019
Recommended citation: Liang, C. Y., You, G. J. Y., & Lee, H. Y. (2019). Investigating the effectiveness and optimal spatial arrangement of low-impact development facilities. Journal of Hydrology, 577, 124008.
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Evaluating the Cost of Failure Risk: A Case Study of the Kang-Wei-Kou Stream Diversion Project
Published in Water, 2021
Recommended citation: Liang, C. Y., Wang, Y. H., You, G. J. Y., Chen, P. C., & Lo, E. (2021). Evaluating the Cost of Failure Risk: A Case Study of the Kang-Wei-Kou Stream Diversion Project. Water, 13(20), 2881.
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Extracting river morphology features from single-beam bathymetry surveys
Published in AGU fall meeting, 2021
Recommended citation: Liang, C. Y., Dey, S., & Merwade, V. (2021, December). Extracting river morphology features from single-beam bathymetry surveys. In AGU Fall Meeting Abstracts (Vol. 2021, pp. H15M-1194).
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SPRING-An automated and flexible framework for developing large-scale 3D representations of river network
Published in AGU fall meeting, 2021
Recommended citation: Dey, S., Liang, C. Y., Merwade, V., & Saksena, S. (2021, December). SPRING-An automated and flexible framework for developing large-scale 3D representations of river network. In AGU Fall Meeting Abstracts (Vol. 2021, pp. H15F-1104).
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Introducing RIMORPHIS, the River Morphology Information System: An Online Community Resource for River Morphology Data and Tools
Published in AGU fall meeting, 2022
Recommended citation: Merwade, V., Minear, J. T., Muste, M., Cox, A., Demir, I., Dey, S., ... & Sermet, Y. (2022, December). Introducing RIMORPHIS, the River Morphology Information System: An Online Community Resource for River Morphology Data and Tools. In Fall Meeting 2022. AGU.
Predicting river bathymetry for data sparse regions with a GANs model
Published in AGU fall meeting, 2022
Recommended citation: Liang, C. Y., Merwade, V., & Dey, S. (2022, December). Predicting river bathymetry for data sparse regions with a GANs model. In AGU Fall Meeting Abstracts (Vol. 2022, pp. EP42C-1610).
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Application of a Generative Deep Learning Model for Predicting River Bathymetry in Data Sparse Regions
Published in AGU fall meeting, 2023
Recommended citation: Liang, C. Y., & Merwade, V. (2023, December). Application of a Generative Deep Learning Model for Predicting River Bathymetry in Data Sparse Regions. In AGU Fall Meeting Abstracts (Vol. 2023, No. 1748, pp. H23M-1748).
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Quantifying long-term geomorphological changes in highly managed river channels of the United States
Published in AGU fall meeting, 2023
Recommended citation: Dey, S., Liang, C. Y., Cox, A., & Merwade, V. (2023, December). Quantifying long-term geomorphological changes in highly managed river channels of the United States. In AGU Fall Meeting Abstracts (Vol. 2023, pp. EP34A-07).
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RIMORPHIS-An information system for accessing and processing river morphology data
Published in AGU fall meeting, 2023
Recommended citation: Merwade, V., Demir, I., Minear, J. T., Muste, M., Cox, A., Liang, C. Y., ... & Sermet, Y. (2023, December). RIMORPHIS-An information system for accessing and processing river morphology data. In AGU Fall Meeting Abstracts (Vol. 2023, pp. IN11A-07).
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talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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