人工智慧與多尺度水文資訊
Artificial Intelligence and Multi-Scale Hydrological Information Laboratory
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Course Description
The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of machine learning algorithms including BPNN, RBFNN, SOM, RNN, CFNN, ANFIS as well as deep learning algorithms such as LSTM and CNN. The course is primarily intended for those individuals, who want to understand the underlying principles of artificial neural networks and want to be able to apply various neurocomputing techniques to solve problems in earth sciences, business administration, ecological environment, biomedical, and engineering.
Course Description
This course introduces the basics of remote sensing and geographic information system and the application in water resources.
Course Description
The main purpose of this course will be concentrated on guiding all students, who can learn the basic concept and fundamental technology for natural hazard mitigation and disaster management. Students taking this course can have an overall review on disaster management, which is going to connect other specific courses scheduled at this international master program. This will help students conduct disaster mitigation and management in the future.
Bindas, Tadd, Wen-Ping Tsai, Jiangtao Liu, Farshid Rahmani, Dapeng Feng, Yuchen Bian and Chaopeng Shen*, 2024. “Improving river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning”, Water Resources Research 60 (1).
Yalan Song, Wen-Ping Tsai, Jonah Gluck, Alan Rhoades, Colin Zarzycki, Rachel McCrary, Kathryn Lawson, and Chaopeng Shen, 2024. “LSTM-based data integration to improve snow water equivalent prediction and diagnose error sources”, Journal of Hydrometeorology. 25(1):223-237.
Wen-Ping Tsai, Dapeng Fen, Ming Pan, Hylke Beck, Kathryn Lawson, Yuan Yang, Jiangtao Liu, and Chaopeng Shen, 2021. “From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling”, Nature Communications. 12, 5988.
Kai Ma, Dapeng Feng, Kathryn Lawson, Wen-Ping Tsai, Chuan Liang, Xiaorong Huang, Ashutosh Sharma, Chaopeng Shen, 2021. “Transferring hydrologic data across continents -- leveraging data-rich regions to improve hydrologic prediction in data-sparse regions”, Water Resources Research. 57(5). e2020WR028600.
Wei Zhi, Dapeng Feng, Wen-Ping Tsai, Gary Sterle, Adrian Harpold, Chaopeng Shen, Li Li, 2021. “From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?”, Environmental Science & Technology. 55, 4, 2357-2368.
Wen-Ping Tsai, Kuai Fang, Xinye Ji, Kathryn Lawson, Chaopeng Shen, 2020, ”Revealing causal controls of storage-streamflow relationships with a data-centric Bayesian framework combining machine learning and process-based modeling”, Frontiers in Water. 2: 40.
Jia-Hao Hu, Wen-Ping Tsai*, Su-Ting Cheng, Fi-John Chang, 2020, “Explore the Relationship between Fish Community and Environmental Factors by Machine Learning Techniques”, Environmental Research. 184:109262.
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