Shiang‐Jen Wu

493 total citations
36 papers, 385 citations indexed

About

Shiang‐Jen Wu is a scholar working on Global and Planetary Change, Atmospheric Science and Water Science and Technology. According to data from OpenAlex, Shiang‐Jen Wu has authored 36 papers receiving a total of 385 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Global and Planetary Change, 15 papers in Atmospheric Science and 12 papers in Water Science and Technology. Recurrent topics in Shiang‐Jen Wu's work include Flood Risk Assessment and Management (18 papers), Hydrology and Watershed Management Studies (12 papers) and Precipitation Measurement and Analysis (9 papers). Shiang‐Jen Wu is often cited by papers focused on Flood Risk Assessment and Management (18 papers), Hydrology and Watershed Management Studies (12 papers) and Precipitation Measurement and Analysis (9 papers). Shiang‐Jen Wu collaborates with scholars based in Taiwan, Switzerland and Indonesia. Shiang‐Jen Wu's co-authors include Che-Hao Chang, Yeou‐Koung Tung, Jinn‐Chuang Yang, Gwo‐Fong Lin, Jan‐Chi Yang, Samkele S. Tfwala, Ping Xie, Xijun Wang, Tung‐Lin Tsai and Jihn‐Sung Lai and has published in prestigious journals such as Scientific Reports, Journal of Hydrology and International Journal for Numerical Methods in Engineering.

In The Last Decade

Shiang‐Jen Wu

31 papers receiving 376 citations

Peers

Shiang‐Jen Wu
Wenchao Qi China
Solomon Seyoum Netherlands
Kaihua Guo China
Hao Han China
Chongxun Mo China
Ioannis M. Kourtis Greece
Mohammad Reza Goodarzi Iran
Huicheng Zhou China
Byung Sik Kim South Korea
Roshan Srivastav India
Wenchao Qi China
Shiang‐Jen Wu
Citations per year, relative to Shiang‐Jen Wu Shiang‐Jen Wu (= 1×) peers Wenchao Qi

Countries citing papers authored by Shiang‐Jen Wu

Since Specialization
Citations

This map shows the geographic impact of Shiang‐Jen Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shiang‐Jen Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shiang‐Jen Wu more than expected).

Fields of papers citing papers by Shiang‐Jen Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shiang‐Jen Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shiang‐Jen Wu. The network helps show where Shiang‐Jen Wu may publish in the future.

Co-authorship network of co-authors of Shiang‐Jen Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Shiang‐Jen Wu. A scholar is included among the top collaborators of Shiang‐Jen Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shiang‐Jen Wu. Shiang‐Jen Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Wu, Shiang‐Jen. (2025). Applications of Machine Learning in Industrial Manufacturing. Applied and Computational Engineering. 175(1). 1–7.
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Chang, Che-Hao, et al.. (2024). Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation. Journal of Hydrology. 636. 131308–131308. 1 indexed citations
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Wu, Shiang‐Jen, et al.. (2022). Long-Term Flooding Maps Forecasting System Using Series Machine Learning and Numerical Weather Prediction System. Water. 14(20). 3346–3346. 3 indexed citations
7.
Chang, Che-Hao, et al.. (2022). Variation of uncertainty of drainage density in flood hazard mapping assessment with coupled 1D–2D hydrodynamics model. Natural Hazards. 111(3). 2297–2315. 16 indexed citations
8.
Lin, Gwo‐Fong, et al.. (2022). Long-Term Temporal Flood Predictions Made Using Convolutional Neural Networks. Water. 14(24). 4134–4134. 9 indexed citations
9.
Wu, Shiang‐Jen, et al.. (2021). Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model. Water. 13(21). 3128–3128. 4 indexed citations
10.
Wu, Shiang‐Jen, et al.. (2021). Stochastic modeling of gridded short-term rainstorms. Hydrology research. 52(4). 876–904. 8 indexed citations
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Wu, Shiang‐Jen, et al.. (2019). Application of Artificial Intelligence to Disaster Prevention and Early Warning of Urban Flooding. EGU General Assembly Conference Abstracts. 7021. 1 indexed citations
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Wu, Shiang‐Jen & Ping Xie. (2016). Blending Gauge Data with CMORPH for a Global Daily Precipitation Analysis. AGU Fall Meeting Abstracts. 2016. 1 indexed citations
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Chang, Che-Hao, et al.. (2015). Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method. Stochastic Environmental Research and Risk Assessment. 29(7). 1903–1920. 19 indexed citations
16.
Wu, Shiang‐Jen, et al.. (2014). Modeling the effect of uncertainties in rainfall characteristics on flash flood warning based on rainfall thresholds. Natural Hazards. 75(2). 1677–1711. 36 indexed citations
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Wu, Shiang‐Jen, et al.. (2013). Modeling probabilistic radar rainfall estimation at ungauged locations based on spatiotemporal errors which correspond to gauged data. Hydrology research. 46(1). 39–59. 13 indexed citations
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Wu, Shiang‐Jen, Jinn‐Chuang Yang, & Yeou‐Koung Tung. (2010). Risk analysis for flood-control structure under consideration of uncertainties in design flood. Natural Hazards. 58(1). 117–140. 44 indexed citations
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Wu, Shiang‐Jen, Jinn‐Chuang Yang, & Yeou‐Koung Tung. (2005). Identification and stochastic generation of representative rainfall temporal patterns in Hong Kong territory. Stochastic Environmental Research and Risk Assessment. 20(3). 171–183. 29 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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