Bing-Chen Jhong

427 total citations
19 papers, 324 citations indexed

About

Bing-Chen Jhong is a scholar working on Global and Planetary Change, Environmental Engineering and Atmospheric Science. According to data from OpenAlex, Bing-Chen Jhong has authored 19 papers receiving a total of 324 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Global and Planetary Change, 13 papers in Environmental Engineering and 11 papers in Atmospheric Science. Recurrent topics in Bing-Chen Jhong's work include Flood Risk Assessment and Management (13 papers), Hydrological Forecasting Using AI (12 papers) and Meteorological Phenomena and Simulations (7 papers). Bing-Chen Jhong is often cited by papers focused on Flood Risk Assessment and Management (13 papers), Hydrological Forecasting Using AI (12 papers) and Meteorological Phenomena and Simulations (7 papers). Bing-Chen Jhong collaborates with scholars based in Taiwan, Japan and United States. Bing-Chen Jhong's co-authors include Gwo‐Fong Lin, Ching‐Pin Tung, Chia‐Chuan Chang, Cheng‐Han Tsai, Hao‐Che Ho, Yih‐Chi Tan, Chung‐Yi Lin, Tomohiro Tanaka, Parmeshwar Udmale and Yasuto Tachikawa and has published in prestigious journals such as Journal of Hydrology, Natural Hazards and Water Resources Management.

In The Last Decade

Bing-Chen Jhong

18 papers receiving 319 citations

Peers

Bing-Chen Jhong

GPC

WST

F. Salas United States

Vinh Ngoc Tran United States

Taymoor A. Awchi Iraq

Taereem Kim United States

Ashoke Basistha India

Boosik Kang South Korea

Matin Rahnamay Naeini United States

Tsun-Hua Yang Taiwan

Ali Moridnejad Iran

Suresh Sharma United States

F. Salas United States

Bing-Chen Jhong

225 ×1.0

GPC

143 ×0.8

237 ×2.0

WST

93 ×0.8

16 ×0.7

Citations per year, relative to Bing-Chen Jhong Bing-Chen Jhong (= 1×) peers F. Salas

Countries citing papers authored by Bing-Chen Jhong

Since Specialization

Citations

This map shows the geographic impact of Bing-Chen Jhong'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 Bing-Chen Jhong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bing-Chen Jhong more than expected).

Fields of papers citing papers by Bing-Chen Jhong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bing-Chen Jhong. 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 Bing-Chen Jhong. The network helps show where Bing-Chen Jhong may publish in the future.

Co-authorship network of co-authors of Bing-Chen Jhong

This figure shows the co-authorship network connecting the top 25 collaborators of Bing-Chen Jhong. A scholar is included among the top collaborators of Bing-Chen Jhong 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 Bing-Chen Jhong. Bing-Chen Jhong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Jhong, Bing-Chen, et al.. (2025). A two-stage multi-step-ahead Gated-Recurrent-Unit-based method with the use of sewer level data for improving real-time forecasting of inundation depth. Journal of Hydrology. 659. 133230–133230.

Jhong, Bing-Chen, et al.. (2024). Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method. Water Resources Management. 38(4). 1359–1380. 11 indexed citations

Jhong, Bing-Chen, et al.. (2024). Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm. Water Resources Management. 38(3). 1141–1164. 24 indexed citations

Chiang, Li-Chi, et al.. (2023). Strategies analysis for improving SWAT model accuracy and representativeness of calibrated parameters in sediment simulation for various land use and climate conditions. Journal of Hydrology. 626. 130124–130124. 5 indexed citations

Chen, Wei‐Ting, et al.. (2023). Adaptation strategy with public space for pluvial flood risk mitigation in a densely populated city: A case study in Huwei, Taiwan. Journal of Hydrology Regional Studies. 48. 101452–101452. 3 indexed citations

Jhong, Bing-Chen, et al.. (2022). Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area. Natural Hazards. 11 indexed citations

Jhong, Bing-Chen, et al.. (2022). Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan. Water Resources Management. 36(9). 3221–3245. 7 indexed citations

Jhong, Bing-Chen, et al.. (2022). Assessing the effective spatial characteristics of input features through physics-informed machine learning models in inundation forecasting during typhoons. Hydrological Sciences Journal. 67(10). 1527–1545. 2 indexed citations

Jhong, Bing-Chen, et al.. (2021). Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with Multi-Objective Genetic Algorithm for Sediment Flux Prediction during Typhoons. Water Resources Management. 35(8). 2387–2408. 3 indexed citations

10.

Jhong, Bing-Chen, Yasuto Tachikawa, Tomohiro Tanaka, Parmeshwar Udmale, & Ching‐Pin Tung. (2020). A Generalized Framework for Assessing Flood Risk and Suitable Strategies under Various Vulnerability and Adaptation Scenarios: A Case Study for Residents of Kyoto City in Japan. Water. 12(9). 2508–2508. 10 indexed citations

11.

Jhong, Bing-Chen, et al.. (2019). Spatial Assessment of Climate Risk for Investigating Climate Adaptation Strategies by Evaluating Spatial-Temporal Variability of Extreme Precipitation. Water Resources Management. 33(10). 3377–3400. 19 indexed citations

12.

Ho, Hao‐Che, et al.. (2019). Interdisciplinary application of numerical and machine-learning-based models to predict half-hourly suspended sediment concentrations during typhoons. Journal of Hydrology. 573. 661–675. 20 indexed citations

13.

Tung, Ching‐Pin, et al.. (2019). Development of a Novel Climate Adaptation Algorithm for Climate Risk Assessment. Water. 11(3). 497–497. 8 indexed citations

14.

Jhong, Bing-Chen, et al.. (2018). A Two-Stage Approach Integrating SOM- and MOGA-SVM-Based Algorithms to Forecast Spatial-temporal Groundwater Level with Meteorological Factors. Water Resources Management. 33(2). 797–818. 21 indexed citations

15.

Jhong, Bing-Chen & Ching‐Pin Tung. (2018). Evaluating Future Joint Probability of Precipitation Extremes with a Copula-Based Assessing Approach in Climate Change. Water Resources Management. 32(13). 4253–4274. 32 indexed citations

16.

Jhong, Bing-Chen, et al.. (2017). An integrated two-stage support vector machine approach to forecast inundation maps during typhoons. Journal of Hydrology. 547. 236–252. 53 indexed citations

17.

Jhong, Bing-Chen, et al.. (2016). Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics. Water Resources Management. 30(12). 4247–4271. 12 indexed citations

18.

Lin, Gwo‐Fong & Bing-Chen Jhong. (2014). A real-time forecasting model for the spatial distribution of typhoon rainfall. Journal of Hydrology. 521. 302–313. 41 indexed citations

19.

Lin, Gwo‐Fong, Bing-Chen Jhong, & Chia‐Chuan Chang. (2013). Development of an effective data-driven model for hourly typhoon rainfall forecasting. Journal of Hydrology. 495. 52–63. 42 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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