Bing-Chen Jhong Journal papers（ 學術期刊論文 ）
1. Yang SY, Jhong YD, Jhong BC*, Fan-Chiang L, Tsai MC, 2025.10, 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, Vol. 659, 133230. https://doi.org/10.1016/j.jhydrol.2025.133230 (SCI)
2. Jhong BC*, Chen FW, Tung CP, 2025.04, Development of a real-time dynamic inundation risk assessment approach on paddy fields during typhoons: Exploration of adaptation strategies and quantification of risks, Journal of Environmental Management, Vol. 380, 124981. https://doi.org/10.1016/j.jenvman.2025.124981 (SCI)
3. Yang SY, Jhong YD, Jhong BC*, Lin YY, 2024.01, Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method, Water Resources Management, Vol. 38, pp. 1359–1380. https://doi.org/10.1007/s11269-023-03725-4 (SCI)
4. Jhong YD, Chen CS, Jhong BC, Tsai CH, Yang SY, 2024.01, Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm, Water Resources Management, Vol. 38, pp. 1141–1164. https://doi.org/10.1007/s11269-023-03713-8 (SCI)
5. Chiang LC, Shih PC, Lu CM, Jhong BC, 2023.11, Strategies analysis for improvement of SWAT model accuracy and representativeness of calibrated parameters in sediment simulation for various land use and climate conditions, Journal of Hydrology, 130124. https://doi.org/10.1016/j.jhydrol.2023.130124 (SCI)
6. Yang SY, Chen WT, Lin CH, Chang LF, Fang WT, Jhong BC*, 2023.08, 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. https://doi.org/10.1016/j.ejrh.2023.101452 (SCI)
7. Yang SY, Jhong BC, Jhong YD, Tsai TT, Chen CS, 2022.12, Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area, Natural Hazards, 1-23. https://doi.org/10.1007/s11069-022-05766-1 (SCI)
8. Jhong BC*, Lin CY, Jhong YD, Chang HK, Chu JL, Fang HT, 2022.07, Assessing the effective spatial characteristics of input features by physics-informed machine learning in inundation forecasting during typhoons, Hydrological Sciences Journal, Vol. 67, No. 10, pp. 1527–1545. https://doi.org/10.1080/02626667.2022.2092406 (SCI)
9. Jhong YD, Lin HP, Chen CS, Jhong BC*, 2022.06, 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, Vol. 36, pp. 3221–3245. https://doi.org/10.1007/s11269-022-03197-y (SCI)
10. Jhong BC, Fang HT, Huang CC, 2021.05, Assessment of the 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, Vol. 35, pp. 2387–2408. https://doi.org/10.1007/s11269-021-02832-4 (SCI)
11. Jhong BC*, Tachikawa Y, Tanaka T, Udmale P, Tung CP, 2020.09, 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, Vol. 12, No. 9, 2508. https://doi.org/10.3390/w12092508 (SCI) (MOST 108-2917-I-564 -020)
12. Jhong BC*, Huang J, Tung CP, 2019.06, Spatial assessment of climate risk for investigating climate adaptation strategies by evaluating spatial-temporal variability of extreme precipitation, Water Resources Management, Vol. 33, No. 10, pp. 3377–3400. https://doi.org/10.1007/s11269-019-02306-8 (SCI)
13. Huang CC, Fang HT, Ho HC, Jhong BC*, 2019.06, Interdisciplinary application of numerical and machine-learning based models to predict half-hourly suspended sediment concentrations during typhoons, Journal of Hydrology, Vol. 573, pp. 661–675. https://doi.org/10.1016/j.jhydrol.2019.04.001 (SCI)
14. Tung CP, Tsao JH, Tien YC, Lin CY, Jhong BC*, 2019.03, Development of a novel climate adaptation algorithm for climate risk assessment, Water, Vol. 11, No. 3, 497. https://doi.org/10.3390/w11030497 (SCI)
15. Fang HT, Jhong BC, Tan YC, Ke KY, 2018.12, A two-stage approach integrating SOM- and MOGA-SVM-based algorithms to forecast spatial-temporal groundwater level with meteorological factors, Water Resources Management, Vol. 33, No. 2, pp. 797–818. https://doi.org/10.1007/s11269-018-2143-x (SCI)
16. 童慶斌、曹榮軒、彭柏文、陳沛芫、李苑華、鍾秉宸，2018年11月。氣候智慧水資源核心研究。台灣土地研究，第21卷第2期，第181至208頁。(TSSCI)
17. Jhong BC*, Tung CP, 2018.07, Evaluating future joint probability of precipitation extremes with a copula-based assessing approach in climate change, Water Resources Management, Vol. 32, No. 13, pp. 4253–4274. http://dx.doi.org/10.1007/s11269-018-2045-y (SCI)
18. Wang JH, Lin GF, Jhong BC, 2018.01, Effective real-time forecasting of inundation maps for early warning systems during typhoons, MATEC Web of Conferences, Vol. 147, 03014. https://doi.org/10.1051/matecconf/201814703014
19. 方熙廷、鍾秉宸、譚義績、柯凱元、陳柏嘉，2017年12月。類神經網路於地下水位預測之時空分析。工程環境會刊，第37期，第112至128頁。
20. Jhong BC, Wang JH, Lin GF, 2017.04, An integrated two-stage support vector machine approach to forecast inundation maps during typhoons, Journal of Hydrology, Vol. 547, pp. 236–252. http://dx.doi.org/10.1016/j.jhydrol.2017.01.057 (SCI)
21. 王志煌、鍾秉宸、林國峰，2016年12月。颱風期間即時淹水地圖預報之研究。農業工程學報，第62卷第4期，第69至86頁。(EI)
22. Jhong BC, Wang JH, Lin GF, 2016.06.29, Improving the long lead-time inundation forecasts using effective typhoon characteristics, Water Resources Management, Vol. 30, Issues 12, pp. 4247–4271. http://dx.doi.org/10.1007/s11269-016-1418-3 (SCI)
23. Lin GF, Jhong BC, 2015.02, A real-time forecasting model for the spatial distribution of typhoon rainfall, Journal of Hydrology, Vol. 521, pp. 302–313. http://dx.doi.org/10.1016/j.jhydrol.2014.12.009 (SCI)
24. Lin GF, Jhong BC, Chang CC, 2013.07.12, Development of an effective data-driven model for hourly typhoon rainfall forecasting, Journal of Hydrology, Vol. 495, pp. 52–63. http://dx.doi.org/10.1016/j.jhydrol.2013.04.050 (SCI)
2. Jhong BC*, Chen FW, Tung CP, 2025.04, Development of a real-time dynamic inundation risk assessment approach on paddy fields during typhoons: Exploration of adaptation strategies and quantification of risks, Journal of Environmental Management, Vol. 380, 124981. https://doi.org/10.1016/j.jenvman.2025.124981 (SCI)
3. Yang SY, Jhong YD, Jhong BC*, Lin YY, 2024.01, Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method, Water Resources Management, Vol. 38, pp. 1359–1380. https://doi.org/10.1007/s11269-023-03725-4 (SCI)
4. Jhong YD, Chen CS, Jhong BC, Tsai CH, Yang SY, 2024.01, Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm, Water Resources Management, Vol. 38, pp. 1141–1164. https://doi.org/10.1007/s11269-023-03713-8 (SCI)
5. Chiang LC, Shih PC, Lu CM, Jhong BC, 2023.11, Strategies analysis for improvement of SWAT model accuracy and representativeness of calibrated parameters in sediment simulation for various land use and climate conditions, Journal of Hydrology, 130124. https://doi.org/10.1016/j.jhydrol.2023.130124 (SCI)
6. Yang SY, Chen WT, Lin CH, Chang LF, Fang WT, Jhong BC*, 2023.08, 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. https://doi.org/10.1016/j.ejrh.2023.101452 (SCI)
7. Yang SY, Jhong BC, Jhong YD, Tsai TT, Chen CS, 2022.12, Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area, Natural Hazards, 1-23. https://doi.org/10.1007/s11069-022-05766-1 (SCI)
8. Jhong BC*, Lin CY, Jhong YD, Chang HK, Chu JL, Fang HT, 2022.07, Assessing the effective spatial characteristics of input features by physics-informed machine learning in inundation forecasting during typhoons, Hydrological Sciences Journal, Vol. 67, No. 10, pp. 1527–1545. https://doi.org/10.1080/02626667.2022.2092406 (SCI)
9. Jhong YD, Lin HP, Chen CS, Jhong BC*, 2022.06, 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, Vol. 36, pp. 3221–3245. https://doi.org/10.1007/s11269-022-03197-y (SCI)
10. Jhong BC, Fang HT, Huang CC, 2021.05, Assessment of the 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, Vol. 35, pp. 2387–2408. https://doi.org/10.1007/s11269-021-02832-4 (SCI)
11. Jhong BC*, Tachikawa Y, Tanaka T, Udmale P, Tung CP, 2020.09, 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, Vol. 12, No. 9, 2508. https://doi.org/10.3390/w12092508 (SCI) (MOST 108-2917-I-564 -020)
12. Jhong BC*, Huang J, Tung CP, 2019.06, Spatial assessment of climate risk for investigating climate adaptation strategies by evaluating spatial-temporal variability of extreme precipitation, Water Resources Management, Vol. 33, No. 10, pp. 3377–3400. https://doi.org/10.1007/s11269-019-02306-8 (SCI)
13. Huang CC, Fang HT, Ho HC, Jhong BC*, 2019.06, Interdisciplinary application of numerical and machine-learning based models to predict half-hourly suspended sediment concentrations during typhoons, Journal of Hydrology, Vol. 573, pp. 661–675. https://doi.org/10.1016/j.jhydrol.2019.04.001 (SCI)
14. Tung CP, Tsao JH, Tien YC, Lin CY, Jhong BC*, 2019.03, Development of a novel climate adaptation algorithm for climate risk assessment, Water, Vol. 11, No. 3, 497. https://doi.org/10.3390/w11030497 (SCI)
15. Fang HT, Jhong BC, Tan YC, Ke KY, 2018.12, A two-stage approach integrating SOM- and MOGA-SVM-based algorithms to forecast spatial-temporal groundwater level with meteorological factors, Water Resources Management, Vol. 33, No. 2, pp. 797–818. https://doi.org/10.1007/s11269-018-2143-x (SCI)
16. 童慶斌、曹榮軒、彭柏文、陳沛芫、李苑華、鍾秉宸，2018年11月。氣候智慧水資源核心研究。台灣土地研究，第21卷第2期，第181至208頁。(TSSCI)
17. Jhong BC*, Tung CP, 2018.07, Evaluating future joint probability of precipitation extremes with a copula-based assessing approach in climate change, Water Resources Management, Vol. 32, No. 13, pp. 4253–4274. http://dx.doi.org/10.1007/s11269-018-2045-y (SCI)
18. Wang JH, Lin GF, Jhong BC, 2018.01, Effective real-time forecasting of inundation maps for early warning systems during typhoons, MATEC Web of Conferences, Vol. 147, 03014. https://doi.org/10.1051/matecconf/201814703014
19. 方熙廷、鍾秉宸、譚義績、柯凱元、陳柏嘉，2017年12月。類神經網路於地下水位預測之時空分析。工程環境會刊，第37期，第112至128頁。
20. Jhong BC, Wang JH, Lin GF, 2017.04, An integrated two-stage support vector machine approach to forecast inundation maps during typhoons, Journal of Hydrology, Vol. 547, pp. 236–252. http://dx.doi.org/10.1016/j.jhydrol.2017.01.057 (SCI)
21. 王志煌、鍾秉宸、林國峰，2016年12月。颱風期間即時淹水地圖預報之研究。農業工程學報，第62卷第4期，第69至86頁。(EI)
22. Jhong BC, Wang JH, Lin GF, 2016.06.29, Improving the long lead-time inundation forecasts using effective typhoon characteristics, Water Resources Management, Vol. 30, Issues 12, pp. 4247–4271. http://dx.doi.org/10.1007/s11269-016-1418-3 (SCI)
23. Lin GF, Jhong BC, 2015.02, A real-time forecasting model for the spatial distribution of typhoon rainfall, Journal of Hydrology, Vol. 521, pp. 302–313. http://dx.doi.org/10.1016/j.jhydrol.2014.12.009 (SCI)
24. Lin GF, Jhong BC, Chang CC, 2013.07.12, Development of an effective data-driven model for hourly typhoon rainfall forecasting, Journal of Hydrology, Vol. 495, pp. 52–63. http://dx.doi.org/10.1016/j.jhydrol.2013.04.050 (SCI)
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