IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v30y2016i2d10.1007_s11269-015-1196-3.html
   My bibliography  Save this article

Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects

Author

Listed:
  • Chih-Chiang Wei

    (National Taiwan Ocean University)

  • Nien-Sheng Hsu

    (National Taiwan University)

  • Chien-Lin Huang

    (National Taiwan University)

Abstract

In meteorology and engineering, the prediction of quantitative precipitation and streamflow during typhoon events is a vital research topic. In Southern Taiwan, typhoons often occur in the summer. The interaction between the typhoon circulation and southwesterly monsoon flow frequently transports abundant moisture into Southern Taiwan leading to the substantial pouring rains. This study proposes a rainfall-runoff prediction methodology for addressing the complicated inflow forecasts of southwest monsoon rainfall during typhoons in the upper Tsengwen River in Southern Taiwan. This paper is novel in that it incorporates various data types (reservoir inflows, watershed rainfalls, typhoon information, and ground-weather characteristics) that were applied as model inputs. The most frequently used support vector regressions were employed to construct the rainfall-runoff models on the basis of three designed data combination scenarios. Typhoons Kalmaegi (2008), Fung-wong (2008), Jangmi (2008), and Morakot (2009) were used as validation typhoons. The model cases, involving lead times of 1 h to 6 h, were evaluated. Six performance criteria were used in the three scenarios to highlight the scenario capable of identifying the optimal performance level. In addition, this study compared the error rates between accumulation observations and accumulation predictions. The results showed that Scenario 3, which considered typhoon information and ground-weather characteristics simultaneously, had superior watershed rainfall and runoff predictions to those of the other scenarios. Thus, this study demonstrated the feasibility of using the proposed methodology to increase the accuracy of rainfall-runoff predictions.

Suggested Citation

  • Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.
  • Handle: RePEc:spr:waterr:v:30:y:2016:i:2:d:10.1007_s11269-015-1196-3
    DOI: 10.1007/s11269-015-1196-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-015-1196-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-015-1196-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kwan Lee & Wei-Chiao Hung & Chung-Chieh Meng, 2008. "Deterministic Insight into ANN Model Performance for Storm Runoff Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 67-82, January.
    2. S. Nadarajah & J. Shiau, 2005. "Analysis of Extreme Flood Events for the Pachang River, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 363-374, August.
    3. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
    4. Ching-Wen Chen & Chih-Chiang Wei & Hung-Jen Liu & Nien-Sheng Hsu, 2014. "Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2813-2832, August.
    5. Dushmanta Dutta & Wendy Welsh & Jai Vaze & Shaun Kim & David Nicholls, 2012. "A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4397-4415, December.
    6. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2014. "Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 425-444, January.
    7. Jaydip Makwana & Mukesh Tiwari, 2014. "Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4857-4873, October.
    8. Saman Razavi & Shahab Araghinejad, 2009. "Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(1), pages 39-55, January.
    9. Chih-Chiang Wei & Nien-Sheng Hsu, 2008. "Multireservoir Flood-Control Optimization with Neural-Based Linear Channel Level Routing Under Tidal Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(11), pages 1625-1647, November.
    10. Jehangir Awan & Deg-Hyo Bae, 2014. "Improving ANFIS Based Model for Long-term Dam Inflow Prediction by Incorporating Monthly Rainfall Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1185-1199, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chih-Chiang Wei, 2017. "Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast," Energies, MDPI, vol. 11(1), pages 1-23, December.
    2. Chih-Chiang Wei, 2020. "Real-time Extreme Rainfall Evaluation System for the Construction Industry Using Deep Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2787-2805, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.
    2. Anas Mahmood Al-Juboori, 2019. "Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3257-3270, July.
    3. Chuan Li & Yun Bai & Bo Zeng, 2016. "Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5145-5161, November.
    4. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    5. Xue-hua Zhao & Xu Chen, 2015. "Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2913-2926, June.
    6. X. Wang & R. Zhao & Y. Hao, 2011. "Flood Control Operations Based on the Theory of Variable Fuzzy Sets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(3), pages 777-792, February.
    7. Hsiao-Ping Wei & Yuan-Fong Su & Chao-Tzuen Cheng & Keh-Chia Yeh, 2020. "Levee Overtopping Risk Assessment under Climate Change Scenario in Kao-Ping River, Taiwan," Sustainability, MDPI, vol. 12(11), pages 1-12, June.
    8. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    9. Ali Suliman & Milad Jajarmizadeh & Sobri Harun & Intan Mat Darus, 2015. "Comparison of Semi-Distributed, GIS-Based Hydrological Models for the Prediction of Streamflow in a Large Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3095-3110, July.
    10. Mustafa Turan & Mehmet Yurdusev, 2014. "Predicting Monthly River Flows by Genetic Fuzzy Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4685-4697, October.
    11. Sheng He & Xuefeng Sang & Junxian Yin & Yang Zheng & Heting Chen, 2023. "Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 747-768, January.
    12. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2014. "Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 425-444, January.
    13. Manish Goyal & C. Ojha, 2011. "Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2177-2195, July.
    14. Anas Mahmood Al-Juboori, 2021. "A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 729-743, January.
    15. Sang Ug Kim & Cheol-Eung Lee, 2021. "Incorporation of Cost-Benefit Analysis Considering Epistemic Uncertainty for Calculating the Optimal Design Flood," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 757-774, January.
    16. Hamid Kardan Moghaddam & Saman Javadi & Timothy O. Randhir & Neda Kavehkar, 2022. "A Multi-Indicator, Non-Cooperative Game Model to Resolve Conflicts for Aquifer Restoration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5521-5543, November.
    17. Mahdi Soleimani Motlagh & Hoda Ghasemieh & Ali Talebi & Khodayar Abdollahi, 2017. "Identification and Analysis of Drought Propagation of Groundwater During Past and Future Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 109-125, January.
    18. Ervin Shan Khai Tiu & Yuk Feng Huang & Jing Lin Ng & Nouar AlDahoul & Ali Najah Ahmed & Ahmed Elshafie, 2022. "An evaluation of various data pre-processing techniques with machine learning models for water level prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 121-153, January.
    19. Sanjeet Kumar & Mukesh Tiwari & Chandranath Chatterjee & Ashok Mishra, 2015. "Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4863-4883, October.
    20. Desalegn Edossa & Mukand Babel, 2011. "Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin, Ethiopia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1759-1773, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:30:y:2016:i:2:d:10.1007_s11269-015-1196-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.