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Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics

Author

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  • Bing-Chen Jhong

    (National Taiwan University)

  • Jhih-Huang Wang

    (National Taiwan University)

  • Gwo-Fong Lin

    (National Taiwan University)

Abstract

In this paper, a new type of inundation forecasting model with the effective typhoon characteristics is proposed by integrating support vector machine (SVM) with multi-objective genetic algorithm (MOGA). Firstly, a comparison of the proposed model and an existing model based on back-propagation network (BPN) is made to highlight the improvement in forecasting performance. Next, the proposed model is compared with the SVM-based model without typhoon characteristics to investigate the influence of typhoon characteristics on inundation forecasting. Effective typhoon characteristics for improving forecasting performance are identified as well. An application to Chiayi City, Taiwan, is conducted to demonstrate the superiority of the proposed model. The results confirm that the proposed model with the effective typhoon characteristics does improve the forecasting performance and the improvement increases with increasing lead-time, especially for long lead-time forecasting. The proposed model is capable of optimizing the input to decrease the negative impact when increasing forecast lead time. In conclusion, effective typhoon characteristics are recommended as key inputs for inundation forecasting during typhoons.

Suggested Citation

  • Bing-Chen Jhong & Jhih-Huang Wang & Gwo-Fong Lin, 2016. "Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4247-4271, September.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:12:d:10.1007_s11269-016-1418-3
    DOI: 10.1007/s11269-016-1418-3
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    References listed on IDEAS

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    1. S. Mohanty & Madan Jha & S. Raul & R. Panda & K. Sudheer, 2015. "Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5521-5532, December.
    2. Aman Mohammad Kalteh, 2016. "Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 747-766, January.
    3. Aman Kalteh, 2016. "Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 747-766, January.
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    Cited by:

    1. Hongfa Wang & Xinjian Guan & Yu Meng & Zening Wu & Kun Wang & Huiliang Wang, 2023. "Coupling Time and Non-Time Series Models to Simulate the Flood Depth at Urban Flooded Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1275-1295, February.
    2. Cheng-Chia Huang, 2024. "Navigating Reservoir Deposition Challenges: Evaluation of Reservoir Desilting Strategy Through a 4-Stage Life Cycle Assessment Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3937-3952, August.
    3. Song-Yue Yang & Bing-Chen Jhong & You-Da Jhong & Tsung-Tang Tsai & Chang-Shian Chen, 2023. "Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area," 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. 116(2), pages 2339-2361, March.
    4. Bing-Chen Jhong & Hsi-Ting Fang & Cheng-Chia Huang, 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: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2387-2408, June.

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