IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i11d10.1007_s11269-019-02342-4.html
   My bibliography  Save this article

Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks

Author

Listed:
  • Jhih-Huang Wang

    (National Taiwan University)

  • Gwo-Fong Lin

    (National Taiwan University)

  • Ming-Jui Chang

    (National Taiwan University)

  • I-Hang Huang

    (National Taiwan University)

  • Yu-Ren Chen

    (National Taiwan University)

Abstract

Accurate forecasts of hourly water levels during typhoons are crucial to disaster emergency response. To mitigate flood damage, the development of a water-level forecasting model has played an essential role. We propose a model based on a dilated causal convolutional neural network (DCCNN) that can yield water-level forecasts with lead times of 1- to 6-h. A DCCNN model can efficiently exploit a broad-range history. Residual and skip connections are also applied throughout the network to enable training of deeper networks and to accelerate convergence. To demonstrate the superiority of the proposed forecasting technique, we applied it to a dataset of 16 typhoon events that occurred during the years 2012–2017 in the Yilan River basin in Taiwan. In order to examine the efficiency of the improved methodology, we also compared the proposed model with two existing models that were based on the multilayer perceptron (MLP) and the support vector machine (SVM). The results indicate that a DCCNN-based model is superior to both the SVM and MLP models, especially for modeling peak water levels. Much of the performance improvement of the proposed model is due to its ability to provide water-level forecasts with a long lead time. The proposed model is expected to be particularly useful in support of disaster warning systems.

Suggested Citation

  • Jhih-Huang Wang & Gwo-Fong Lin & Ming-Jui Chang & I-Hang Huang & Yu-Ren Chen, 2019. "Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3759-3780, September.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:11:d:10.1007_s11269-019-02342-4
    DOI: 10.1007/s11269-019-02342-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02342-4
    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/s11269-019-02342-4?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. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3803-3823, August.
    2. 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.
    3. Vanessa Sari & Nilza Maria Reis Castro & Olavo Correa Pedrollo, 2017. "Estimate of Suspended Sediment Concentration from Monitored Data of Turbidity and Water Level Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4909-4923, December.
    4. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4113-4113, September.
    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. Jun-Mao Liao & Ming-Jui Chang & Luh-Maan Chang, 2020. "Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques," Energies, MDPI, vol. 13(7), pages 1-22, April.
    2. Jiun-Huei Jang & Kun-Fang Lee & Jin-Cheng Fu, 2022. "Improving River-Stage Forecasting Using Hybrid Models Based on the Combination of Multiple Additive Regression Trees and Runge–Kutta Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1123-1140, February.
    3. 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.
    4. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," 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. 103(3), pages 2631-2689, September.
    5. Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention," Energies, MDPI, vol. 13(7), pages 1-29, April.
    6. Xingsheng Shu & Wei Ding & Yong Peng & Ziru Wang & Jian Wu & Min Li, 2021. "Monthly Streamflow Forecasting Using Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5089-5104, December.

    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. Ali Nouh Mabdeh & A’kif Al-Fugara & Khaled Mohamed Khedher & Muhammed Mabdeh & Abdel Rahman Al-Shabeeb & Rida Al-Adamat, 2022. "Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms," Sustainability, MDPI, vol. 14(15), pages 1-26, August.
    2. Mirzaei, Mohsen & Jafari, Ali & Gholamalifard, Mehdi & Azadi, Hossein & Shooshtari, Sharif Joorabian & Moghaddam, Saghi Movahhed & Gebrehiwot, Kindeya & Witlox, Frank, 2020. "Mitigating environmental risks: Modeling the interaction of water quality parameters and land use cover," Land Use Policy, Elsevier, vol. 95(C).
    3. Ahmet Cemkut Badem & Recep Yılmaz & Muhammet Raşit Cesur & Elif Cesur, 2024. "Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
    4. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    5. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    6. Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
    7. Shuheng Wang & Guohao Li & Yifan Bao, 2018. "A novel improved fuzzy support vector machine based stock price trend forecast model," Papers 1801.00681, arXiv.org.
    8. Sina Paryani & Mojgan Bordbar & Changhyun Jun & Mahdi Panahi & Sayed M. Bateni & Christopher M. U. Neale & Hamidreza Moeini & Saro Lee, 2023. "Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran," 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(1), pages 837-868, March.
    9. Sri Lakshmi Sesha Vani Jayanthi & Venkata Reddy Keesara & Venkataramana Sridhar, 2022. "Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    10. Vivien Lai & Ali Najah Ahmed & M.A. Malek & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Ahmed El-Shafie & Amr El-Shafie, 2019. "Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms," Sustainability, MDPI, vol. 11(17), pages 1-26, August.
    11. Ahmed El-Shafie & Amr El-Shafie & Muhammad Mukhlisin, 2014. "New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2093-2107, June.
    12. Ozgur Kisi, 2015. "Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5109-5127, November.
    13. Zhenzhu Meng & Yiren Wang & Sen Zheng & Xiao Wang & Dan Liu & Jinxin Zhang & Yiting Shao, 2024. "Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm," Mathematics, MDPI, vol. 12(9), pages 1-18, April.
    14. Yashon O. Ouma & Ditiro B. Moalafhi & George Anderson & Boipuso Nkwae & Phillimon Odirile & Bhagabat P. Parida & Jiaguo Qi, 2022. "Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors," Sustainability, MDPI, vol. 14(22), pages 1-31, November.
    15. Mohammed Falah Allawi & Ahmed El-Shafie, 2016. "Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4773-4788, October.
    16. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    17. Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
    18. Raúl Parada & Jordi Font & Jordi Casas-Roma, 2019. "Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs," Energies, MDPI, vol. 12(10), pages 1-21, May.
    19. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    20. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.

    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:33:y:2019:i:11:d:10.1007_s11269-019-02342-4. 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.