IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i3d10.1007_s11269-018-2177-0.html
   My bibliography  Save this article

Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models

Author

Listed:
  • Wei Li

    (Chongqing Water Resources and Electric Engineering College
    Huazhong University of Science and Technology)

  • Jianzhong Zhou

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Lu Chen

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Kuaile Feng

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Hairong Zhang

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Changqing Meng

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Na Sun

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

Abstract

The uncertainty research of hydrological forecast attracts the attention of a host of hydrological experts. Prediction Interval (PI) is a convinced method that can ensure the forecasting accuracy meanwhile take uncertainty range into consideration. While the existed Prediction Interval methods need algorithm optimization and are susceptible to local optima, so it is particularly urgent to provide an efficient Prediction Interval (PI) model with excellent performance. This paper proposes a novel upper and lower bound interval estimation model to rapidly define the PI and reduce the amount of calculation to implement convenient and high precise hydrological forecast. Above all, the ideal upper and lower bounds are defined according to the relative width or absolute width. Then, the proposed model is utilized to forecast interval runoff via least square method and multiple linear regression methods. The estimated interval inclusion ratio, interval width, symmetry, and root-mean-square error which are popular used to judge the precision serve as accuracy evaluation indexes. The measured discharge data from five hydrological stations which located upstream of the Yangtze River is applied for interval forecasting. Compared with the results of neural network-based upper and lower bound interval estimation model, the proposed method yields higher forecasting accuracy, meanwhile, the ideal upper and lower bounds successfully minimize the number of processes which require a mass of parameter searching and optimization.

Suggested Citation

  • Wei Li & Jianzhong Zhou & Lu Chen & Kuaile Feng & Hairong Zhang & Changqing Meng & Na Sun, 2019. "Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1203-1215, February.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:3:d:10.1007_s11269-018-2177-0
    DOI: 10.1007/s11269-018-2177-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-018-2177-0
    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/s11269-018-2177-0?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. Jun Guo & Jianzhong Zhou & Qiang Zou & Yi Liu & Lixiang Song, 2013. "A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2923-2946, June.
    2. Saeed Golian & Bahram Saghafian & Reza Maknoon, 2010. "Derivation of Probabilistic Thresholds of Spatially Distributed Rainfall for Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3547-3559, October.
    3. Xi Chen & Tao Yang & Xiaoyan Wang & Chong-Yu Xu & Zhongbo Yu, 2013. "Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1393-1409, March.
    4. Yan-Fang Sang, 2013. "Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2807-2821, June.
    5. Zhao Liu & Yiping Guo & Lixia Wang & Qing Wang, 2015. "Streamflow Forecast Errors and Their Impacts on Forecast-based Reservoir Flood Control," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(12), pages 4557-4572, September.
    6. Hong Li & Chong-Yu Xu & Stein Beldring & Lena Merete Tallaksen & Sharad K. Jain, 2016. "Water Resources Under Climate Change in Himalayan Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 843-859, January.
    7. Hong Li & Chong-Yu Xu & Stein Beldring & Lena Tallaksen & Sharad Jain, 2016. "Water Resources Under Climate Change in Himalayan Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 843-859, January.
    8. Juan Wu & Guihua Lu & Zhiyong Wu, 2014. "Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system," 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. 74(2), pages 325-340, November.
    9. Yousef Hassanzadeh & Amin Abdi & Siamak Talatahari & Vijay Singh, 2011. "Meta-Heuristic Algorithms for Hydrologic Frequency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(7), pages 1855-1879, May.
    10. Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    11. Fernando Mainardi Fan & Dirk Schwanenberg & Rodolfo Alvarado & Alberto Assis dos Reis & Walter Collischonn & Steffi Naumman, 2016. "Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3609-3625, August.
    12. Pao-Shan Yu & Tao-Chang Yang & Chen-Min Kuo & Yi-Tai Wang, 2014. "A Stochastic Approach for Seasonal Water-Shortage Probability Forecasting Based on Seasonal Weather Outlook," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 3905-3920, 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. Guan-jun Lei & Chang-shun Liu & Wenchuan Wang & Jun-xian Yin & Hao Wang, 2022. "Study on Ecological Allocation of Mine Water in Mining Area Based on Long-term Rainfall Forecast," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5545-5563, November.
    2. Xinyu Chang & Jun Guo & Hui Qin & Jingwei Huang & Xinying Wang & Pingan Ren, 2024. "Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3953-3972, August.

    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. Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.
    2. 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.
    3. Simmons, Aaron T. & Perovic, David J. & Roth, Guy, 2022. "Making waves – Are water scarcity footprints of irrigated agricultural commodities suitable to inform consumer decisions?," Agricultural Water Management, Elsevier, vol. 268(C).
    4. Chandra Lal Pandey, 2021. "Managing urban water security: challenges and prospects in Nepal," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 241-257, January.
    5. Shashidhar Kumar Jha & Ajeet Kumar Negi & Juha Mikael Alatalo & Vignesh Prabhu & Mani Bhushan Jha & Hemant Kumar, 2022. "Forest Degradation Index: A Tool for Forest Vulnerability Assessment in Indian Western Himalaya," Sustainability, MDPI, vol. 14(23), pages 1-29, November.
    6. Lu Zhuo & Dawei Han & Qiang Dai & Tanvir Islam & Prashant Srivastava, 2015. "Appraisal of NLDAS-2 Multi-Model Simulated Soil Moistures for Hydrological Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3503-3517, August.
    7. Crescenzo Pepe & Silvia Maria Zanoli, 2024. "Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review," Energies, MDPI, vol. 17(4), pages 1-35, February.
    8. Zhiqiang Jiang & Zhengyang Tang & Yi Liu & Yuyun Chen & Zhongkai Feng & Yang Xu & Hairong Zhang, 2019. "Area Moment and Error Based Forecasting Difficulty and its Application in Inflow Forecasting Level Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4553-4568, October.
    9. Jianzhong Zhou & Shuo Ouyang & Xuemin Wang & Lei Ye & Hao Wang, 2014. "Multi-Objective Parameter Calibration and Multi-Attribute Decision-Making: An Application to Conceptual Hydrological Model Calibration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 767-783, February.
    10. Naser Dehghanian & S. Saeid Mousavi Nadoushani & Bahram Saghafian & Ruhangiz Akhtari, 2019. "Performance Evaluation of a Fuzzy Hybrid Clustering Technique to Identify Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4621-4636, October.
    11. Changjun Liu & Liang Guo & Lei Ye & Shunfu Zhang & Yanzeng Zhao & Tianyu Song, 2018. "A review of advances in China’s flash flood early-warning system," 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. 92(2), pages 619-634, June.
    12. Suleman Sarwar & Ghazala Aziz & Daniel Balsalobre-Lorente, 2023. "Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    13. Jônatas Belotti & Hugo Siqueira & Lilian Araujo & Sérgio L. Stevan & Paulo S.G. de Mattos Neto & Manoel H. N. Marinho & João Fausto L. de Oliveira & Fábio Usberti & Marcos de Almeida Leone Filho & Att, 2020. "Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants," Energies, MDPI, vol. 13(18), pages 1-22, September.
    14. Milan Stojković & Srđan Kostić & Stevan Prohaska & Jasna Plavšić & Vesna Tripković, 2017. "A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1089-1103, March.
    15. Jenq-Tzong Shiau, 2021. "Analytical Water Shortage Probabilities and Distributions of Various Lead Times for a Water Supply Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3809-3825, September.
    16. Zhou, Jianzhong & Zhang, Yongchuan & Zhang, Rui & Ouyang, Shuo & Wang, Xuemin & Liao, Xiang, 2015. "Integrated optimization of hydroelectric energy in the upper and middle Yangtze River," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 481-512.
    17. Qin Tu & Hong Li & Xinkun Wang & Chao Chen, 2015. "Ant Colony Optimization for the Design of Small-Scale Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2323-2339, May.
    18. Vahid Nourani & Nardin Jabbarian Paknezhad & Hitoshi Tanaka, 2021. "Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    19. Anand Verdhen & Bhagu Chahar & Om Sharma, 2014. "Snowmelt Modelling Approaches in Watershed Models: Computation and Comparison of Efficiencies under Varying Climatic Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3439-3453, September.
    20. Pao-Shan Yu & Tao-Chang Yang & Chen-Min Kuo & Yi-Tai Wang, 2014. "A Stochastic Approach for Seasonal Water-Shortage Probability Forecasting Based on Seasonal Weather Outlook," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 3905-3920, September.

    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:3:d:10.1007_s11269-018-2177-0. 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.