IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i13d10.1007_s11269-022-03292-0.html
   My bibliography  Save this article

Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling

Author

Listed:
  • Farshid Rezaei

    (K. N. Toosi University of Technology)

  • Rezvane Ghorbani

    (K. N. Toosi University of Technology)

  • Najmeh Mahjouri

    (K. N. Toosi University of Technology)

Abstract

In this paper, daily and monthly runoff discharge forecasts are improved by developing an ensemble model based on the Bayesian maximum entropy (BME), which integrates the outcomes of several single-source simulation models. The performance of the developed ensemble model in daily and monthly discharge forecasting is evaluated by comparing its results with those obtained from 12 other ensemble models, including simple averaging (SA), weighted averaging (WA), Bayesian model averaging (BMA), the best model in last time step (BMLS), artificial neural network (ANN), artificial neural network-based on differenced data, error analysis (EA), wavelet-artificial neural network (Wavelet-ANN), multi-model super ensemble (MMSE), modified multi-model super ensemble (MMMSE), Kriging and an ensemble model based on K-means clustering. Six models, namely artificial neural network (ANN), auto-regressive integrated moving average (ARIMA), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), M5tree, and Wavelet-ANN, are used as single-source forecasting models. These models provide the inputs of the ensemble models. After analyzing different hydrologic variables, the best set of predictors is chosen for the single-source models. Then, the single-source models are calibrated and validated and the ones with the best performances are selected for being used in the ensemble phase. The performance of the models is evaluated by applying them to two different case studies of the Liqvan watershed, a sub-watershed of Lake Urmia catchment in the north-west of Iran, and the Latyan watershed, in the east of Tehran, Iran. The results show that the ensemble models, especially the BME and ANN-based ensemble models, significantly improve both monthly and daily river discharge forecasts. The values of the performance criteria of Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) in forecasting the extreme values of monthly runoff discharge, using the best single-source model, are respectively 0.787 and 0.869. The corresponding values for the best ensemble model are 0.933 and 0.962, respectively. For the daily runoff discharge forecast, the values of the mentioned performance criteria for the best single-source model are respectively 0.874 and 0.877, while corresponding values for the best ensemble model are 0.927 and 0.935, respectively.

Suggested Citation

  • Farshid Rezaei & Rezvane Ghorbani & Najmeh Mahjouri, 2022. "Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5063-5089, October.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:13:d:10.1007_s11269-022-03292-0
    DOI: 10.1007/s11269-022-03292-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03292-0
    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/s11269-022-03292-0?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. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Mohammad Nikoo & Najmeh Mahjouri, 2013. "Water Quality Zoning Using Probabilistic Support Vector Machines and Self-Organizing Maps," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2577-2594, May.
    3. Huaping Huang & Zhongmin Liang & Binquan Li & Dong Wang & Yiming Hu & Yujie Li, 2019. "Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3321-3338, July.
    4. Qiang Zhang & Ben-De Wang & Bin He & Yong Peng & Ming-Lei Ren, 2011. "Singular Spectrum Analysis and ARIMA 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. 25(11), pages 2683-2703, 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. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.
    2. M. Rajesh & Sachdeva Anishka & Pansari Satyam Viksit & Srivastav Arohi & S. Rehana, 2023. "Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 75-90, January.

    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. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
    2. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
    3. Jinyu Meng & Zengchuan Dong & Yiqing Shao & Shengnan Zhu & Shujun Wu, 2022. "Monthly Runoff Forecasting Based on Interval Sliding Window and Ensemble Learning," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    4. He, Chaofei & Chen, Fulong & Long, Aihua & Qian, YuXia & Tang, Hao, 2023. "Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area," Agricultural Water Management, Elsevier, vol. 279(C).
    5. Gavin Boyd & Dain Na & Zhong Li & Spencer Snowling & Qianqian Zhang & Pengxiao Zhou, 2019. "Influent Forecasting for Wastewater Treatment Plants in North America," Sustainability, MDPI, vol. 11(6), pages 1-14, March.
    6. Wei Li & Xiaosheng Wang & Shujiang Pang & Haiying Guo, 2022. "A Runoff Prediction Model Based on Nonhomogeneous Markov Chain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1431-1442, March.
    7. 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.
    8. 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.
    9. Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
    10. Mehrdad Ghorbani Mooselu & Hamid Amiri & Sama Azadi & Helge Liltved, 2022. "Spatiotemporal assessment of surface water vulnerability to road construction," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 7851-7873, June.
    11. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    12. Vidhi Vig & Anmol Kaur, 2022. "Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2920-2933, December.
    13. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Lin Qiu & Can-can Liu, 2017. "The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 461-477, January.
    14. Baoying Shan & Ping Guo & Shanshan Guo & Zhong Li, 2019. "A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    15. Liangxu Liu & Xueyong Zhao & Qinglan Meng & He Zhao & Xiaoqian Lu & Junkai Gao & Xueli Chang, 2017. "Annual Precipitation Fluctuation and Spatial Differentiation Characteristics of the Horqin Region," Sustainability, MDPI, vol. 9(1), pages 1-16, January.
    16. Y. Liu & B. Wang & H. Zhan & Y. Fan & Y. Zha & Y. Hao, 2017. "Simulation of Nonstationary Spring Discharge Using Time Series Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4875-4890, December.
    17. Ali Danandeh Mehr & Vahid Nourani, 2018. "Season Algorithm-Multigene Genetic Programming: A New Approach for Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2665-2679, June.
    18. 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.
    19. Fu Qiao, 2020. "Study on Price Fluctuation of Industry Index in Chinas Stock Market Based on Empirical Mode Decomposition," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(5), pages 559-573, May.
    20. Parisa-Sadat Ashofteh & Taher Rajaee & Parvin Golfam, 2017. "Assessment of Water Resources Development Projects under Conditions of Climate Change Using Efficiency Indexes (EIs)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3723-3744, 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:36:y:2022:i:13:d:10.1007_s11269-022-03292-0. 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.