IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i9d10.1007_s11269-019-02299-4.html
   My bibliography  Save this article

Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models

Author

Listed:
  • Anas Mahmood Al-Juboori

    (University of Mosul)

Abstract

The data-driven techniques have gained more attention in stream flow prediction in recent years. In the current study, three different trees models (random forest, TreeBoost, and decision tree) were applied to predict the monthly stream flow for a river using the nearest river monthly stream flow data as external predictor variables. The cross-correlation function was used to select the optimum input predictor variables for the proposed models. A different scenario for selecting the optimal input predictor variables combination was studied. The performances of the models were evaluated by using root mean squared error and Nash and Sutcliffe coefficient indices. The Greater Zab River and the Lesser Zab River in Iraq were chosen as a case study to apply the proposed models. The monthly stream flow data for the Greater Zab River were generated using the monthly stream flow data for the Lesser Zab River, and the monthly stream flow data for the Lesser Zab River were generated using the monthly stream flow data for the Greater Zab River. The results showed a high performance of the random forest model to generate the monthly stream flow in comparing with the TreeBoost and decision tree models. The Nash and Sutcliffe coefficient is 0.84 and 0.89 in validating periods to generate monthly stream flow data using the random forest model for the Greater Zab River and the Lesser Zab River, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:9:d:10.1007_s11269-019-02299-4
    DOI: 10.1007/s11269-019-02299-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02299-4
    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/s11269-019-02299-4?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. Juran Ahmed & Arup Sarma, 2007. "Artificial neural network model for synthetic streamflow generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 1015-1029, June.
    2. 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.
    3. Sheng Yue & ChunYuan Wang, 2004. "The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(3), pages 201-218, June.
    4. 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.
    5. Manish Goyal, 2014. "Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1991-2003, May.
    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. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3227-3241, June.
    2. 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.
    3. Mehrdad Jeihouni & Ara Toomanian & Ali Mansourian, 2020. "Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 139-154, January.
    4. Xin Jing & Jungang Luo & Jingmin Wang & Ganggang Zuo & Na Wei, 2022. "A Multi-imputation Method to Deal With Hydro-Meteorological Missing Values by Integrating Chain Equations and Random Forest," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1159-1173, March.

    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. Zaher Mundher Yaseen & Minglei Fu & Chen Wang & Wan Hanna Melini Wan Mohtar & Ravinesh C. Deo & Ahmed El-shafie, 2018. "Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1883-1899, March.
    2. Rana Muhammad Adnan & Xiaohui Yuan & Ozgur Kisi & Muhammad Adnan & Asif Mehmood, 2018. "Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4469-4486, November.
    3. 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.
    4. 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.
    5. Miao Fang & Xin Li & Hans W. Chen & Deliang Chen, 2022. "Arctic amplification modulated by Atlantic Multidecadal Oscillation and greenhouse forcing on multidecadal to century scales," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    6. Mansoor Ahmed & Ghulam Hussain Dars & Suhail Ahmed & Nir Y. Krakauer, 2023. "Analyzing drought trends over Sindh Province, Pakistan," 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. 119(1), pages 643-661, October.
    7. Xiaqing Feng & Guangxin Zhang & Xiongrui Yin, 2011. "Hydrological Responses to Climate Change in Nenjiang River Basin, Northeastern China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 677-689, January.
    8. 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.
    9. 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.
    10. Hüseyin Yavuz & Saffet Erdoğan, 2012. "Spatial Analysis of Monthly and Annual Precipitation Trends in Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(3), pages 609-621, February.
    11. Dimitrios Myronidis & Konstantinos Ioannou & Dimitrios Fotakis & Gerald Dörflinger, 2018. "Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1759-1776, March.
    12. Yonas Ghile & Roland Schulze, 2010. "Evaluation of Three Numerical Weather Prediction Models for Short and Medium Range Agrohydrological Applications," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(5), pages 1005-1028, March.
    13. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    14. Wan-Jiun Chen, 2017. "Is the Green Solow Model Valid for $$\hbox {CO}_{2}$$ CO 2 Emissions in the European Union?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(1), pages 23-45, May.
    15. 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.
    16. 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.
    17. Roquia Salam & Abu Reza Md. Towfiqul Islam & Shakibul Islam, 2020. "Spatiotemporal distribution and prediction of groundwater level linked to ENSO teleconnection indices in the northwestern region of Bangladesh," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4509-4535, June.
    18. 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.
    19. Yi-min Wang & Jian-xia Chang & Qiang Huang, 2010. "Simulation with RBF Neural Network Model for Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2597-2610, September.
    20. 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.

    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:9:d:10.1007_s11269-019-02299-4. 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.