IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v32y2018i10d10.1007_s11269-018-1998-1.html
   My bibliography  Save this article

Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods

Author

Listed:
  • Sinan Jasim Hadi

    (Anadolu University)

  • Mustafa Tombul

    (Anadolu University)

Abstract

Modelling streamflow is essential for activities, such as flood control, drought mitigation, and water resources utilization and management. Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM) are techniques that are frequently used in hydrology to specifically model streamflow. This study compares the accuracy of ANN, ANFIS, and SVM in forecasting the daily streamflow with the traditional approach known as autoregressive (AR) model for basins with different physical characteristics. The accuracies of the models are compared for three basins, that is, 1801, 1805, and 1822, at the Seyhan River Basin in Turkey. The comparison was performed by using coefficient of efficiency, index of agreement, and root-mean-square error. Results indicate that ANN and ANFIS are more accurate than AR and SVM for all the basins. ANN and ANFIS perform similarly, while ANN outperformed ANFIS. Among the models used, the ANN demonstrates the highest performance in forecasting the peak flood values. This study also finds that physical characteristics, such as small area, high slope, and high elevation variation, and streamflow variance deteriorate the accuracy of the methods.

Suggested Citation

  • Sinan Jasim Hadi & Mustafa Tombul, 2018. "Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3405-3422, August.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:10:d:10.1007_s11269-018-1998-1
    DOI: 10.1007/s11269-018-1998-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-018-1998-1
    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/s11269-018-1998-1?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. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    2. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
    3. Ozgur Kisi & Alireza Nia & Mohsen Gosheh & Mohammad Tajabadi & Azadeh Ahmadi, 2012. "Intermittent Streamflow Forecasting by Using Several Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 457-474, January.
    4. Mehdi Rezaeian Zadeh & Seifollah Amin & Davar Khalili & Vijay Singh, 2010. "Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2673-2688, September.
    5. Firat, Mahmut & Güngör, Mahmud, 2007. "River flow estimation using adaptive neuro fuzzy inference system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 75(3), pages 87-96.
    6. Seyed Akrami & Vahid Nourani & S. Hakim, 2014. "Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2999-3018, August.
    7. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    8. S. Aggarwal & Arun Goel & Vijay Singh, 2012. "Stage and Discharge Forecasting by SVM and ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3705-3724, October.
    9. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    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. Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
    2. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
    3. Yutao Qi & Zhanao Zhou & Lingling Yang & Yining Quan & Qiguang Miao, 2019. "A Decomposition-Ensemble Learning Model Based on LSTM Neural Network for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4123-4139, September.
    4. Maryam Rahimzad & Alireza Moghaddam Nia & Hosam Zolfonoon & Jaber Soltani & Ali Danandeh Mehr & Hyun-Han Kwon, 2021. "Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4167-4187, September.
    5. Fugang LI & Guangwen MA & Shijun CHEN & Weibin HUANG, 2021. "An Ensemble Modeling Approach to Forecast Daily Reservoir Inflow Using Bidirectional Long- and Short-Term Memory (Bi-LSTM), Variational Mode Decomposition (VMD), and Energy Entropy Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2941-2963, July.
    6. Quoc Bao Pham & S. I. Abba & Abdullahi Garba Usman & Nguyen Thi Thuy Linh & Vivek Gupta & Anurag Malik & Romulus Costache & Ngoc Duong Vo & Doan Quang Tri, 2019. "Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5067-5087, December.
    7. Fang-Fang Li & Han Cao & Chun-Feng Hao & Jun Qiu, 2021. "Daily Streamflow Forecasting Based on Flow Pattern Recognition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4601-4620, October.

    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. Babak Mohammadi & Farshad Ahmadi & Saeid Mehdizadeh & Yiqing Guan & Quoc Bao Pham & Nguyen Thi Thuy Linh & Doan Quang Tri, 2020. "Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3387-3409, August.
    2. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4661-4679, November.
    3. Mayank Suman & Rajib Maity, 2019. "Assessment of Streamflow Variability with Upgraded HydroClimatic Conceptual Streamflow Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1367-1382, March.
    4. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    5. Maya Rajnarayan Ray & Arup Kumar Sarma, 2016. "Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4695-4711, October.
    6. Saeid Mehdizadeh & Ali Kozekalani Sales, 2018. "A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3001-3022, July.
    7. Samad Emamgholizadeh & Khadije Moslemi & Gholamhosein Karami, 2014. "Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and 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. 28(15), pages 5433-5446, December.
    8. Huantian Xie & Dingfang Li & Lihua Xiong, 2016. "Exploring the Regional Variance using ARMA-GARCH Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3507-3518, August.
    9. Changsam Jeong & Ju-Young Shin & Taesoon Kim & Jun-Haneg Heo, 2012. "Monthly Precipitation Forecasting with a Neuro-Fuzzy Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4467-4483, December.
    10. 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.
    11. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
    12. Mohammad R. Hassanvand & Hojat Karami & Sayed-Farhad Mousavi, 2018. "Investigation of neural network and fuzzy inference neural network and their optimization using meta-algorithms in river flood routing," 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. 94(3), pages 1057-1080, December.
    13. Wen-Ping Tsai & Yen-Ming Chiang & Jun-Lin Huang & Fi-John Chang, 2016. "Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4789-4806, October.
    14. 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.
    15. 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.
    16. Xianming Dou & Yongguo Yang & Jinhui Luo, 2018. "Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements," Sustainability, MDPI, vol. 10(1), pages 1-26, January.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:32:y:2018:i:10:d:10.1007_s11269-018-1998-1. 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.