IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i4d10.1007_s11269-017-1581-1.html
   My bibliography  Save this article

An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models

Author

Listed:
  • Meral Buyukyildiz

    (University of Selcuk)

  • Serife Yurdagul Kumcu

    (University of Necmettin Erbakan)

Abstract

Sediment transport in streams and rivers takes two forms as suspended load and bed load. Suspended load comprises sand + silt + clay-sized particles that are held in suspension due to the turbulence and will only settle when the stream velocity decreases, such as when the streambed becomes flatter, or the streamflow into a pond or lake. The sources of the suspended sediments are the sediments transported from the river basin by runoff or wind and the eroded sediments of the river bed and banks. Suspended-sediment load is a key indicator for assessing the effect of land use changes, water quality studies and engineering practices in watercourses. Measuring suspended sediment in streams is real sampling and the collection process is both complex and expensive. In recent years, artificial intelligence methods have been used as a predictor for hydrological phenomenon namely to estimate the amount of suspended sediment. In this paper the abilities of Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) models among the artificial intelligence methods have been investigated to estimate the suspended sediment load (SSL) in Ispir Bridge gauging station on Coruh River (station number: 2316). Coruh River is located in the northern east part of Turkey and it is one of the world”s the fastest, the deepest and the largest rivers of the Coruh Basin. In this study, in order to estimate the suspended sediment load, different combinations of the streamflow and the SSL were used as the model inputs. Its results accuracy was compared with the results of conventional correlation coefficient analysis between input and output variables and the best combination was identified. Finally, in order to predict SSL, the SVM, ANFIS and various ANNs models were used. The reliability of SVM, ANFIS and ANN models were determined based on performance criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Efficiency Coefficient (EC) and Determination Coefficient (R2).

Suggested Citation

  • Meral Buyukyildiz & Serife Yurdagul Kumcu, 2017. "An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1343-1359, March.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:4:d:10.1007_s11269-017-1581-1
    DOI: 10.1007/s11269-017-1581-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-017-1581-1
    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/s11269-017-1581-1?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. Melesse, A.M. & Ahmad, S. & McClain, M.E. & Wang, X. & Lim, Y.H., 2011. "Suspended sediment load prediction of river systems: An artificial neural network approach," Agricultural Water Management, Elsevier, vol. 98(5), pages 855-866, March.
    2. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
    3. 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.
    4. Vahid Nourani & Farhad Alizadeh & Kiyoumars Roushangar, 2016. "Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 393-407, January.
    5. Vahid Nourani & Farhad Alizadeh & Kiyoumars Roushangar, 2016. "Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 393-407, January.
    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. Sarita Gajbhiye Meshram & Vijay P. Singh & Ozgur Kisi & Vahid Karimi & Chandrashekhar Meshram, 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4561-4575, December.
    2. You-Da Jhong & Hsin-Ping Lin & Chang-Shian Chen & Bing-Chen Jhong, 2022. "Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3221-3245, July.
    3. Xing-Yun Zou & Xin-Yu Peng & Xin-Xin Zhao & Chun-Ping Chang, 2023. "The impact of extreme weather events on water quality: international evidence," 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. 115(1), pages 1-21, January.
    4. Lubna Jamal Chachan, 2022. "Models for Predicting River Suspended Sediment Load Using Machine Learning: A Survey," Technium, Technium Science, vol. 4(1), pages 239-249.
    5. Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    6. 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.
    7. Laís Coelho Teixeira & Priscila Pacheco Mariani & Olavo Correa Pedrollo & Nilza Maria Castro & Vanessa Sari, 2020. "Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3709-3723, September.
    8. Bing-Chen Jhong & Hsi-Ting Fang & Cheng-Chia Huang, 2021. "Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with Multi-Objective Genetic Algorithm for Sediment Flux Prediction during Typhoons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2387-2408, June.
    9. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    10. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
    11. Shahab Araghinejad & Nima Fayaz & Seyed-Mohammad Hosseini-Moghari, 2018. "Development of a Hybrid Data Driven Model for Hydrological Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3737-3750, September.

    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. Mingxiang Yang & Hao Wang & Yunzhong Jiang & Xing Lu & Zhao Xu & Guangdong Sun, 2020. "GECA Proposed Ensemble–KNN Method for Improved Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 849-863, January.
    2. Asli Ulke & Gokmen Tayfur & Sevinc Ozkul, 2017. "Investigating a Suitable Empirical Model and Performing Regional Analysis for the Suspended Sediment Load Prediction in Major Rivers of the Aegean Region, Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 739-764, February.
    3. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    4. Ozgur Kisi & Mohammad Zounemat-Kermani, 2016. "Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3979-3994, September.
    5. Vahid Nourani & Mohammad Taghi Sattari & Amir Molajou, 2017. "Threshold-Based Hybrid Data Mining Method for Long-Term Maximum Precipitation Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2645-2658, July.
    6. Bing-Chen Jhong & Hsi-Ting Fang & Cheng-Chia Huang, 2021. "Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with Multi-Objective Genetic Algorithm for Sediment Flux Prediction during Typhoons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2387-2408, June.
    7. Kazi Ali Tamaddun & Ajay Kalra & Sajjad Ahmad, 2019. "Spatiotemporal Variation in the Continental US Streamflow in Association with Large-Scale Climate Signals Across Multiple Spectral Bands," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1947-1968, April.
    8. 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.
    9. Dong-Hyuk Kim & Ha-Yeong Kim & Ki-Hoon Moon & Jin-Hoon Jeong, 2022. "Prediction of Fracture Toughness of Intermediate Layer of Asphalt Pavements Using Artificial Neural Network," Sustainability, MDPI, vol. 14(13), pages 1-28, June.
    10. Guangyang Wu & Lanhai Li & Sajjad Ahmad & Xi Chen & Xiangliang Pan, 2013. "A Dynamic Model for Vulnerability Assessment of Regional Water Resources in Arid Areas: A Case Study of Bayingolin, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3085-3101, June.
    11. 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.
    12. Pankaj Bhardwaj & Tejpal Sharma & Omvir Singh, 2021. "Impact evaluation of watershed management programmes in Siwalik Himalayas of Haryana, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5251-5276, April.
    13. Bibhuti Bhusan Sahoo & Sovan Sankalp & Ozgur Kisi, 2023. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4271-4292, September.
    14. Thiago Victor Medeiros Nascimento & Celso Augusto Guimarães Santos & Camilo Allyson Simões Farias & Richarde Marques Silva, 2022. "Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2359-2377, May.
    15. Saad Sh. Sammen & T. A. Mohamed & A. H. Ghazali & L. M. Sidek & A. El-Shafie, 2017. "An evaluation of existent methods for estimation of embankment dam breach parameters," 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. 87(1), pages 545-566, May.
    16. Ayoub Zeroual & Mohamed Meddi & Ali A. Assani, 2016. "Artificial Neural Network Rainfall-Discharge Model Assessment Under Rating Curve Uncertainty and Monthly Discharge Volume Predictions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3191-3205, July.
    17. T. R. Rosin & M. Romano & E. Keedwell & Z. Kapelan, 2021. "A Committee Evolutionary Neural Network for the Prediction of Combined Sewer Overflows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1273-1289, March.
    18. Mostafa Mardani Najafabadi & Abbas Mirzaei & Hassan Azarm & Siamak Nikmehr, 2022. "Managing Water Supply and Demand to Achieve Economic and Environmental Objectives: Application of Mathematical Programming and ANFIS Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3007-3027, July.
    19. Hamid Moeeni & Hossein Bonakdari & Isa Ebtehaj, 2017. "Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2141-2156, May.
    20. Zhenfang He & Yaonan Zhang & Qingchun Guo & Xueru Zhao, 2014. "Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5297-5317, December.

    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:31:y:2017:i:4:d:10.1007_s11269-017-1581-1. 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.