IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v79y2008i1p94-103.html
   My bibliography  Save this article

Constructing neural network sediment estimation models using a data-driven algorithm

Author

Listed:
  • Kisi, Özgür

Abstract

Artificial neural network (ANN) models are designed for suspended sediment estimation using statistical pre-processing of the data. Statistical properties such as cross-, auto- and partial auto-correlation of the data series are used for identifying a unique input vector to the ANN that best represents the sediment estimation process for a basin. The methodology is evaluated using the flow and sediment data from the stations Quebrada Blanca and Rio Valenciano in USA. The result of the study indicates that the statistical pre-processing of the data could significantly reduce the effort and computational time required in developing an ANN model. Three ANN training algorithms are also compared with each other for the selected input vector.

Suggested Citation

  • Kisi, Özgür, 2008. "Constructing neural network sediment estimation models using a data-driven algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(1), pages 94-103.
    • Handle: RePEc:eee:matcom:v:79:y:2008:i:1:p:94-103
    DOI: 10.1016/j.matcom.2007.10.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475407002819
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2007.10.005?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. Linker, Raphael & Seginer, Ido, 2004. "Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 19-29.
    2. Maier, H.R. & Dandy, G.C., 1997. "Modelling cyanobacteria (blue-green algae) in the River Murray using artificial neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 43(3), pages 377-386.
    3. 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.
    4. Chaves, Paulo & Tsukatani, Tsuneo & Kojiri, Toshiharu, 2004. "Operation of storage reservoir for water quality by using optimization and artificial intelligence techniques," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 67(4), pages 419-432.
    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. Rana Muhammad Adnan Ikram & Leonardo Goliatt & Ozgur Kisi & Slavisa Trajkovic & Shamsuddin Shahid, 2022. "Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction," Mathematics, MDPI, vol. 10(16), pages 1-30, August.
    2. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1879-1897, May.
    3. Aytac Guven & Özgür Kişi, 2011. "Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 691-704, January.
    4. Alireza Moghaddam Nia & Debasmita Misra & Mahsa Hasanpour Kashani & Mohsen Ghafari & Madhumita Sahoo & Marzieh Ghodsi & Mohammad Tahmoures & Somayeh Taheri & Maryam Sadat Jaafarzadeh, 2023. "Runoff and Sediment Yield Processes in a Tropical Eastern Indian River Basin: A Multiple Machine Learning Approach," Land, MDPI, vol. 12(8), pages 1-15, August.
    5. Ozgur Kisi & Coskun Ozkan, 2017. "A New Approach for Modeling Sediment-Discharge Relationship: Local Weighted Linear Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 1-23, January.
    6. 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.
    7. 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.

    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. Bagher Shirmohammadi & Hamidreza Moradi & Vahid Moosavi & Majid Semiromi & Ali Zeinali, 2013. "Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran)," 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. 69(1), pages 389-402, October.
    2. Tsai, Wen-Ping & Cheng, Chung-Lien & Uen, Tinn-Shuan & Zhou, Yanlai & Chang, Fi-John, 2019. "Drought mitigation under urbanization through an intelligent water allocation system," Agricultural Water Management, Elsevier, vol. 213(C), pages 87-96.
    3. 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.
    4. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    5. Qing Gu & Ke Wang & Jiadan Li & Ligang Ma & Jinsong Deng & Kefeng Zheng & Xiaobin Zhang & Li Sheng, 2015. "Spatio-Temporal Trends and Identification of Correlated Variables with Water Quality for Drinking-Water Reservoirs," IJERPH, MDPI, vol. 12(10), pages 1-16, October.
    6. Alireza Dariane & Farzane Karami, 2014. "Deriving Hedging Rules of Multi-Reservoir System by Online Evolving Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3651-3665, September.
    7. F. Gonçalves & L. Costa & Helena Ramos, 2011. "ANN for Hybrid Energy System Evaluation: Methodology and WSS Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2295-2317, July.
    8. Andre Ferreira & Ramesh Teegavarapu, 2012. "Optimal and Adaptive Operation of a Hydropower System with Unit Commitment and Water Quality Constraints," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(3), pages 707-732, February.
    9. 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.
    10. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
    11. Gonçalves, F.V. & Costa, L.H. & Ramos, H.M., 2011. "Best economical hybrid energy solution: Model development and case study of a WDS in Portugal," Energy Policy, Elsevier, vol. 39(6), pages 3361-3369, June.
    12. Swades Pal, 2017. "Impact of Tilpara barrage on backwater reach of Kushkarni River: a tributary of Mayurakshi River," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(5), pages 2115-2142, October.
    13. Ashkan Shokri & Omid Bozorg Haddad & Miguel Mariño, 2014. "Multi-Objective Quantity–Quality Reservoir Operation in Sudden Pollution," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 567-586, January.
    14. Xu, Jiuping & Tao, Zhimiao, 2012. "A class of multi-objective equilibrium chance maximization model with twofold random phenomenon and its application to hydropower station operation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 85(C), pages 11-33.
    15. Germán Díaz-Flórez & Jorge Mendiola-Santibañez & Luis Solís-Sánchez & Domingo Gómez-Meléndez & Ivan Terol-Villalobos & Hector Gutiérrez-Bañuelos & Ma. Araiza-Esquivel & Gustavo Espinoza-García & Juan , 2019. "Modeling and Simulation of Temperature and Relative Humidity Inside a Growth Chamber," Energies, MDPI, vol. 12(21), pages 1-22, October.
    16. Song, Chenyu & Zhang, Haiping, 2020. "Study on turbidity prediction method of reservoirs based on long short term memory neural network," Ecological Modelling, Elsevier, vol. 432(C).
    17. Benya Suntaranont & Somrawee Aramkul & Manop Kaewmoracharoen & Paskorn Champrasert, 2020. "Water Irrigation Decision Support System for Practical Weir Adjustment Using Artificial Intelligence and Machine Learning Techniques," Sustainability, MDPI, vol. 12(5), pages 1-18, February.
    18. Onderka, Milan, 2007. "Correlations between several environmental factors affecting the bloom events of cyanobacteria in Liptovska Mara reservoir (Slovakia)—A simple regression model," Ecological Modelling, Elsevier, vol. 209(2), pages 412-416.
    19. Renata Graf & Viktor Vyshnevskyi, 2022. "Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions," Resources, MDPI, vol. 11(12), pages 1-24, November.

    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:eee:matcom:v:79:y:2008:i:1:p:94-103. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.