IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i1d10.1007_s11269-016-1518-0.html
   My bibliography  Save this article

RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers

Author

Listed:
  • Mohammed Seyam

    (University College of Technology Sarawak)

  • Faridah Othman

    (University of Malaya)

  • Ahmed El-Shafie

    (University of Malaya)

Abstract

Lag time (Lt) reflects the speed at which a river basin responds to rainfall (RF) events and is influenced by many hydrological parameters such as RF and stream flow (SF). These two parameters are represented by four variables, namely peak RF intensity, previous 48-h rainfall, peak SF and previous 48-h SF. In fact, lag time is highly stochastic in nature and its relation with the mentioned four variables is highly nonlinear interrelationship. The main objective of this study is to develop a model to estimate the Lt between upstream and downstream stations in tropical humid rivers. The graphical hydrological approach (HGA) has been used to estimate the Lt based on 95 RF-SF and considered as the references value for the proposed model evaluation. Linear, non-linear and Radial Basis Function Neural Network (RBFNN) methods have been developed successfully for Selangor River basin. The results show that the RBFNN outperformed the linear and the non-linear model and could achieve correlation coefficient (r) between the observed Lt and predicted Lt equal to 0.979 while r for the linear and the non-linear model equal to 0.519 and 0.631, receptively. Furthermore, the RBFNN model could attain minimum root mean square error (RMSE) between the observed Lt and predicted Lt equal to 1.23 while RMSE for the linear and the non-linear model equal to 1.9 and 2.02, receptively. The proposed RBFNN model significantly abridges the estimation of Lt values and avoids the essential need for comprehensive description of all parameters affecting on its value.

Suggested Citation

  • Mohammed Seyam & Faridah Othman & Ahmed El-Shafie, 2017. "RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 187-204, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1518-0
    DOI: 10.1007/s11269-016-1518-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-016-1518-0
    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/s11269-016-1518-0?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. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.
    2. Mohammed Seyam & Faridah Othman, 2014. "The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2583-2597, July.
    3. Mallikarjuna Perugu & Aruna Singam & Chandra Kamasani, 2013. "Multiple Linear Correlation Analysis of Daily Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1489-1500, March.
    4. Ahmed El-Shafie & Alaa Abdin & Aboelmagd Noureldin & Mohd Taha, 2009. "Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2289-2315, September.
    5. Seyed Akrami & Ahmed El-Shafie & Othman Jaafar, 2013. "Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3507-3523, July.
    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. Sandeep Samantaray & Abinash Sahoo, 2024. "Prediction of flow discharge in Mahanadi River Basin, India, based on novel hybrid SVM approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 18699-18723, July.
    2. Xinyu Wan & Qingyan Yang & Peng Jiang & Ping’an Zhong, 2019. "A Hybrid Model for Real-Time Probabilistic Flood Forecasting Using Elman Neural Network with Heterogeneity of Error Distributions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 4027-4050, 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. Mohammed Falah Allawi & Ahmed El-Shafie, 2016. "Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4773-4788, October.
    2. Mohammad Valipour, 2014. "Use of average data of 181 synoptic stations for estimation of reference crop evapotranspiration by temperature-based methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4237-4255, September.
    3. Valipour, Mohammad & Gholami Sefidkouhi, Mohammad Ali & Raeini−Sarjaz, Mahmoud, 2017. "Selecting the best model to estimate potential evapotranspiration with respect to climate change and magnitudes of extreme events," Agricultural Water Management, Elsevier, vol. 180(PA), pages 50-60.
    4. 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.
    5. Kulwinder Parmar & Rashmi Bhardwaj, 2015. "River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(1), pages 17-33, January.
    6. Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    7. Suiling Wang & Zhiqiang Jiang & Hairong Zhang, 2022. "Correction of Reservoir Runoff Forecast Based on Multi-scenario Division and Multi Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5277-5296, October.
    8. Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
    9. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    10. Mohammed Seyam & Faridah Othman, 2014. "The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2583-2597, July.
    11. Vinay Kumar Gautam & Chaitanya B. Pande & Kanak N. Moharir & Abhay M. Varade & Nitin Liladhar Rane & Johnbosco C. Egbueri & Fahad Alshehri, 2023. "Prediction of Sodium Hazard of Irrigation Purpose using Artificial Neural Network Modelling," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    12. David Robertson & Q. Wang, 2013. "Seasonal Forecasts of Unregulated Inflows into the Murray River, Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2747-2769, June.
    13. J. Villanueva & P. Coustumer & F. Huneau & M. Motelica-Heino & T.R. Perez & R. Materum & M.V.O. Espaldon & S. Stoll, 2013. "Assessment of Trace Metals during Episodic Events using DGT Passive Sampler: A Proposal for Water Management Enhancement," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(12), pages 4163-4181, September.
    14. Mohammad Dorofki & Ahmed Elshafie & Othman Jaafar & Othman Karim & Sharifah Abdullah, 2014. "A GIS-ANN-Based Approach for Enhancing the Effect of Slope in the Modified Green-Ampt Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 391-406, January.
    15. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
    16. Guang Yang & Shenglian Guo & Pan Liu & Xiaofeng Liu & Jiabo Yin, 2020. "Heuristic Input Variable Selection in Multi-Objective Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 617-636, January.
    17. Ozgur Kisi & Mohammad Zounemat-Kermani, 2014. "Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2655-2675, July.
    18. Pavitra Kumar & Sai Hin Lai & Jee Khai Wong & Nuruol Syuhadaa Mohd & Md Rowshon Kamal & Haitham Abdulmohsin Afan & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, vol. 12(11), pages 1-26, May.
    19. Ozgur Kisi & Armin Azad & Hamed Kashi & Amir Saeedian & Seyed Ali Asghar Hashemi & Salar Ghorbani, 2019. "Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 847-861, January.
    20. Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.

    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:1:d:10.1007_s11269-016-1518-0. 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.