IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v27y2013i11p4113-4113.html
   My bibliography  Save this article

Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

Author

Listed:
  • Afiq Hipni
  • Ahmed El-shafie
  • Ali Najah
  • Othman Karim
  • Aini Hussain
  • Muhammad Mukhlisin

Abstract

No abstract is available for this item.

Suggested Citation

  • Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With 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. 27(11), pages 4113-4113, September.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:11:p:4113-4113
    DOI: 10.1007/s11269-013-0406-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-013-0406-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-013-0406-0?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vivien Lai & Ali Najah Ahmed & M.A. Malek & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Ahmed El-Shafie & Amr El-Shafie, 2019. "Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms," Sustainability, MDPI, vol. 11(17), pages 1-26, August.
    2. Raúl Parada & Jordi Font & Jordi Casas-Roma, 2019. "Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs," Energies, MDPI, vol. 12(10), pages 1-21, May.
    3. Jhih-Huang Wang & Gwo-Fong Lin & Ming-Jui Chang & I-Hang Huang & Yu-Ren Chen, 2019. "Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3759-3780, September.
    4. Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
    5. 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.
    6. 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.
    7. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    8. Shuheng Wang & Guohao Li & Yifan Bao, 2018. "A novel improved fuzzy support vector machine based stock price trend forecast model," Papers 1801.00681, arXiv.org.
    9. 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.
    10. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    11. Ahmed El-Shafie & Amr El-Shafie & Muhammad Mukhlisin, 2014. "New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2093-2107, June.
    12. Sina Paryani & Mojgan Bordbar & Changhyun Jun & Mahdi Panahi & Sayed M. Bateni & Christopher M. U. Neale & Hamidreza Moeini & Saro Lee, 2023. "Hybrid-based approaches for the flood susceptibility prediction of Kermanshah 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. 116(1), pages 837-868, March.
    13. Ahmet Cemkut Badem & Recep Yılmaz & Muhammet Raşit Cesur & Elif Cesur, 2024. "Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
    14. Zhenzhu Meng & Yiren Wang & Sen Zheng & Xiao Wang & Dan Liu & Jinxin Zhang & Yiting Shao, 2024. "Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm," Mathematics, MDPI, vol. 12(9), pages 1-18, April.
    15. Yashon O. Ouma & Ditiro B. Moalafhi & George Anderson & Boipuso Nkwae & Phillimon Odirile & Bhagabat P. Parida & Jiaguo Qi, 2022. "Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors," Sustainability, MDPI, vol. 14(22), pages 1-31, November.
    16. Sri Lakshmi Sesha Vani Jayanthi & Venkata Reddy Keesara & Venkataramana Sridhar, 2022. "Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    17. Onur Genç & Ali Dağ, 2016. "A machine learning-based approach to predict the velocity profiles in small streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 43-61, January.
    18. Ali Nouh Mabdeh & A’kif Al-Fugara & Khaled Mohamed Khedher & Muhammed Mabdeh & Abdel Rahman Al-Shabeeb & Rida Al-Adamat, 2022. "Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms," Sustainability, MDPI, vol. 14(15), pages 1-26, August.
    19. Mirzaei, Mohsen & Jafari, Ali & Gholamalifard, Mehdi & Azadi, Hossein & Shooshtari, Sharif Joorabian & Moghaddam, Saghi Movahhed & Gebrehiwot, Kindeya & Witlox, Frank, 2020. "Mitigating environmental risks: Modeling the interaction of water quality parameters and land use cover," Land Use Policy, Elsevier, vol. 95(C).
    20. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    21. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.

    More about this item

    Statistics

    Access and download statistics

    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:27:y:2013:i:11:p:4113-4113. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.