IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i12d10.1007_s11269-022-03280-4.html
   My bibliography  Save this article

A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series

Author

Listed:
  • Bulent Haznedar

    (Gaziantep University)

  • Huseyin Cagan Kilinc

    (Istanbul Esenyurt University)

Abstract

River flow modeling plays a leading role in the management of water resources and ensuring sustainability. The complex nature of hydrological systems and the difficulty in the application process have led researchers to seek more instantaneous methods for flow predictions. Therefore, with the development of artificial intelligence-based techniques, hybrid modeling has become popular among hydrologists in recent years. For that reason, this study seeks to develop a hybrid model that integrates an adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) to predict river flow. Fundamentally, the performance of an ANFIS model depends on the optimum model parameters. Thus, it is aimed to increase the prediction performance by optimizing the ANFIS parameters with the population-based GA algorithm, which is a powerful algorithm. In this respect, the data gathered from Zamanti and Körkün Flow Measurement Stations (FMS) of Seyhan River, one of Turkey's significant rivers, were employed. Besides, the proposed hybrid ANFIS-GA approach was compared to classical ANFIS model to demonstrate the improvement of its performance. Also, within the scope of simulation studies, the traditional artificial neural networks (ANN) and the long-short term memory (LSTM) method which is a quite popular in recent years were used to predict streamflow data. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. In a nutshell, the outcomes indicated that the proposed ANFIS-GA method was the most successful model by achieving the highest values of R2 (≈0.9409) and R2 (≈0.9263) for the Zamanti and Körkün FMS data.

Suggested Citation

  • Bulent Haznedar & Huseyin Cagan Kilinc, 2022. "A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4819-4842, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:12:d:10.1007_s11269-022-03280-4
    DOI: 10.1007/s11269-022-03280-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03280-4
    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/s11269-022-03280-4?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. 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.
    2. Yongtao Wang & Jian Liu & Rong Li & Xinyu Suo & EnHui Lu, 2022. "Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 971-987, February.
    3. Hui Hu & Jianfeng Zhang & Tao Li, 2021. "A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5119-5138, December.
    4. 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.
    5. Ozgur Kisi & Levent Latifoğlu & Fatma Latifoğlu, 2014. "Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4045-4057, September.
    6. Mojtaba Poursaeid & Amir Houssain Poursaeid & Saeid Shabanlou, 2022. "A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1499-1519, 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. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.

    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. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    2. 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.
    3. 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.
    4. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.
    5. Peiqiang Gao & Wenfeng Du & Qingwen Lei & Juezhi Li & Shuaiji Zhang & Ning Li, 2023. "NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1481-1497, March.
    6. Yong Huang & Kehan Miao & Xiaoguang Liu & Yin Jiang, 2022. "The Hysteresis Response of Groundwater to Reservoir Water Level Changes in a Plain Reservoir Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4739-4763, September.
    7. Djerbouai Salim & Souag-Gamane Doudja & Ferhati Ahmed & Djoukbala Omar & Dougha Mostafa & Benselama Oussama & Hasbaia Mahmoud, 2023. "Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1401-1420, February.
    8. Seyed Ahmad Soleymani & Shidrokh Goudarzi & Mohammad Hossein Anisi & Wan Haslina Hassan & Mohd Yamani Idna Idris & Shahaboddin Shamshirband & Noorzaily Mohamed Noor & Ismail Ahmedy, 2016. "A Novel Method to Water Level Prediction using RBF and FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3265-3283, July.
    9. 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.
    10. 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.
    11. Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
    12. Wei Li & Xiaosheng Wang & Shujiang Pang & Haiying Guo, 2022. "A Runoff Prediction Model Based on Nonhomogeneous Markov Chain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1431-1442, March.
    13. Stephen Afrifa & Tao Zhang & Peter Appiahene & Vijayakumar Varadarajan, 2022. "Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis," Future Internet, MDPI, vol. 14(9), pages 1-31, August.
    14. Saeideh Samani & Meysam Vadiati & Farahnaz Azizi & Efat Zamani & Ozgur Kisi, 2022. "Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3627-3647, August.
    15. 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.
    16. 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.
    17. 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.
    18. Kiyoumars Roushangar & Sadegh Abdelzad, 2023. "River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3813-3831, August.
    19. M. Rajesh & Sachdeva Anishka & Pansari Satyam Viksit & Srivastav Arohi & S. Rehana, 2023. "Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 75-90, January.
    20. 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.

    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:36:y:2022:i:12:d:10.1007_s11269-022-03280-4. 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.