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Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting

Author

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  • Vahid Moosavi

    (Yazd University)

  • Ali Talebi

    (Yazd University)

  • Mohammad Reza Hadian

    (Yazd University)

Abstract

Effective runoff prediction is one of the main aspects of successful water resources management. One of the most important problems in the modeling of such hydrological processes is the non-stationarities in the data. Several data mining models have deficiencies in handling non-stationary data particularly when signal variations are highly non-stationary. The main objective of this study was to develop a robust model to estimate daily runoff quantities. Firstly, Group Method of Data Handling (GMDH) was used in its single form to model the rainfall-runoff process. Then, the discrete wavelet and wavelet packet transforms were used to decompose the original data to their corresponding components. Thereafter, hybrid models were developed using the wavelet-based analyzed data. Three different rivers were selected to perform these modeling approaches. Results showed that GMDH model had a moderate performance (R2 ≈ 0.84, RMSE ≈ 2.17 m3/s and Max. Error ≈ 24 m3/s for Ghale Chay River). Wavelet transform enhanced the ability of the GMDH model to some extent (R2 ≈ 0.90, RMSE ≈ 1.7 m3/s, and Max. Error ≈ 16 m3/s for Ghale-Chay River). However, it was shown that wavelet packet transform significantly enhanced the ability of the single GMDH model with R2 of 0.94, RMSE of 1.37m3/s, and Maximum Error of about 9.8m3/s for Ghale-Chay River. The results were similar in the other two rivers. It was confirmed that the wavelet packet transform can be effectively used to deal with the non- stationarities in the data and can efficiently enhance the performance of GMDH model.

Suggested Citation

  • Vahid Moosavi & Ali Talebi & Mohammad Reza Hadian, 2017. "Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 43-59, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1507-3
    DOI: 10.1007/s11269-016-1507-3
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    References listed on IDEAS

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    1. Yan-Fang Sang & Zhonggen Wang & Changming Liu, 2015. "Wavelet Neural Modeling for Hydrologic Time Series Forecasting with Uncertainty Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1789-1801, April.
    2. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
    3. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    4. Vinit Sehgal & Rajeev Sahay & Chandranath Chatterjee, 2014. "Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(6), pages 1733-1749, April.
    5. 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.
    6. Maryam Shafaei & Ozgur Kisi, 2016. "Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 79-97, January.
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    2. 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.
    3. Vahid Moosavi & Ayoob Karami & Negin Behnia & Ronny Berndtsson & Christian Massari, 2022. "Linking Hydro-Physical Variables and Landscape Metrics using Advanced Data Mining for Stream-Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4255-4273, September.
    4. Mohammad Hijji & Tzu-Chia Chen & Muhammad Ayaz & Ali S. Abosinnee & Iskandar Muda & Yury Razoumny & Javad Hatamiafkoueieh, 2023. "Optimization of State of the Art Fuzzy-Based Machine Learning Techniques for Total Dissolved Solids Prediction," Sustainability, MDPI, vol. 15(8), pages 1-23, April.

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