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Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method

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  • Sanjeet Kumar
  • Mukesh Tiwari
  • Chandranath Chatterjee
  • Ashok Mishra

Abstract

Accurate and reliable forecasting of reservoir inflow is necessary for efficient and effective water resources planning and management. The aim of this study is to develop an ensemble modeling approach based on wavelet analysis, bootstrap resampling and neural networks (BWANN) for reservoir inflow forecasting. In this study, performance of BWANN model is also compared with wavelet based ANN (WANN), wavelet based MLR (WMLR), bootstrap and wavelet analysis based multiple linear regression models (BWMLR), standard ANN, and standard multiple linear regression (MLR) models for inflow forecasting. Robust ANN and WANN models are ensured considering state of the art methodologies in the field. For development of WANN models, initially original time series data is decomposed using wavelet transformation, and wavelet sub-time series are considered to develop WANN models instead of standard data used for development of ANN model. To ensure a robust WANN model different types of wavelet functions are utilized. Further, a comparative analysis is carried out among different approaches of WANN model development using wavelet sub time series. Seven years of reservoir inflow data along with outflow data from two upstream reservoirs in the Damodar catchment along with rainfall data of 5 upstream rain gauge stations are considered in this study. Out of 7 years daily data, 5 years data are used for training the model, 1 year data are used for cross-validation and remaining 1 year data are used to evaluate the performance of the developed models. Different performance indices indicated better performance of WANN model in comparison with WMLR, ANN and MLR models for inflow forecasting. This study demonstrated the effectiveness of proper selection of wavelet functions and appropriate methodology for wavelet based model development. Moreover, performance of BWANN models is found better than BWMLR model for uncertainty assessment, and is found that instead of point predictions, range of forecast will be more reliable, accurate and can be very helpful for operational inflow forecasting. Copyright Springer Science+Business Media Dordrecht 2015

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  • Sanjeet Kumar & Mukesh Tiwari & Chandranath Chatterjee & Ashok Mishra, 2015. "Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4863-4883, October.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:13:p:4863-4883
    DOI: 10.1007/s11269-015-1095-7
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    7. Muhammad Shoaib & Asaad Y. Shamseldin & Sher Khan & Mudasser Muneer Khan & Zahid Mahmood Khan & Tahir Sultan & Bruce W. Melville, 2018. "A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 83-103, January.
    8. P. Biglarbeigi & W. A. Strong & D. Finlay & R. McDermott & P. Griffiths, 2020. "A Hybrid Model-Based Adaptive Framework for the Analysis of Climate Change Impact on Reservoir Performance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4053-4066, October.
    9. Zhangjun Liu & Shenglian Guo & Honggang Zhang & Dedi Liu & Guang Yang, 2016. "Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2111-2126, May.
    10. Adam P. Piotrowski & Maciej J. Napiorkowski & Monika Kalinowska & Jaroslaw J. Napiorkowski & Marzena Osuch, 2016. "Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1217-1237, February.
    11. 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.
    12. Vahid Nourani & Nardin Jabbarian Paknezhad & Hitoshi Tanaka, 2021. "Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    13. Peyman Abbaszadeh, 2016. "Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1701-1721, March.
    14. Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    15. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.
    16. Yaoze Liu & Indrajeet Chaubey & Laura C. Bowling & Vincent F. Bralts & Bernard A. Engel, 2016. "Sensitivity and Uncertainty Analysis of the L-THIA-LID 2.1 Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4927-4949, October.
    17. V. Ramaswamy & F. Saleh, 2020. "Ensemble Based Forecasting and Optimization Framework to Optimize Releases from Water Supply Reservoirs for Flood Control," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 989-1004, February.
    18. Jinping Zhang & Honglin Xiao & Hongyuan Fang, 2022. "Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 433-448, January.
    19. Peyman Abbaszadeh, 2016. "Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1701-1721, March.
    20. Hamid Moeeni & Hossein Bonakdari & Isa Ebtehaj, 2017. "Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2141-2156, May.

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