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Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction

Author

Listed:
  • Hamid Moeeni

    (Razi University)

  • Hossein Bonakdari

    (Razi University)

  • Isa Ebtehaj

    (Razi University)

Abstract

Regarding the complexity and limitations of current knowledge, monthly inflow prediction is often not sufficiently accurate and cannot fulfil the needs in water resource planning. Such time series consist of periodic and random components. Thus, by using data pre-processing methods, it is possible to reduce the problematic effects of these components in the modeling process. Monthly inflow methods encompass statistical and soft computing methods. Each of these methods has advantages and disadvantages. In this study, a hybrid model comprising both methods’ advantages is presented. This four-step model includes seasonal autoregressive integrated moving average (SARIMA) and adaptive neuro fuzzy inference systems (ANFIS), which is a new hybrid model (SARIMA-ANFIS). The first step entails data pre-processing to prepare the data for linear component modeling. In the second step, the linear and nonlinear terms are estimated by the SARIMA model. In the third step, some goodness of fit tests are applied to investigate the validity of the linear and nonlinear components of decomposed inflows and SARIMA model parameters. Upon the confident correct selection of components, in the fourth step the nonlinear components are modeled by ANFIS. In this method, ANN modeling is used instead of ANFIS (SARIMA-ANN model). The result comparison indicates that the ANFIS is more accurate than artificial neural networks (ANN) and SARIMA-ANN models, and SARIMA-ANFIS is the superior model among all.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:7:d:10.1007_s11269-017-1632-7
    DOI: 10.1007/s11269-017-1632-7
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    References listed on IDEAS

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    1. 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.
    2. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    3. Ruiz-Aguilar, J.J. & Turias, I.J. & Jiménez-Come, M.J., 2014. "Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 1-13.
    4. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
    5. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
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    Cited by:

    1. Amir Hossein Zaji & Hossein Bonakdari & Bahram Gharabaghi, 2019. "Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4215-4230, September.
    2. Hamid Moeeni & Hossein Bonakdari, 2018. "Impact of Normalization and Input on ARMAX-ANN Model Performance in 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. 32(3), pages 845-863, February.
    3. Zomchak, Larysa & Umrysh, Hryhorii, 2017. "Моделювання Й Прогнозування Виробництва М’Яса Та Яєць В Україні За Допомогою Сезонної Arima-Моделі," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 3(3), September.
    4. Zhennan Liu & Qiongfang Li & Jingnan Zhou & Weiguo Jiao & Xiaoyu Wang, 2021. "Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2921-2940, July.
    5. Maryam Malekzadeh & Saeid Kardar & Keivan Saeb & Saeid Shabanlou & Lobat Taghavi, 2019. "A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1609-1628, March.
    6. Zhihan Cui & Boyu Huang & Haowen Dou & Yan Cheng & Jitian Guan & Teng Zhou, 2022. "A Two-Stage Hybrid Extreme Learning Model for Short-Term Traffic Flow Forecasting," Mathematics, MDPI, vol. 10(12), pages 1-17, June.
    7. Isa Ebtehaj & Keyvan Soltani & Afshin Amiri & Marzban Faramarzi & Chandra A. Madramootoo & Hossein Bonakdari, 2021. "Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    8. Mingxiang Yang & Hao Wang & Yunzhong Jiang & Xing Lu & Zhao Xu & Guangdong Sun, 2020. "GECA Proposed Ensemble–KNN Method for Improved Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 849-863, January.

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