IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v81y2016i3d10.1007_s11069-016-2163-x.html
   My bibliography  Save this article

Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition

Author

Listed:
  • Mostafa Dastorani

    (University of Kashan)

  • Mohammad Mirzavand

    (University of Kashan)

  • Mohammad Taghi Dastorani

    (Ferdowsi University of Mashhad (FUM))

  • Seyyed Javad Sadatinejad

    (University of Tehran)

Abstract

The aim of this study is to investigate the ability of different time series models in forecasting monthly rainfall. In order to do this, monthly rainfall data were collected from 9 rainfall stations in North Khorasan province (North east of Iran) from 1989 to 2012. R software was used to predict the highest rainfall in these 9 rain gage stations for the time period 2002–2012 using monthly highest rainfall data of 1989–2002. In this study, AR, MA, ARMA, ARIMA, and SARIMA with 11 different structures based on trial and error were examined. Because the trend, seasonal and jump components are deterministic components, it is not necessary to model these components, but modeling of random component is very important for rainfall forecasting. So, the main data series was decomposed (for AR, MA and ARMA models) and the random part has been modeled. After that, the random component was collected with the seasonal and trend component and the amount of rainfall was simulated. But for ARIMA and SARIMA, models fitted on original series. The result showed that in 33 % of data MA(2), in 22 % of data AR(1) and ARMA(2, 1) and in 11.11 % of data MA(1) and ARIMA(1, 1, 2) had the best performance in monthly rainfall forecasting. On the other hand, best time series model by change of data could vary. So, it is important to assess all the time series models for any area and any hydrological parameter.

Suggested Citation

  • Mostafa Dastorani & Mohammad Mirzavand & Mohammad Taghi Dastorani & Seyyed Javad Sadatinejad, 2016. "Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition," 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. 81(3), pages 1811-1827, April.
    • Handle: RePEc:spr:nathaz:v:81:y:2016:i:3:d:10.1007_s11069-016-2163-x
    DOI: 10.1007/s11069-016-2163-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2163-x
    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/s11069-016-2163-x?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. Dushmanta Dutta & Wendy Welsh & Jai Vaze & Shaun Kim & David Nicholls, 2012. "A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4397-4415, December.
    2. Bagher Shirmohammadi & Mehdi Vafakhah & Vahid Moosavi & Alireza Moghaddamnia, 2013. "Application of Several Data-Driven Techniques for Predicting Groundwater Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 419-432, January.
    3. Radhakrishnan, P. & Dinesh, S., 2006. "An alternative approach to characterize time series data: Case study on Malaysian rainfall data," Chaos, Solitons & Fractals, Elsevier, vol. 27(2), pages 511-518.
    4. R. Venkata Ramana & B. Krishna & S. Kumar & N. Pandey, 2013. "Monthly Rainfall Prediction Using Wavelet Neural Network Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3697-3711, August.
    5. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    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. Köppelová, J. & Jindrová, A., 2017. "Comparative Study of Short-Term Time Series Models: Use of Mobile Telecommunication Services in CR Regions," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 9(1), March.
    2. Mahdi Soleimani Motlagh & Hoda Ghasemieh & Ali Talebi & Khodayar Abdollahi, 2017. "Identification and Analysis of Drought Propagation of Groundwater During Past and Future Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 109-125, January.
    3. Abdol Rassoul Zarei, 2018. "Evaluation of Drought Condition in Arid and Semi- Arid Regions, Using RDI Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1689-1711, March.
    4. Yingqi Zhu & Ying Wang & Tianxue Liu & Qi Sui, 2018. "Assessing macroeconomic recovery after a natural hazard based on ARIMA—a case study of the 2008 Wenchuan earthquake in China," 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. 91(3), pages 1025-1038, April.
    5. Han Du & Danqing Song, 2022. "Investigation of failure prediction of open-pit coal mine landslides containing complex geological structures using the inverse velocity method," 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. 111(3), pages 2819-2854, April.
    6. Anderson, Benjamin & Rane, Jayaraj & Khan, Rabia, 2023. "Distributed wind-hybrid microgrids with autonomous controls and forecasting," Applied Energy, Elsevier, vol. 333(C).

    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. Jinping Zhang & Hongbin Li & Xixi Shi & Yang Hong, 2019. "Wavelet-Nonlinear Cointegration Prediction of Irrigation Water in the Irrigation District," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2941-2954, June.
    2. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    3. Jinping Zhang & Yong Zhao & Weihua Xiao, 2015. "Multi-Resolution Cointegration Prediction for Runoff and Sediment Load," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3601-3613, August.
    4. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    5. Wang, Xin & Sun, Mei, 2021. "A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    6. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    7. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    8. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
    9. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    10. Tian, Zhongda & Chen, Hao, 2021. "Multi-step short-term wind speed prediction based on integrated multi-model fusion," Applied Energy, Elsevier, vol. 298(C).
    11. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    12. Mohamed Shenify & Amir Danesh & Milan Gocić & Ros Taher & Ainuddin Abdul Wahab & Abdullah Gani & Shahaboddin Shamshirband & Dalibor Petković, 2016. "Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 641-652, January.
    13. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    14. Xianming Dou & Yongguo Yang & Jinhui Luo, 2018. "Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements," Sustainability, MDPI, vol. 10(1), pages 1-26, January.
    15. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    16. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
    17. Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
    18. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
    19. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    20. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.

    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:nathaz:v:81:y:2016:i:3:d:10.1007_s11069-016-2163-x. 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.