IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v219y2019icp72-85.html
   My bibliography  Save this article

Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting

Author

Listed:
  • Mouatadid, Soukayna
  • Adamowski, Jan F.
  • Tiwari, Mukesh K.
  • Quilty, John M.

Abstract

Many countries are suffering from water resource constraints due to rising demands from different water-consuming sectors and a changing climate. In some countries, such as Spain, operators of agricultural irrigation are under increasing pressure to improve efficiency. Accordingly, accurate forecasts of water demand are a pre-requisite for the successful implementation of water resources planning and management tools in irrigated areas. However, forecasting approaches based on traditional neural networks have challenges to capture temporal dependencies that generally characterize irrigation flow time series. In this study, for the first time, a recurrent long short-term memory network (LSTM) was coupled with the maximum overlap discrete wavelet transformation (MODWT) and bootstrap techniques, for accurate and robust irrigation flow forecasting. For comparative purposes, the study also explored the ability of an Artificial Neural Network (ANN), a Least Squares Support Vector Regression (LSSVR), an Extreme Learning Machine (ELM) and a Multi Linear Regression (MLR), to forecast irrigation flow in Palos de la Frontera, an irrigation district located in Huelva (southern Spain), for a one-day lead time. The autocorrelation function and partial autocorrelation function determined the most significant lagged time series of irrigation flow to be used as model inputs. The wavelet-LSTM (W-LSTM) and waveletbootstrap-ANN models (WB-ANN) had the most accurate forecasts, as measured by statistical metrics including the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (r2), mean relative error (MRE), mean absolute relative error (MARE), Willmott’s index, and Nash-Sutcliffe efficiency index (NASH). In this study, the capabilities of LSTMs, along with the MODWT transform analysis and bootstrap techniques, were benchmarked and could be further explored for forecasting other water resources variables in different climatic regions and for multi-step lead time forecasting.

Suggested Citation

  • Mouatadid, Soukayna & Adamowski, Jan F. & Tiwari, Mukesh K. & Quilty, John M., 2019. "Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting," Agricultural Water Management, Elsevier, vol. 219(C), pages 72-85.
    • Handle: RePEc:eee:agiwat:v:219:y:2019:i:c:p:72-85
    DOI: 10.1016/j.agwat.2019.03.045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377418311831
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2019.03.045?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. Deo, Ravinesh C. & Şahin, Mehmet, 2017. "Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 828-848.
    2. 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.
    3. Vahid Nourani & Mehdi Komasi & Akira Mano, 2009. "A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(14), pages 2877-2894, November.
    4. Van Aelst, P. & Ragab, R. A. & Feyen, J. & Raes, D., 1988. "Improving irrigation management by modelling the irrigation schedule," Agricultural Water Management, Elsevier, vol. 13(2-4), pages 113-125, June.
    5. 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.
    6. 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.
    7. Ashu Jain & Ashish Kumar Varshney & Umesh Chandra Joshi, 2001. "Short-Term Water Demand Forecast Modelling at IIT Kanpur 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. 15(5), pages 299-321, October.
    8. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    9. Lahiri, S. N., 1993. "On the moving block bootstrap under long range dependence," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 405-413, December.
    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. Liangfeng Zou & Yuanyuan Zha & Yuqing Diao & Chi Tang & Wenquan Gu & Dongguo Shao, 2023. "Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 427-449, January.
    2. Majid Mirzaei & Haoxuan Yu & Adnan Dehghani & Hadi Galavi & Vahid Shokri & Sahar Mohsenzadeh Karimi & Mehdi Sookhak, 2021. "A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation," Sustainability, MDPI, vol. 13(23), pages 1-16, December.
    3. Fatemeh Faal & Hamid Reza Ghafouri & Seyed Mohammad Ashrafi, 2022. "Monitoring and Predicting Saltwater Intrusion via Temporal Aquifer Vulnerability Maps and Surrogate Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 785-801, February.
    4. Tingqi Wang & Yuting Guo & Mazina Svetlana Evgenievna & Zhenjiang Wu, 2024. "Application of a Multi-Model Fusion Forecasting Approach in Runoff Prediction: A Case Study of the Yangtze River Source Region," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
    5. Jing Liu & Xin-Lei Zhou & Lu-Qi Zhang & Yue-Ping Xu, 2023. "Forecasting Short-term Water Demands with an Ensemble Deep Learning Model for a Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2991-3012, June.
    6. Zhang, Tao & Qiu, Rangjian & Ding, Risheng & Wu, Jingwei & Clothier, Brent, 2023. "Multi-scale spectral characteristics of latent heat flux over flooded rice and winter wheat rotation system," Agricultural Water Management, Elsevier, vol. 288(C).
    7. Liu, Hui & Duan, Zhu, 2020. "A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection," Applied Energy, Elsevier, vol. 261(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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Chuan Li & Yun Bai & Bo Zeng, 2016. "Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5145-5161, November.
    8. Tian Peng & Chu Zhang & Jianzhong Zhou & Xin Xia & Xiaoming Xue, 2019. "Multi-Objective Optimization for Flood Interval Prediction Based on Orthogonal Chaotic NSGA-II and Kernel Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4731-4748, November.
    9. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Deo, Ravinesh C., 2020. "Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    10. Haijiao Yu & Xiaohu Wen & Qi Feng & Ravinesh C. Deo & Jianhua Si & Min Wu, 2018. "Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 301-323, January.
    11. Jeremy Berkowitz & Lutz Kilian, 2000. "Recent developments in bootstrapping time series," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 1-48.
    12. Sholpan Saimova & Gulsim Makenova & Aizhan Skakova & Aitolkyn Moldagaliyeva & Ardak Beisembinova & Zhamilya Berdiyarova & Bagdagul Imanbekova, 2020. "Towards a Low-carbon Economic Sustainable Development: Scenarios and Policies for Kazakhstan," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 638-646.
    13. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
    14. Feiyan Chen & Zhigao Zhou & Aiwen Lin & Jiqiang Niu & Wenmin Qin & Zhong Yang, 2019. "Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods," Energies, MDPI, vol. 12(1), pages 1-19, January.
    15. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    16. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    17. 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.
    18. 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.
    19. Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
    20. Choi, Ji-Eun & Shin, Dong Wan, 2019. "Moving block bootstrapping for a CUSUM test for correlation change," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 95-106.

    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:eee:agiwat:v:219:y:2019:i:c:p:72-85. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.