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

Author

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. Lahiri, S. N., 1993. "On the moving block bootstrap under long range dependence," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 405-413, December.
    5. 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.
    6. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    7. 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.
    8. 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.
    9. 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.
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