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Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning

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  • Ming Wei

    (Tianjin University)

  • Xue-yi You

    (Tianjin University)

Abstract

Rainfall forecast is critical to the management and allocation of water resources. Deep learning is used to predict rainfall time series with high temporal and spatial variability. Discrete wavelet transform (DWT), long-short term memory (LSTM) and dilated causal convolutional neural network (DCCNN) is integrated to build a hybrid model (DWT-CLSTM-DCCNN). Two methods of sample construction are used to train DWT-CLSTM-DCCNN and their effects on the model performance are analyzed. LSTM and DCCNN are built as benchmark models. The forecasting performance of DWT-CLSTM-DCCNN on monthly rainfall data from four major cities in China is evaluated. The results of DWT-CLSTM-DCCNN are compared with those of benchmark models in terms of the mean absolute error (MAE), root mean squared error (RMSE) and Nash-Sutcliffe efficiency (NSE) as well as the forecasting curves. The results show that DWT-CLSTM-DCCNN outperforms the benchmark models in model accuracy and peak and mutational rainfall capture.

Suggested Citation

  • Ming Wei & Xue-yi You, 2022. "Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4003-4018, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:11:d:10.1007_s11269-022-03218-w
    DOI: 10.1007/s11269-022-03218-w
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    References listed on IDEAS

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    Cited by:

    1. Peiqiang Gao & Wenfeng Du & Qingwen Lei & Juezhi Li & Shuaiji Zhang & Ning Li, 2023. "NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1481-1497, March.
    2. 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.
    3. Guo-Yu Huang & Chi-Ju Lai & Ping-Feng Pai, 2022. "Forecasting Hourly Intermittent Rainfall by Deep Belief Networks with Simple Exponential Smoothing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5207-5223, October.
    4. Goksu Tuysuzoglu & Kokten Ulas Birant & Derya Birant, 2023. "Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    5. Mahrouz Nourali, 2023. "Improved Treatment of Model Prediction Uncertainty: Estimating Rainfall using Discrete Wavelet Transform and Principal Component Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4211-4231, September.

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