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Convolutional Neural Network- ANN- E (Tanh): A New Deep Learning Model for Predicting Rainfall

Author

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  • Mahdie Afshari Nia

    (University of Kashan)

  • Fatemeh Panahi

    (University of Kashan)

  • Mohammad Ehteram

    (Semnan University)

Abstract

The prediction of rainfall is essential for monitoring droughts and floods. The purpose of this paper is to develop a deep learning model for predicting monthly rainfall. The new model is used to predict rainfall in the Kashan plain of Iran. This study combines a deep learning model with an artificial neural network (ANN) model to predict rainfall. In this study, a convolutional neural network (CONV) is used as a deep learning model. The paper also introduces a new activation function called E-Tanh to develop ANN models. The new model has two main advantages. The model automatically determines key features. In addition, the new activation function can enhance the precision of ANN models. Lagged rainfall values are inserted into the models to predict rainfall. This study uses a bat optimization algorithm to choose inputs. At the training level, the mean absolute percentage errors (MAPES) of CONV-ANN-ANN-E-Tanh, CONV, and ANN-E-Tanh were 0.5%, 1%, and 2%, respectively. At the testing level, the MAPEs of CONV-ANN -E-Tanh, CONV, and ANN-E-Tanh were 1%, 3%, and 4%, respectively. The E-Tanh performed better than other activation functions based on error function values. Also, the CONV-ANN-E-Tanh can reduce CPU time. Our results show that the new hybrid model is a reliable tool for simulating complex phenomena.

Suggested Citation

  • Mahdie Afshari Nia & Fatemeh Panahi & Mohammad Ehteram, 2023. "Convolutional Neural Network- ANN- E (Tanh): A New Deep Learning Model for Predicting Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1785-1810, March.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:4:d:10.1007_s11269-023-03454-8
    DOI: 10.1007/s11269-023-03454-8
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    References listed on IDEAS

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    1. Yang, Qiangda & Dong, Ning & Zhang, Jie, 2021. "An enhanced adaptive bat algorithm for microgrid energy scheduling," Energy, Elsevier, vol. 232(C).
    2. 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.
    3. 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.
    4. Elham Ghanbari-Adivi & Mohammad Ehteram & Alireza Farrokhi & Zohreh Sheikh Khozani, 2022. "Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4313-4342, September.
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    Cited by:

    1. Mohammad Ehteram & Ali Najah Ahmed & Zohreh Sheikh Khozani & Ahmed El-Shafie, 2023. "Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3631-3655, July.

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