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A hybrid deep learning model for rainfall in the wetlands of southern Iraq

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Abstract

Machine learning is being used by researchers and computer scientists to develop a new method for predicting rainfall. Due to the non-linear relationship between input data and output conditions, rainfall prediction is hard, so deep neural network (DNN) models substitute for costly, complex systems. Deep neural network-based weather forecasting models can be designed quickly and cheaply to predict rainfall. On the other hand, water levels depend on rainfall. Unpredictable rainfall due to climate change might cause floods or droughts. Many individuals, especially farmers, rely on rain forecasts. In our study, we focus on the area of marshes in southern Iraq, some of the most famous landmarks in the area (and the world), where the Shatt al-Arab flows into the Arabic Gulf and the Tigris and Euphrates rivers developed within the Mesopotamian plain to create a natural balance. Since the beginning of the 1980s, the wetlands, sometimes known as "the marshes," have experienced droughts. And by the late 1990s, a sizable portion of the marshes had dried up, leaving the arid and salty Sabkha lands void of life, particularly lands with vast bodies of water and high levels of human activity. Moreover, the corresponding regions have undergone visible hydrological and climatic changes. In this study focuses on the marshes of southern Iraq and aims to develop a rainfall forecasting model. We propose a novel approach based on optimized LSTM and hybrid deep learning algorithms to improve the forecasting of average monthly rainfall. To evaluate the efficiency of the predictions, a comparison of the predicted rainfall and the actual recorded rainfall is made, and the performance and accuracy of the models are examined. The hybrid convolutional stacked bidirectional long-short term memory (CNN-BDLSTMs) outperformed the other models.

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Data availability

The datasets generated during and analysed during the current study are available in the Hybrid deep learning models algorithm for modelling and forecasting rainwater in Wetlands in south repository, https://github.com/abotalebmostafa11/Hybrid-deep-learning-models-algorithm-for-modelling-and-forecasting-rainwater-in-Wetlands-in-south-I.

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Authors and Affiliations

  1. Department of Computer Science, King Fahad Naval Academy, Al Jubail, 35512, Saudi Arabia

    Fehaid Alqahtani

  2. Department of System Programming, South Ural State University, Chelyabinsk, Russia

    Mostafa Abotaleb

  3. Electronic and Computer Center, University of Diyala, Baqubah, MJJ2+R9G, Iraq

    Alhumaima Ali Subhi

  4. Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt

    El-Sayed M. El-Kenawy

  5. Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt

    Abdelaziz A. Abdelhamid

  6. Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, 11961, Saudi Arabia

    Abdelaziz A. Abdelhamid

  7. Department of Statistics and Programming, Faculty of Economics, University of Tishreen, P.O. Box 2230, Latakia, Syria

    Khder Alakkari

  8. Faculty of Science, School of Science and Technology, University of New England, Armidale, NSW, 2350, Australia

    Amr Badr

  9. Electronic Computer Centre, University of Diyala, Diyala, 32001, Iraq

    H. K. Al-Mahdawi

  10. Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt

    Abdelhameed Ibrahim

  11. Department of Food and Biotechnology, South Ural State University, 454080, Chelyabinsk, Russia

    Ammar Kadi

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  1. Fehaid Alqahtani
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Correspondence to Mostafa Abotaleb.

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Alqahtani, F., Abotaleb, M., Subhi, A.A. et al. A hybrid deep learning model for rainfall in the wetlands of southern Iraq. Model. Earth Syst. Environ. 9, 4295–4312 (2023). https://doi.org/10.1007/s40808-023-01754-x

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