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

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.