Senlin Li
ABSTRACT
Climate prediction plays a crucial role in understanding and adapting to the impacts of climate change. In recent years, researchers have explored its application in climate prediction models. Our aim is to build a deep hybrid model for climate prediction specifically in the Daxiangxi region, which includes CNN, LSTM, and MLP (called CLM). It aims to assist in decision-making related to agriculture, water resource management, and human being life. Deep learning models typically adopt different neural network structures with multiple hidden layers, which can learn the spatiotemporal relationship in historical meteorological data, and effectively predict future climate conditions. In this research, a large amount of meteorological data, including temperature, precipitation, and wind speed, was collected. These data will be used to train the different models, such as LSTM, CNN, ConvLSTM, and our proposed hybrid model, for predicting future climate changes in the Daxiangxi region. To achieve the high accuracy of our proposed model, optimization techniques such as data processing, regularization, and hyperparameter tuning can be employed. Additionally, in our experiments, datasets need to be divided into three parts such as training, validation, and testing sets. Appropriate evaluation metrics are selected to assess the predictive capability of these models. Our tests show that the proposed model performs well.
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