Deep Neural Network-Based Earth System Forecasting Model Employing Non-Independent and Non-Identically Distributed Samples

The sampling in the Earth system poses challenges to meeting the assumption of independent and identically distributed samples. However, existing deep neural network forecasting models inadequately address this issue, impacting the model's generalization and stability. To tackle this problem, this paper introduces the Non-Independent Sample Bias Elimination Module and the Non-Identically Distributed Prototype Modeling Module within the classical deep neural network forecasting framework. The effectiveness of the proposed approach is validated through forecasting experiments on sea surface temperature and sea surface height in the Chinese sea region.