# An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
## 5 Conclusions  
In this paper, we propose an innovative approach to explore marine heatwave patterns through deep learning. We have successfully combined the robustness of complex deep learning models with interpretive tools to provide a deeper and more interpretable understanding of marine heatwave patterns. In this paper, a LSTM model identifies nonlinear relationships between 10m wind speeds, sea surface pressure, and sea surface temperatures at 13 coastal sites in China. Then, using 10m wind speeds and sea surface pressure, the model predicts sea surface temperatures. The efficacy of the model is assessed through the MSE and the RMSE of these predictions, which indicate good predictive performance. Following the definition of marine heatwaves, the paper identifies marine heatwave events and applies feature importance score clustering analysis to characterize the events. This  
Figure 16: The marine heatwave event that occurred at NSI station on 29 May 2010, and its corresponding feature importance. The first and second row plots represent the historical data of different points in the 180 days before the marine heatwave event, with colors indicating the feature importance at different time points. The third row plot represents the predicted and observed sea surface temperature values. The fourth and fifth row plots represent the feature importance of pressure and 10m wind speed, respectively. analysis has revealed five patterns contributing to the occurrence of marine heatwaves along the Chinese coast. In addressing these marine heatwave events, we employ two interpretable methods for explanation and compare the outcomes with existing theoretical research, finding a congruence. We demonstrated that the expected gradient method mentioned in the paper reveals different patterns of feature importance and reveals different performances of the LSTM networks in retaining and discarding information when simulating different types of marine heatwaves through additive decomposition methods. This deep learning framework can effectively reveal complex and non-linear patterns of marine heatwaves. The critical breakthrough of this method is that the features and parameters used to generate predictive results can be explained easily, making it a powerful tool for researchers to fully understand such initially intricate phenomena. We observed that due to the dynamic and adaptive nature provided by deep learning, this method shows a robust capability in dealing with the vulnerability and anomalies of marine heatwaves, which are aspects that traditional models fail to grasp. This breakthrough method has opened new possibilities in analyzing and simulating the complexity and uncertainty of marine heatwaves. However, we recognize that there are some challenges and limitations in the development of models, including the quality and representation of data, and the transparency and interpretability of deep learning models. These issues require further research to increase the application value and practicality of the model. In summary, our research indicates that deep learning-based predictions on marine heatwave patterns provide a tool with enormous potential. In future research, we look forward to further optimizing the model and applying this framework to larger environmental datasets to more accurately predict and understand marine heatwaves and their impact on the global climate. Moreover, this framework also provides a new, interpretable model for applying deep learning to other complex issues in environmental science and climate research. Although our method yielded good results, it still has limitations. Since EG feature importance scores are mostly chaotic and unordered, they may not provide a good explanation method, and the fact that the patterns that the experiment generated are uninterpretable also poses directions for future research in marine heatwave patterns in Earth science.