# An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
###### Abstract  
Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to alterations in marine ecosystems and an increased incidence of extreme weather events. MHWs have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent years, global warming has intensified MHWs, and research on MHWs has rapidly developed into an important research frontier. With the development of deep learning models, they have demonstrated remarkable performance in predicting sea surface temperature, which is instrumental in identifying and anticipating marine heatwaves (MHWs). However, the complexity of deep learning models makes it difficult for users to understand how the models make predictions, posing a challenge for scientists and decision-makers who rely on interpretable results to manage the risks associated with MHWs. In this study, we propose an interpretable model for discovering MHWs. We first input variables that are relevant to the occurrence of MHWs into an LSTM model and use a posteriori explanation method called Expected Gradients to represent the degree to which different variables affect the prediction results. Additionally, we decompose the LSTM model to examine the information flow within the model. Our method can be used to understand which features the deep learning model focuses on and how these features affect the model's predictions. From the experimental results, this study provides a new perspective for understanding the causes of MHWs and demonstrates the prospect of future artificial intelligence-assisted scientific discovery. sea surface temperature; marine heat waves; explainable artificial intelligence +
Footnote †: journal: Applied Sciences  
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a given area [12]. Therefore, SST prediction is the key to anticipating and mitigating the impacts of these extreme events. In the field of marine science, deep learning has shown remarkable potential, providing new perspectives and tools for understanding and addressing ocean-related issues [13]. Compared to statistical methods, deep learning can establish nonlinear mapping relationships between oceanic variables and other features more conveniently, with adaptability and higher predictive accuracy. In recent years, the application of deep learning in marine heatwave prediction has become a research hotspot. [PERSON] et al. [14] proposed the use of LSTM models to address sea surface temperature (SST) prediction, demonstrating the effectiveness of this method with small prediction errors. [PERSON] et al. [15] applied a CNN model to ENSO prediction, demonstrating that the CNN model is an advanced approach for analyzing the complex mechanisms of ENSO events. In 2021, an all-season CNN model was created, incorporating seasonality in climate data [16]. [PERSON] et al. [17] used random forests and the N-BEATS model to predict sea surface temperature at the seasonal scale, and then used the predicted SST data to forecast the occurrence of marine heatwaves one year in advance.These studies have demonstrated that deep learning models hold scientific significance in predicting marine heatwaves. Currently, existing deep learning prediction models are entirely data-driven. The nonlinear elements implemented inside the model use activation functions like Sigmoid, tanh, etc., which exhibit strong non-convexity. Internally, the network can be regarded as a \"black box\", lacking interpretability in dimensions such as time and space [18]. In recent years, the research on the interpretability of deep learning has attracted widespread attention. Scholars around the world have overviewed the field of explainable artificial intelligence from different research angles and emphases [19].