Storm Surge Modeling in the AI Era: Enhancing Forecasting Accuracy with LSTM-Based Machine Learning

핵심 개념

The author proposes using an LSTM-based ML model to predict systemic errors in storm surge forecasting models, leading to improved accuracy by correcting biases.

초록

The content discusses the use of LSTM-based machine learning to enhance storm surge forecasting accuracy. The authors trained their model on historical storm data and tested it on hurricane Ian, showing significant improvements in forecasting accuracy. Various ML techniques were compared for bias correction in storm surge modeling.
The study highlights the potential of ML models to improve real-time forecasting and bias correction in physics-based simulation scenarios beyond storm surge forecasting. Different scenarios were considered, demonstrating the effectiveness of the proposed approach across various prediction window lengths.
Key metrics such as R2, MSE, RMSE, and MAE were used to evaluate model performance. The results show promising outcomes in improving water level predictions through ML bias correction.

통계

We trained our proposed ML model on a dataset of 61 historical storms in the coastal regions of the U.S.
Our model consistently improved the forecasting accuracy for hurricane Ian at all gauge station coordinates used for initial data.
By examining different subsets of training data from hurricanes with similar or different tracks, we found similar quality of bias correction with only six hurricanes.
The LSTM model outperformed other architectures like ANN and CNN for predicting storm surge levels at gauge stations.
The proposed ML architecture showed reasonable sea level predictions based on tidal station data under normal weather conditions.
The use of LSTM networks was explored for predicting wind waves from buoy stations data with promising results.

인용구

핵심 통찰 요약

Storm Surge Modeling in the AI ERA

by Stefanos Gia... 게시일 arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.04818.pdf

더 깊은 질문

How can the findings from this study be applied to improve other types of natural disaster forecasting

The findings from this study on storm surge modeling using LSTM-based ML for bias correction can be applied to improve other types of natural disaster forecasting, such as flood prediction, earthquake early warning systems, and wildfire behavior modeling. By training ML models on historical data and observed outcomes, similar bias correction techniques can be implemented to enhance the accuracy of predictions in these scenarios. For instance, in flood forecasting, ML models could correct biases in river level predictions based on past discrepancies between modeled and observed water levels. In earthquake early warning systems, ML algorithms could adjust seismic activity forecasts by learning from historical data patterns. Similarly, in wildfire behavior modeling, ML models could refine fire spread predictions by accounting for systemic errors identified through bias correction methods.

What are some potential limitations or challenges associated with implementing ML bias correction models in real-time scenarios

Implementing ML bias correction models in real-time scenarios may pose several limitations and challenges. One key challenge is the need for continuous model retraining to adapt to evolving conditions and new data inputs. Real-time applications require rapid processing speeds and frequent updates to ensure accurate bias corrections as new information becomes available. Additionally, ensuring the reliability and robustness of the ML model under dynamic environmental conditions poses a significant challenge. The interpretability of complex AI algorithms used for bias correction is another limitation since understanding how these models arrive at their decisions is crucial for trustworthiness in critical decision-making processes.

How might advancements in AI technology impact the future development of storm surge modeling and forecasting methods

Advancements in AI technology are poised to revolutionize storm surge modeling and forecasting methods by enhancing predictive capabilities and improving accuracy. With AI-driven approaches like LSTM-based machine learning enabling better capture of non-linear relationships within complex datasets, storm surge models can benefit from more precise simulations that account for systemic errors inherent in physics-based simulations. Furthermore, advancements in AI technology offer opportunities for automated feature extraction from large-scale meteorological datasets which can lead to more efficient model training processes with improved generalization capabilities across different regions or weather events.