Introduction

Bangladesh, with its intricate river systems and vast coastline, is highly susceptible to coastal surges caused by monsoon rains, tidal influences, and cyclones. These natural events pose a persistent threat to millions of residents, leading to embankment failures, widespread inundation, and significant economic disruption. As climate change continues to amplify these hazards, the need for advanced predictive models has never been greater. Traditional forecasting methods have limitations, and real-time monitoring alone often provides insufficient time for effective response measures. This is where artificial intelligence (AI) steps in, offering a sophisticated and proactive approach to surge prediction.

Understanding Embankment Failures

In Bangladesh’s coastal regions, embankments serve as crucial barriers against rising waters. However, they remain vulnerable to two primary forms of failure: overtopping and breaching.

• Overtopping occurs when the water level surpasses the embankment height, leading to direct overflow into protected areas.
• Breaching is more complex and results from prolonged exposure to high water levels. When embankments experience sustained hydrostatic pressure, their structural integrity weakens, often leading to sudden failures.

The challenge lies in predicting these failures before they happen. This requires not only monitoring current water levels but also forecasting how they will change in response to environmental variables such as precipitation, wind speed, air pressure, and tidal influences. AI-powered predictive modeling is uniquely equipped to analyze these dynamic factors and generate early warnings, allowing for timely interventions.

Data Collection and Challenges

One of the biggest hurdles in implementing this system is acquiring accurate and reliable data. Initially, water level records from the Bangladesh Water Development Board (BWDB) were used. However, these early datasets were collected manually using traditional gauge readings, making them prone to human error and inconsistencies.

A breakthrough came when BWDB established automated gauge stations in late 2022. These sensors provide continuous water level measurements with higher accuracy, forming the backbone of our AI training dataset. Additionally, project-deployed sensors began collecting data in October 2024. However, since deep learning models require extensive historical data for training, these datasets are still in the early stages of accumulation.

AI Model Development

Our AI-based prediction system utilizes deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, which are well-suited for time-series forecasting. LSTMs excel at capturing sequential dependencies, making them ideal for predicting future water levels based on past trends.

To train the model, the first 1.5 years of automated water level data were used, with performance evaluated on the most recent year. The model was assessed using the Root Mean Squared Error (RMSE) metric, which measures prediction accuracy. Preliminary results indicate that our AI-driven approach significantly outperforms traditional statistical models, reducing forecasting errors and improving reliability.

The Role of Remote Sensing and Meteorological Data

Beyond water level monitoring, our model incorporates remote sensing and meteorological data to enhance predictive accuracy. Key factors include:

• Satellite imagery: Used to assess land surface conditions and detect early signs of embankment stress.
• Precipitation and weather forecasts: Integrated to predict how upcoming rainfall and wind conditions will impact water levels.
• Lunar cycles and tidal patterns: Analyzed to account for natural fluctuations in coastal water levels.

By merging these diverse data sources, the AI system gains a holistic understanding of surge risks, allowing for more precise and actionable predictions.

Early Findings and Future Improvements

The initial deployment of our AI model has yielded promising results. In test scenarios, the system successfully predicted water level changes with greater accuracy than conventional models. However, several areas require further refinement:

• Expanding Training Data: As more sensor data becomes available, the model’s predictive accuracy will improve.
• Real-Time Adaptability: Enhancing the model to adapt dynamically to sudden environmental shifts.
• Community Engagement: Integrating local knowledge and real-world observations to validate AI-generated forecasts.

Implications for Disaster Preparedness

The ability to anticipate embankment failures in advance has profound implications for disaster management. Early warnings empower authorities to take preventive measures, such as reinforcing vulnerable embankments, evacuating at-risk populations, and allocating emergency resources effectively. Furthermore, AI-driven predictions can assist policymakers in long-term infrastructure planning, ensuring that future embankments are designed with resilience in mind.

Conclusion

By leveraging AI-driven predictive modeling, real-time sensor data, and remote sensing technologies, this research aims to revolutionize surge risk management in Bangladesh. The integration of machine learning into early warning systems represents a transformative step toward safeguarding coastal communities from embankment failures and their devastating consequences. As our model continues to evolve, its ability to provide accurate and timely forecasts will play a crucial role in mitigating the socio-economic impact of extreme weather events. Through continued research, collaboration, and data-driven innovation, we move closer to a future where surge-related disasters are not just responded to, but proactively prevented.