# Bangladesh Flood Prediction System: An Intelligent Disaster Prevention Platform Integrating Satellite Remote Sensing and Machine Learning

> A flood prediction and monitoring platform based on XGBoost, BiLSTM, and U-Net architectures, integrating Sentinel-1 SAR satellite data, DAHITI altimetry data, and Open-Meteo weather forecasts to provide real-time flood warnings and inundation range analysis for Bangladesh.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-03T21:44:58.000Z
- 最近活动: 2026-06-03T21:48:27.099Z
- 热度: 163.9
- 关键词: 洪水预测, 机器学习, 卫星遥感, Sentinel-1, LSTM, U-Net, XGBoost, 孟加拉国, 防灾减灾, 静态站点生成
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-tahmidxp96-flood-prediction
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-tahmidxp96-flood-prediction
- Markdown 来源: floors_fallback

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## Bangladesh Flood Prediction System: An Intelligent Disaster Prevention Platform Integrating Satellite Remote Sensing and Machine Learning (Introduction)

This project is a flood prediction and monitoring platform based on XGBoost, BiLSTM, and U-Net architectures, integrating Sentinel-1 SAR satellite data, DAHITI altimetry data, and Open-Meteo weather forecasts to provide real-time flood warnings and inundation range analysis for Bangladesh. The project is maintained by tahmidxp96, with source code hosted on GitHub (link: https://github.com/tahmidxp96/flood-prediction), and was released in June 2026.

## Project Background and Significance

Located in the Ganges-Brahmaputra Delta, Bangladesh is one of the countries most severely affected by flood disasters in the world, with millions of people affected each monsoon season. Traditional monitoring relies on ground sensors, which are costly and have limited coverage. This project provides water level prediction and real-time inundation range analysis by integrating satellite remote sensing, machine learning, and web visualization technologies, offering scientific decision support for disaster prevention and mitigation.

## Technical Architecture Overview

The system adopts dual-model collaboration: 1. Time-series flood prediction model (XGBoost baseline + BiLSTM sequence model), which uses satellite altimetry data, terrain parameters, and weather forecasts to predict warning levels; 2. Spatial inundation range mapping model (ResNet-34 U-Net), which processes Sentinel-1 SAR images to generate 10-meter resolution water boundary maps. The architecture uses a "static-first" design, where all computations are completed outside requests. GitHub Actions daily scheduled tasks automatically fetch data, run models, and generate static outputs, ensuring zero cold start and fast loading.

## Core Data Source Integration

The system integrates multi-source data: DAHITI API (satellite altimetry water level data), Open-Meteo API (gridded runoff forecasts), HydroSHEDS (static drainage network and terrain data), and Sentinel-1 SAR (C-band radar observations with all-weather capability). This strategy avoids reliance on local sensors and enables autonomous monitoring.

## Data Assets and Visualization

Generated static data assets include build-meta.json (build information), summary.json (national flood index), aoi-index.json (monitoring area information), GeoJSON boundaries, time-series prediction data, etc. The frontend uses the Next.js framework, glassmorphism UI design, Chart.js for interactive charts, and Leaflet/Mapbox for geovisualization.

## Deployment and Operation

Optimized deployment for Vercel: Import GitHub repository → Select Next.js framework → Build command next build → Output directory out. GitHub Actions daily workflows automatically update data and trigger Vercel rebuilds to ensure users access the latest results.

## Technical Highlights and Insights

Highlights include: Combination of edge computing and cloud native (precomputation reduces costs), multi-modal data fusion (time-series prediction + spatial segmentation), open-source ecosystem integration (using free resources like ESA Copernicus data), and innovative application of static site generation (SSG) (potential of JAMstack in the data science field).

## Conclusion

This project demonstrates the potential of combining machine learning with Earth observation data, providing an affordable disaster prevention technical path for resource-constrained areas. Its architecture ideas of static-first, precomputation, and automated pipelines are worth referencing for other environmental monitoring and disaster warning projects.