Landslide Alert System_.pptx Landslide Alert System_.pptx

Landslide Alert System_.pptx Landslide Alert System_.pptx

1. Integrated Real time Landslide  forecasting & Disaster Management Platform for Border Roads Organization

2. Introduction RLMFS (Real-time Landslide Monitoring and Forecasting System ) A cutting-edge software platform that enables real-time monitoring , early warning , and forecasting of landslides in mountainous regions using geospatial data, environmental sensors, and predictive analytics. Who benefits? Governments, emergency responders, infrastructure planners, and communities living in hilly and landslide-prone areas. Key features Real-time data collection & analysis Early warning alerts Integrated disaster management tools Proactive risk mitigation strategies How it works Combines sensors, satellite data, AI algorithms, and expert insights to deliver actionable forecasts and emergency responses . Problem Frequent landslides cause loss of life, property damage, and disruption Lack of timely warnings and coordinated disaster response Solution Real-time monitoring and forecasting platform Early alerts and integrated management tools Enables faster action, reduces risks, and saves lives

3. Global Impact of Natural Disasters: Reported Fatalities by Region Impact of Natural Disasters: Reported Fatalities by Region

4. Continuous monitoring of landslide-prone areas Data-driven forecasts using AI and sensors . Emergency response support and coordination Real-time alerts for potential landslides Risk analysis & mitigation strategies Safety tools for communities and authorities What the Platform Will Provide? 4 Identification of landslide prone areas based on historical data.

5. Benefits Proactive Repair Suggestions

6. Key Components

7. Data Acquisition Remote Sensing Satellite imagery (Sentinel, Landsat), InSAR, LiDAR. IoT Sensors : Rain gauges, soil moisture sensors, tiltmeters, geophones. Meteorological Data : Rainfall, temperature, humidity from IMD and other APIs. Geological Data : Slope, soil type, fault lines, vegetation cover. Historical Landslide Records : GSI, NDEM and local agencies.

8. Machine Learning Models : * Random Forest, XGBoost for classification. * LSTM for time-series rainfall prediction. Predictive Modeling Risk Zoning : *Use terrain and rainfall data to classify zones as low, medium, or high risk. Threshold-Based Alerts : *Trigger warnings when rainfall or soil moisture exceeds critical levels.

9. Web Dashboard : *Interactive maps (Leaflet.js, Mapbox ). *Real-time sensor data visualization. *Historical trends and forecast overlays. Mobile App : *Alerts via push notifications/SMS. *Community reporting of landslide events. Alert System : *Multi-channel alerts (email, SMS, sirens, traffic signals). Visualization & Alerts

10. 01 02 03 04 5 Implementation Phases Feasibility & Planning Identify target regions. Stakeholder consultation (local authorities, GSI, IMD). Define data sources and infrastructure needs . Platform Development Build a minimal viable product (MVP) with: Basic GIS map Sensor data integration Simple alert system Model Training & Testing Collect historical data. Train ML models. Validate with recent landslide events. Deployment Deploy sensors in field. Launch dashboard and mobile app. Integrate with disaster management authorities. Operation & Maintenance Regular model updates. Expand to new regions. Add new data sources (e.g., drone imagery).

11. 2 1 3 POC Identification of 10 KM for POC. Define and agree success criteria for POC. Gather data for POC. Develop a prototype for POC. Conduct the POC . Procurement of product name. Implantation of the Product. What’s Next?

12. Case Study

13. Case Study 1: Japan Japan, with its steep topography, heavy rainfall, and seismic activity, has developed one of the most advanced nationwide landslide early warning systems. System Name: Nationwide Landslide Disaster Risk Assessment System. Key Features: Rainfall-Based Thresholds: The system primarily uses a combination of two rainfall indices: Soil-Water Index (SWI): Measures the antecedent soil moisture. 60-minute Cumulative Rainfall: Tracks short-term, intense rainfall. High-Resolution Monitoring: The country is divided into small 5-km grid meshes. Alerts are issued at this localized level when a combination of SWI and rainfall exceeds the predefined thresholds for that specific area. Advanced Technology: Japan uses cutting-edge technologies like airborne LiDAR (Light Detection and Ranging) to create highly detailed topographic maps that can identify old landslide scars hidden under forests, which helps in better risk assessment. They also use automated satellite systems like Himawari-8 to detect changes in vegetation cover caused by landslides. Public Dissemination: Alerts are issued by the Japan Meteorological Agency (JMA), often in collaboration with local authorities, to provide residents with information for potential evacuation.

14. Case Study 2: Hong Kong Hong Kong has a long history of landslide disasters due to its steep terrain and dense population. Its system is a classic example of a top-down, government-led approach that has significantly reduced landslide-related fatalities. System Name: Landslip Warning System, jointly operated by the Geotechnical Engineering Office (GEO) and the Hong Kong Observatory (HKO). Key Features: Rainfall Thresholds: The system is primarily based on empirical rainfall thresholds. The GEO and HKO use real-time rainfall data from a network of over 100 automatic raingauges , along with weather radar and satellite imagery. Dual Criteria: The warning is triggered when specific rainfall thresholds are met, particularly a high accumulated rainfall over 24 hours or a very high intensity over a short period (e.g., 70 mm in one hour or 175 mm in 24 hours). Public Dissemination: Once a warning is issued, it is immediately broadcast to the public through radio, television, mobile apps, and social media. This alert triggers a coordinated response from various government departments, including the opening of temporary shelters and mobilization of emergency services. Proactive Mitigation: The warning system is one part of a larger "Slope Safety System." This comprehensive program also includes large-scale slope stabilization works, a public education campaign, and stringent controls on new construction. This proactive approach has been highly effective in reducing landslide risks over decades

15. Sample Dashboard

16. Case Study 3: Peru Peru faces significant landslide risks from heavy rainfall in the Andes, and recent research has focused on developing a scientific basis for a national-scale warning system. System Approach: Development of rainfall-based thresholds for a potential country-wide landslide warning system. Key Features: Data-Driven Research: Unlike older systems that relied on a few key gauges, recent studies in Peru have focused on using gridded rainfall data (satellite-derived) and comprehensive landslide inventories to develop empirical-statistical thresholds for different regions. Regionalized Thresholds: The research found that a single national threshold is not effective due to the country's diverse climate and geology. Therefore, distinct rainfall thresholds are being developed for 11 different rainfall regions. This regionalization is critical for increasing the accuracy of warnings. Focus on Shallow Landslides: The research specifically focuses on shallow landslides, which are most commonly triggered by intense, short-duration rainfall. The "mean intensity-duration" of rainfall was found to be the most effective trigger for these events. Future Potential: This work lays the foundation for a future operational warning system that can combine rainfall forecasts with these newly developed regional thresholds to provide timely alerts. It highlights how a country can leverage scientific research to build the technical framework for a national warning system.

17. Q&A?

18. THANK YOU Sutantra Systems Confidential 19

19. Frontend Backend Database Events and tasks React.js / Vue.js, Leaflet.js Python (Flask/Django), Node.js PostgreSQL + PostGIS , InfluxDB Attitudes and behaviors ML/AI GIS Cloud APIs Scikit-learn, TensorFlow, PyTorch QGIS, ArcGIS, GDAL AWS / Azure / GCP OpenWeatherMap , IMD Technology Stack

20. System Architecture > > > > Sensors IoT Gateways Cloud Analytics GIS Dashboard Alerts to Police, SDRF, Public