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TerraShield AI: Building a Landslide Risk Assessment Prototype

Discover TerraShield AI, a web-based prototype that combines environmental risk indicators to help communities understand potential landslide risks and improve early-warning preparedness.

Introduction

Landslides can threaten lives, homes, infrastructure, and communities, especially in regions exposed to heavy rainfall and unstable terrain. Early awareness of environmental risk indicators can help people and local authorities prepare for potential hazards.
TerraShield AI is a landslide risk assessment and early-warning support prototype designed to make environmental risk information easier to understand through a web dashboard.

The Problem

Communities in landslide-prone areas need accessible ways to understand changing environmental conditions and prepare for potential emergencies. Risk information can be difficult to interpret without a clear, unified view of relevant indicators.

Our Approach

TerraShield AI demonstrates a dashboard-based approach to landslide risk awareness. It considers four indicators:
  • Rainfall: Represents precipitation-related risk.
  • Soil moisture: Represents wetness conditions in the soil.
  • Ground tilt: Represents potential changes in ground position.
  • Vibration: Represents ground movement activity.
These indicators contribute to a combined risk score, which is displayed through the web interface to help users understand the prototype's assessment.

Technology and Implementation

The project is built as a web application using React, TypeScript, and Vite. The current prototype is deployed using Cloudflare Workers and demonstrates the risk-assessment workflow using simulated or locally stored data.
The current version does not have live field-sensor integration or a trained machine-learning model. The risk score is a prototype calculation intended to demonstrate the concept, rather than a validated prediction of an actual landslide.

Potential Future Improvements

Future development could include:
  • Integrating real environmental sensors and reliable weather data.
  • Evaluating the risk model against validated historical and field data.
  • Adding location-aware notifications and emergency communication workflows.
  • Exploring AWS services for scalable data ingestion, storage, processing, and alert delivery.
  • Working with domain experts and local authorities to evaluate the system's safety and reliability.

Conclusion

TerraShield AI explores how environmental indicators can be presented in a clear and accessible format to support landslide risk awareness. Although the current implementation is a prototype, it provides a foundation for further development toward a more comprehensive early-warning support system.
The long-term goal is to help communities and emergency responders access understandable risk information and make better-informed preparedness decisions. The prototype is not a replacement for official alerts, professional hazard assessments, or validated warning systems.
# artificial-intelligence# web-development# sustainability# disaster-recovery
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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