SciTransfer
PLOTO · Project

AI-Powered Climate Resilience and Risk Management System for Inland Waterway Transport

transportTestedTRL 6

Imagine having a crystal ball for rivers and canals that tells you exactly where a flood or accident will cause a traffic jam. This system uses satellite images, drones, and weather data to predict problems before they happen. It's like a high-tech GPS that helps boat operators and port managers avoid disasters and keep cargo moving smoothly.

By the numbers
21
Consortium partners
3
Case study validation countries
8
Industry partners
The business problem

What needed solving

Inland waterway transport is highly vulnerable to extreme weather and accidents, leading to costly delays and infrastructure damage. Operators currently lack integrated tools to predict these risks and simulate the impact of adaptation measures in real-time.

The solution

What was built

An integrated resilience platform featuring a Common Operational Picture (COP), an Incident Management System (IMS), and a simulation environment for 'what-if' risk scenarios.

Audience

Who needs this

River and canal port authoritiesBarge and ship fleet operatorsGovernment transport ministriesInfrastructure maintenance firms
Business applications

Who can put this to work

Logistics & Shipping
enterprise
Target: Inland waterway transport operator

If you are a shipping company dealing with unpredictable river levels and weather delays — this project developed a Common Operational Picture and early warning system that provides real-time impact assessments to keep vessels moving.

Infrastructure Management
any
Target: Canal and river authority

If you are a government agency dealing with aging locks and bridges prone to climate damage — this project developed a Resilience Assessment Platform that runs 'what-if' scenarios to test how different repairs reduce risk.

Environmental Monitoring
SME
Target: Specialized drone and sensor service provider

If you are a tech firm dealing with the need for automated infrastructure inspection — this project developed computer vision and machine learning tools for UAV- and satellite-based observations of transport assets.

Frequently asked

Quick answers

What is the cost or pricing for this system?

Based on available project data, specific pricing or cost structures are not mentioned as this is an EU-funded research and innovation action.

Is this solution ready for industrial scale?

The project is validating the tools through 3 case studies in Belgium, Romania, and Hungary, indicating it is moving toward industrial scale.

How is the IP and licensing handled?

Based on available project data, the specific licensing terms are not disclosed, though it involves a consortium of 21 partners including 8 industry players.

How does this integrate with existing data?

The system integrates existing data sources with new sensor-generated data, specifically using computer vision and tailored weather forecasts.

What is the implementation timeline?

The project period runs from 2022-09-01 to 2026-02-28, with deliverables including integrated prototypes for testing.

Consortium

Who built it

The consortium is heavily weighted toward practical application, with 38% industry participation (8 companies) and 6 SMEs. With 21 partners across 8 countries, the project combines academic research (6 universities, 3 research centers) with a strong commercial drive led by Netcompany SA, ensuring the resulting tools are tailored for actual operational use in the transport sector.

How to reach the team

Contact Netcompany SA in Belgium

Next steps

Talk to the team behind this work.

Contact us to explore licensing the Resilience Assessment Platform for your waterway assets.

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