Developing a Forecasting System for the Parana - Río de la Plata Waterway

Overview

The Vía Navegable Troncal (VNT), integrating the Paraná River and the Río de la Plata, is the primary logistical artery for Argentina’s grain exports. Ensuring safe and efficient navigation requires precise water level forecasts to optimize vessel draft and manage sailing windows.

HCS was commissioned to develop and implement a next-generation operational forecasting system. Unlike traditional hydrodynamic models that can be computationally expensive and difficult to maintain in real-time, this solution leverages Deep Learning (Neural Networks) to predict water levels with high accuracy and speed.

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The Challenge

Forecasting water levels in this system involves capturing complex non-linear interactions between upstream river discharge, astronomical tides, and meteorological surges (sudestadas). The client needed a solution that could:

• Operate in Real-Time: Processing data and updating forecasts multiple times a day.
• Handle Data Gaps: Robustly dealing with frequent sensor failures or transmission outages in the field network.
• Scale Efficiently: Covering the entire waterway from riverine to estuarine sections without prohibitive computational costs.

The objective was to deploy a fully automated system capable of supporting critical decision-making for port authorities and navigators.

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Our Contribution

HCS designed, trained, and deployed a data-driven forecasting suite integrated into a cloud-based operational environment.

Instead of solving physical equations, the core engine consists of trained Neural Networks that learn the hydraulic behavior of the system from historical data.

• Paraná River Model: Focuses on the translation of flood waves and local flow variations.
• Río de la Plata Model: specifically tuned to capture tidal dynamics and wind-driven surges.

To ensure continuity of service during sensor failures, HCS implemented a “Substitute Model” strategy. These auxiliary neural networks estimate missing input data based on correlations with other functioning stations, preventing forecast interruptions when critical sensors go offline.

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Key Findings & Performance

The transition to an AI-based approach yielded significant operational benefits:

• High Accuracy: The neural networks successfully reproduced complex events, including the damping of tides upstream and storm surges in the estuary.
• Computational Efficiency: Forecasts for the entire system are generated in seconds.
• Operational Reliability: The continuous performance monitoring system tracks errors daily. Periodic retraining will allow the models to become increasingly precise.

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Outcomes and Client Value

The delivered system provides the client with a state-of-the-art Digital Tool that:

• Optimizes Logistics: Provides reliable draft forecasts up to 7 days in advance, aiding in cargo planning.
• Enhances Safety: Offers accurate predictions of low-water events and storm surges.
• Reduces Maintenance: The cloud-based, data-driven approach requires less manual calibration than traditional physical models.

This project showcases HCS’s ability to apply Advanced Data Science and Cloud Computing to solve traditional hydraulic engineering challenges.