A Step to Smart Fishways: An Autonomous Obstruction Detection System Using Hydraulic Modeling and Sensor Networks

Area of Science:

• Environmental Engineering
• Hydraulic Engineering
• Artificial Intelligence

Background:

• Stepped fishways facilitate fish passage over river obstacles.
• Inadequate maintenance can compromise fishway functionality and effectiveness.

Purpose of the Study:

• To develop an autonomous system for detecting obstructions and malfunctions in stepped fishways.
• To monitor the hydraulic performance of fishways in real-time.

Main Methods:

• A low-cost sensor network using custom ultrasonic water level nodes.
• Integration of fishway hydraulics data with neural networks (Keras + TensorFlow) programmed in Python.
• Real-time data transmission and remote alarm generation.

Main Results:

• The proposed system successfully detected malfunctions in field and offline assessments.
• Performance evaluation considered the number of sensing nodes and obstruction dimensions.
• The system demonstrated the capability to monitor fishway hydraulic performance.

Conclusions:

• The developed system offers autonomous obstruction/malfunction detection for stepped fishways.
• It optimizes maintenance scheduling, reducing operational costs and increasing structure operability.
• This technology advances smart fishway management and enhances fish passage efficiency.