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

Juan Francisco Fuentes-Pérez¹, Ana García-Vega², Francisco Javier Bravo-Córdoba²

¹Department of Hydraulics and Hydrology, ETSIIAA, University of Valladolid, 34004 Palencia, Spain.

Sensors (Basel, Switzerland)

|October 26, 2021

Summary

This summary is machine-generated.

A new sensor network monitors fishway hydraulics using neural networks, enabling autonomous detection of obstructions and malfunctions. This system optimizes maintenance, reducing costs and improving fish passage structures.

Keywords:

clogging fishways hydraulic modeling low-cost neural networks water-level sensors

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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.