Water Quality Monitoring Dataset for Tilapia (Oreochromis niloticus) Aquaculture in Montería, Colombia (2024)

This dataset contains comprehensive water quality measurements for a tilapia (Oreochromis niloticus) aquaculture pond located in Montería, Colombia, collected over a six-month period in 2024. The dataset is part of a study aimed at enhancing water quality management in aquaculture systems, particularly in rural environments with limited technological infrastructure.

Data Collection and Parameters: The data was gathered through an Internet of Things (IoT) system designed to continuously monitor key water quality parameters. Parameters include:

Temperature (°C): Essential for maintaining optimal conditions for fish metabolism and growth.
Dissolved Oxygen (DO) (mg/L): Crucial for fish respiration and overall pond health.
pH: Indicates the acidity or alkalinity of the water, which affects fish health and nutrient availability.
Turbidity (NTU): Reflects the clarity of the water, which can impact light penetration and fish behavior.

Purpose and Applications: This dataset supports predictive modeling for water quality management using Machine Learning (ML) algorithms, including Random Forest and Support Vector Machines (SVM), with optimizations implemented via the Quantum Approximate Optimization Algorithm (QAOA). The dataset was utilized to train and validate models for real-time water quality prediction, achieving high accuracy in managing aquaculture conditions and reducing fish mortality.

Significance: The dataset provides valuable insights into water quality dynamics in tropical aquaculture settings, making it suitable for researchers, aquaculture managers, and data scientists focused on sustainable aquaculture practices. The data can aid in developing predictive models to stabilize water quality, support rural and urban aquaculture management, and contribute to global food security initiatives.

Acknowledgments: The collection of this dataset was made possible by an IoT-based monitoring system tailored for the environmental conditions of Montería, with an emphasis on adaptability for resource-limited rural regions.