Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

Sen, Sohom; Maiti, Samaresh; Manna, Sumanta; Roy, Bibaswan; GHOSH, ANKIT

doi:10.36227/techrxiv.22300435.v1

Cited by 1 publication

(1 citation statement)

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“…Continuing the exploration of water quality prediction, Sen et al [2] conducted further research focusing on addressing challenges in aquaculture through the introduction of an intelligent machine learning and IoT-based biofloc system. This research aimed to enhance efficiency, production, water recycling, and automatic feeding within the aquaculture domain.…”

Section: Introductionmentioning

confidence: 99%

WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction

Firdiani,

Mandala,

Adiwijaya

et al. 2023

Applied Sciences

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Water presents challenges in swiftly and accurately assessing its quality due to its intricate composition, diverse sources, and the emergence of new pollutants. Current research tends to oversimplify water quality, categorizing it as potable or not, despite its complexity. To address this, we developed a water quality prediction system (WaQuPs), a sophisticated solution tackling the intricacies of water quality assessment. WaQuPs employs advanced machine learning, including an ensemble learning model, categorizing water quality into nuanced levels: potable, lightly polluted, moderately polluted, and heavily polluted. To ensure rapid and precise dissemination of information, WaQuPs integrates an Internet of Things (IoT)-based communication protocol for the efficient delivery of detected water quality results. In its development, we utilized advanced techniques, such as random oversampling (ROS) for dataset balance. We used a correlation coefficient to select relevant features for the ensemble learning algorithm based on the Random Forest algorithm. Further enhancements were made through hyperparameter tuning to improve the prediction accuracy. WaQuPs exhibited impressive metrics, achieving an accuracy of 83%, precision of 82%, recall of 83%, and an F1-score of 82%. Comparative analysis revealed that WaQuPs with the Random Forest model outperformed both the XGBoost and CatBoost models, confirming its superiority in predicting water quality.

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Section: Introductionmentioning

confidence: 99%