Feature Selection-Driven Supervised Machine Learning for Dissolved Oxygen Prediction Using Multidomain Environmental Parameters in a Major Fishing Lake
Al R. ROMANO, Eden May B. DELA PENA, Karl Ezra S. PILARIO
Abstract. Dissolved oxygen (DO) holds an important role in water quality monitoring due to its impact on the aquatic organisms inhabiting the water body. Sampaloc lake, located in San Pablo City in Laguna Province, Philippines, one of the major fishing lakes, has been affected by the decrease of DO concentration leading to continuous fish kill incidents. This study used supervised machine learning-based model utilizing the filter and wrapper method in feature selection to predict the DO levels in Sampaloc Lake. The goal of the study is to provide a baseline for building an early warning system for fish kills events in the lake. Water quality, physical and hydroclimatic parameters are the range of features selected as predictors to create a reliable DO predictive model. Based on the feature selection process, water temperature, atmospheric temperature, biochemical oxygen demand, surface pressure and pH are the consistent important feature in predicting DO for both filter and wrapper method. Overall, the result of the eXtreme Gradient Boosting Regression (XGBR) Model of the filter method with R² = 0.95, RMSE = 0.14, and MAE = 0.10 showed a reliable performance among the predicted models (Multiple Linear Regression, Random Forest, Kernel Ridge Regression, and Bootstrap Aggregating) tested in the study.
Keywords
Dissolved Oxygen, Machine Learning, Water Quality Prediction, Aquaculture, Feature Selection
Published online 5/10/2026, 17 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Al R. ROMANO, Eden May B. DELA PENA, Karl Ezra S. PILARIO, Feature Selection-Driven Supervised Machine Learning for Dissolved Oxygen Prediction Using Multidomain Environmental Parameters in a Major Fishing Lake, Materials Research Proceedings, Vol. 66, pp 406-422, 2026
DOI: https://doi.org/10.21741/9781644904152-37
The article was published as article 37 of the book Advanced Materials and Sustainable Energy Technologies
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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