Status : Verified
| Personal Name | Romano, Al R. |
|---|---|
| Resource Title | Feature Selection-Driven Supervised Machine Learning for Dissolved Oxygen Prediction Using Multidomain Environmental Parameters in a Major Fishing Lake |
| Date Issued | 6 July 2025 |
| Abstract | Dissolved oxygen (DO) is a critical indicator of water quality, as it directly affects the health and survival of aquatic organisms, including those raised in aquaculture systems. This study investigates the feasibility of using supervised machine learning-based models for predicting DO levels in Sampaloc Lake, San Pablo, Laguna, utilizing Filter and Wrapper methods of feature selection. The analysis is based on a dataset comprising 104 historical records collected from 2010 to 2022, incorporating a total of 21 features. These features encompass a range of water quality, physical, and hydroclimatic parameters, which are integrated as predictors to develop reliable predictive models. Both Filter and Wrapper feature selection methods proved effective in identifying influential parameters and predicting DO levels. Among the models, XGBoost Regression (XGBR) emerged as the top performer for the Filter Method with R2 = 0.95, RMSE=0.14, and MAE = 0.10. For the Wrapper Method, Random Forest (RF) demonstrates better generalization, with higher test R2 (0.91) and lower RMSE (0.39) and MAE (0.22) values, making it the most suitable representative for the Wrapper Method. Comparing the two feature selection approaches, the Filter Method was found to be superior overall. The SHAP value analyses for both the Filter and Wrapper feature selection methods consistently identified biochemical oxygen demand (BOD), air temperature (AT), and surface pressure (SP) as the most influential predictors of DO concentration in Sampaloc Lake. Overall, this study concludes that the Filter Method with XGBR offers the most reliable and effective framework for predicting DO levels in Sampaloc Lake, contributing valuable insights for water quality management and sustainable aquaculture practices. |
| Degree Course | MS Environmental Engineering |
| Language | English |
| Keyword | Dissolved Oxygen, Machine Learning, Water Quality Prediction, Aquaculture, Feature Selection |
| Material Type | Thesis/Dissertation |
Preliminary Pages
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Category : P - Author wishes to publish the work personally.
Access Permission : Limited Access