Predicting dissolved oxygen levels in aquatic ecosystems using machine learning models

Yıl 2025, Cilt: 4 Sayı: 1, 56 - 65, 30.06.2025

Çağrı Arısoy , Enes Eren Süzgen , Gülbahar Yildiz

Öz

This research investigates the performance of several machine learning algorithms in forecasting dissolved oxygen (DO) levels in the Brisbane River, utilizing physicochemical parameters alongside water flow data. We examined algorithms such as Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, XGBoost, and K-Nearest Neighbors, employing evaluation metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE). Among the models, ensemble techniques, particularly Random Forest and XGBoost, exhibited enhanced predictive accuracy and robustness in identifying intricate, non-linear relationships. Analysis revealed that key variables, including pH, salinity, and specific conductance, were significant predictors, a finding corroborated by the correlation matrix. This study underscores the promise of machine learning, particularly ensemble approaches, in improving water quality monitoring and management, providing valuable insights for ecological sustainability and informed policymaking.

Anahtar Kelimeler

Machine Learning Algorithms , Aquatic Ecosystems , Brisbane River , Water Quality

Makine öğrenimi modelleri kullanarak su ekosistemlerindeki çözünmüş oksijen seviyelerinin tahmini

Öz

Bu araştırma, Brisbane Nehri'ndeki çözünmüş oksijen (DO) seviyelerini tahmin etmek için çeşitli makine öğrenimi algoritmalarının performansını, fizikokimyasal parametreler ve su akış verileri ile birlikte incelemektedir. Lineer Regresyon, Destek Vektör Regresyonu, Random Forest, Gradient Boosting, XGBoost ve K-En Yakın Komşu algoritmaları değerlendirilmiş; Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE) gibi değerlendirme metrikleri kullanılmıştır. Modeller arasında, özellikle Random Forest ve XGBoost gibi topluluk (ensemble) teknikleri, karmaşık ve doğrusal olmayan ilişkileri belirlemede üstün tahmin doğruluğu ve dayanıklılık sergilemiştir. Analizler, pH, tuzluluk ve spesifik iletkenlik gibi anahtar değişkenlerin önemli öngörücüler olduğunu ve bu bulguların korelasyon matrisi ile desteklendiğini ortaya koymuştur. Bu çalışma, makine öğreniminin, özellikle topluluk yaklaşımlarının, su kalitesinin izlenmesi ve yönetiminin iyileştirilmesindeki potansiyelini vurgulamakta ve ekolojik sürdürülebilirlik ile bilinçli politika oluşturma için değerli içgörüler sunmaktadır.

Anahtar Kelimeler

Makine Öğrenimi Algoritmaları , Su Ekosistemleri , Brisbane Nehri , Su Kalitesi

Kaynakça

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