Integrating machine learning models for optimizing ecosystem health assessments through prediction of nitrate-N concentrations in the lower stretch of Ganga River, India.

• Tác giả: Shubhadeep Das Gupta, Akankshya Das, Basanta Kumar Das, Pranab Gogoi, Canciyal Johnson, Biswajit Mandal, Liton Paul, Sanatan Paul, Archisman Ray, Shreya Roy, Ajoy Saha
• Ngôn ngữ: eng
• Ký hiệu phân loại: 006.31 Machine learning
• Thông tin xuất bản: Germany : Environmental science and pollution research international , 2025
• Bộ sưu tập: NCBI
• ID: 642218

Tóm tắt

Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentration in rivers and its interactions with other water-quality parameters is crucial for effective freshwater ecosystem management. This study uses advanced machine learning models to analyse water quality parameters and predict nitrate-N concentrations in the lower stretch of the Ganga River from the observations of six annual periods (2017 to 2022). The parameters include water temperature, pH, specific conductivity (Sp_Con), dissolved oxygen (DO), nitrate-N, total phosphate (TP), turbidity, biochemical oxygen demand (BOD), silicate, total dissolved solids (TDS), and rainfall. The present study evaluated the predictive performance of five models-Multiple Polynomial Regression (MPR), Generalized Additive Models (GAMs), Decision Tree Regression, Random Forest (RF), and XGBoost (Extreme Gradient Boosting)-using RMSE, MAE, MAPE, NSE and R