Dissolved Oxygen Prediction of the Ciliwung River using Artificial Neural Networks, Support Vector Machine, and Streeter-Phelps

Evaluation of Ciliwung river water quality can be done by analyzing the distribution dissolved oxygen (DO). The purpose this research is to analyze environmental parameters that affect DO, carrying out predictive modeling estimate DO in River. data used primary and secondary data, some which were obtained from previous studies. are temperature, biochemical demand, chemical power hydrogen, turbidity. dataset has a missing value 28.8%. To optimize model results, preprocessing carried using machine learning approach, namely comparing support vector (SVM), artificial neural networks (ANN), linear regression. three models compared predict results performance evaluation SVM, ANN Streeter-Phelps had RMSE values 0.110, 0.771, 0.114.