Modelling of ammonia nitrogen in river using soft computing techniques

Abstract

Ammonia nitrogen is one of the most hazardous water pollution parameters. It is crucial to monitor the concentration of ammonia nitrogen to minimize ammonia nitrogen pollution in river water. This study aims to develop a reliable model to accurately predict ammonia nitrogen concentration. Langat River was selected as the study area. Two soft computing techniques namely Backpropagation Neural Network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed for the model development. Different model architectures were developed and evaluated. ANFIS model VI appears as an effective tool to serve the main objective where it has a considerably high coefficient of determination, low mean absolute and root mean squared errors, and small average percentage error. The model has an average percentage error of 23%, indicating it is able to provide an estimation accuracy of at least 77%.