Prediction of Water Quality for Free Water Surface Constructed Wetland Using ANN and MLRA

Abstract

Constructed wetland is commonly used as a practice to reduce non-point source pollutants and as a stormwater treatment system. For many years, the evaluation of water quality assessment for the constructed wetland is using normal sampling and laboratory work. However, in line with the technology expansion, the prediction for water quality using modelling has been developed. This study focuses on the prediction of water quality parameter for constructed wetland under tropical climate using Artificial Neural Networks (ANN) and Multiple Linear Regressions Analysis (MLRA). There are five input parameters such as water quality at the inlet point, detention time, depth of water, ratio length to width, and rainfall. The output parameters consist of the water quality at the outlet point namely Biochemical Oxygen Demand (BOD 5 ), Chemical Oxygen Demand (COD), Total Phosphorus (TP), Total Nitrogen (TN), and Total Suspended Solid (TSS). Squared correlation coefficient (R 2 ) and root mean square error (RMSE) were applied to assess the model presentation and the result indicated that the ANN model shows excellent performance compared to MLRA. The R 2 value for each output parameter is higher than 0.90 and the RMSE values were closer to zero. However, TN has shown a very good pollutant removal in constructed wetland compared to other water quality tested. Findings from this study will contribute towards the enhancement of design performance and guideline for constructed wetlands under tropical climate.