Application of soft computing to predict water quality in wetland

• Published in: Environmental science and pollution research international 2021, Vol.28 (1), p.185-200
• Main Authors: Pham, Quoc Bao, Mohammadpour, Reza, Linh, Nguyen Thi Thuy, Mohajane, Meriame, Pourjasem, Ameneh, Sammen, Saad Sh, Anh, Duong Tran, Nam, Van Thai
• Format: Article
• Language: English
• Subjects: Adaptive systems; Aquatic environment; Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Biochemical oxygen demand; Biological Oxygen Demand Analysis; Chemical oxygen demand; Dissolved oxygen; Earth and Environmental Science; Ecosystem; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental monitoring; Environmental science; Free surfaces; Fuzzy logic; Group method of data handling; Humans; Neural networks; Oxygen; Parameter sensitivity; pH effects; Predictions; Research Article; Resource management; Rivers; Sensitivity analysis; Soft computing; Solid suspensions; Suspended solids; Waste Water Technology; Water Management; Water monitoring; Water Pollution Control; Water Quality; Water resources management; Wetlands
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-020-10344-8

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.