ArticleEnvironmental science and pollution research international2021
Application of soft computing to predict water quality in wetland.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 51 citations in OpenAlex.
- Groundwater quality index prediction and aquifer failure risk analysis using metaheuristic-tuned artificial neural networks.Scientific reports · 2026Article
- Advances in machine learning and IoT for water quality monitoring: A comprehensive review.Heliyon · 2024Review
- Risk Assessment in Monitoring of Water Analysis of a Brazilian River.Molecules (Basel, Switzerland) · 2022Article
- Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms.Environmental science and pollution research international · 2022Article
- Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH.Environmental science and pollution research international · 2022Article
- A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation.Environmental science and pollution research international · 2021Article
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Authors and funding
8 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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