Metro-environmental data approach for the prediction of chemical oxygen demand in new Nicosia wastewater treatment plant
2021; Taylor & Francis; Volume: 221; Linguagem: Inglês
10.5004/dwt.2021.27049
ISSN1944-3994
AutoresAuwalu Saleh Mubarak, Parvaneh Esmaılı, Zubaida Said Ameen, Rabiu Aliyu Abdulkadir, Muhammad Sani Gaya, Mehmet Özsöz, Gaurav Saini, Sani I. Abba,
Tópico(s)Water Quality Monitoring and Analysis
ResumoThis study aimed at employing three data-driven models, namely the Hammerstein-Weiner (HW) model, support vector machine (SVM), and feedforward back propagation neural network (FFBPNN) and traditional multi-linear regression, as well as two non-linear ensemble techniques viz: HW-ensemble and FFBPNN-ensemble, were employed to predict chemical oxygen demand (COD eff ).For the prediction of the COD eff , two types of data were used, the first one being environmental data from the new Nicosia waste water treatment plant conductivity (Cond inf ), including total nitrogen (TN inf ), total phosphorus (TP inf ) and one-effluent parameter COD eff as M1, where the second was meteorology data from the National Aeronautics and Space Administration (NASA) (at 2 m above the Earth's surface), such as relative humidity (R2H), maximum temperature (T2M_M) and mean temperature (T2M) as M2, in a hybrid model M3, which was a combination of both the meteorology and environmental data M1 and M2.According to the performance criteria RMSE and DC of the single models, values of HW-M1 (0.0308 and 0.9686), HW-M2 (0.0322 and 0.9093) and SVM-M3 (0.025 and 0.9486) were recorded.The ensemble technique improved the performance of the single models in the verification phase by 12% and 19% for HW-E and FFBPNN-E, respectively.
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