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- W3134645401 abstract "The availability of freshwater is an essential element for any living being, and its adequate quality is a necessary feature for water-borne disease prevention and improving quality of life. The rapid growth of industrialization and abrupt environmental changes establish emergencies in the control and purification of water quality. Therefore drinking water quality monitoring and forecasting is the most important task for water supply management to keep human population healthy. The researchers have contributed to the early identification system with the help of machine learning due to the demand for water quality prediction. The proposed study is about the identification of suspended particles by forecasting of water quality parameters such as turbidity and pH level along with precipitation as an external physical feature. The impact relationship between them is verified with the Johansen Cointegration. The comparative model analysis demonstrated for water parameter forecasting consists of Vector Autoregression (VAR), Vector Error Correction Model (VECM), Autoregressive Integrated Moving Average with Explanatory Variables (ARIMAX) and Long Short Term Memory (LSTM). The comparative analysis based on RMSE and MAPE illustrates LSTM performance is better than other autoregressive models.Keywords— Turbidity, pH Level, ARIMAX, VAR, VECM, LSTM" @default.
- W3134645401 created "2021-03-15" @default.
- W3134645401 creator A5030366283 @default.
- W3134645401 date "2020-01-01" @default.
- W3134645401 modified "2023-09-23" @default.
- W3134645401 title "Prediction of Suspended Particulate MatterUsing Machine Learning" @default.
- W3134645401 hasPublicationYear "2020" @default.
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