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Prediction of Suspended Particulate Matter Using Machine Learning

Kolekar, Vinayak Vishnu (2020) Prediction of Suspended Particulate Matter Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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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

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 22 Jan 2021 14:42
Last Modified: 22 Jan 2021 14:42

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