Publication: Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction
Date
2021
Authors
Sami B.H.Z.
Jee khai W.
Sami B.F.Z.
Ming Fai C.
Essam Y.
Ahmed A.N.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Ain Shams University
Abstract
This research studies the implementation of artificial neural networks (ANN) in predicting the concentration of total suspended solids (TSS) for the Fei Tsui reservoir in Taiwan. The prediction model developed in this study is designed to be used for monitoring the water quality in the Fei Tsui reservoir. High concentrations of total suspended solids (TSS) have been a crucial problem in the Fei Tsui reservoir for decades. As the Fei Tsui reservoir is a primary water source for Taipei City, this issue impacts the drinking water supply for the city due to etherification problems in the reservoir. 10-year average monthly records and 13-year average annual records have been collected for 26 parameters and correlated with the TSS concentrations to determine the parameters that have a strong relationship with the TSS concentrations. The parameters that were shown to have a strong correlation with the TSS concentration are the trophic state index (TSI), nitrate (NO3) concentration, total phosphorous (TP) concentration, iron concentration (IRON), and turbidity. Linear regression was used to develop the model that estimates the TSS concentration in the Fei Tsui Reservoir. The results show that model 3, a three-layer ANN model that uses three-input parameters namely NO3 concentration, TP concentration, and turbidity, with five neurons, to predict the output parameter which is TSS concentration, produces the highest coefficient of determination (R2) and Willmott Index (WI), which are 0.9589 and 0.9933 respectively, and the lowest root mean square error, which is 0.4753. Based on these performance criteria, model 3 is concluded as the best model to predict TSS concentrations in this study. � 2020 Faculty of Engineering, Ain Shams University
Description
Forecasting; Iron; Learning algorithms; Machine learning; Mean square error; Neural networks; Potable water; Reservoirs (water); Turbidity; Water quality; Water supply; Coefficient of determination; Iron concentrations; Output parameters; Performance criterion; Root mean square errors; Strong correlation; Total suspended solids; TSS concentration; Predictive analytics