| Issue |
MATEC Web Conf.
Volume 203, 2018
International Conference on Civil, Offshore & Environmental Engineering 2018 (ICCOEE 2018)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 8 | |
| Section | Environmental Engineering | |
| DOI | https://doi.org/10.1051/matecconf/201820303001 | |
| Published online | 17 September 2018 | |
Suitability of ANN and GP for Predicting Soak Pit Tank Efficiency under Limited Data Conditions
1
Department of Biotechnology, Kalasalingam Academy of Research and Education,
Tamilnadu 626126,
India
2
Centre for Water Technology, Civil Engineering Department, Kalasalingam Academy of Research and Education,
Tamilnadu 626126,
India
* Corresponding author: naresh@klu.ac.in
Under Industry 4.0 scenario, smart sensors can be suitably integrated with control systems to monitor the treatment of wastewater systems. Such control systems need a sound modelling or forecasting tool for the real time monitoring. This work reports a pilot study to model the treatment efficiency of soak pit tanks for the treatment of grey water using Genetic Programming (GP) and Artificial Neural Network (ANN). Only the inlet total suspended solid is considered for modelling. The Root Mean Square of Errors for GP and ANN run models were found to be 1.8 and 12.5 respectively for Tank 1. The results indicate that GP is a more promising tool than ANN particularly when modelling under limited data conditions. The difference in performance of both the methods seem to depend on the type learning mode adopted in each case.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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