Prediction of Water Quality Index using Deep Learning in Mining Company

Wildan Azka Fillah*, Diana Purwitasari

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The quality of mine water at the mining company XYZ already has an Internet-of-things-based measuring tool. The quality of mine water is a standard that must be complied by the company to the regulator. The impact of passing the quality standard is the temporary suspension of mining activity. An effective process for implementing mine water quality is by predicting mine water quality based on historical data. The data is obtained every two minutes in areas in the company. Forecasting using ARIMA and Support Vector Regression (SVR) have used for years. In recent years, Recurrent Neural Networks (RNN) have shown more correct prediction results than ARIMA. Long Short-Term Memory (LSTM) is a RNN model that uses past data (long term) to predict current data (short term). The results of this research are comparison model of water quality using ARIMA, SVR, and LSTM. It shown that LSTM algorithm gave the best result with lower error. The model from the LSTM method can be used to make predictions such seven days prediction of the pH value in next day whether it is following the rules or needs to be controlled. Because of this preventive maintenance, the company will not be penalized.

Original languageEnglish
Title of host publicationProceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
Subtitle of host publicationApplying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages574-578
Number of pages5
ISBN (Electronic)9798350399615
DOIs
Publication statusPublished - 2022
Event6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 - Virtual, Online, Indonesia
Duration: 13 Dec 202214 Dec 2022

Publication series

NameProceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

Conference

Conference6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period13/12/2214/12/22

Keywords

  • LSTM
  • deep learning
  • mine water
  • water quality

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