Implementation of long-short term memory neural network (LSTM) for predicting the water quality parameters in Sungai Selangor / Nur Natasya Mohd Anuar ... [et al.]

Mohd Anuar, Nur Natasya and Fauzi, Nur Fatihah and Ab Halim, Huda Zuhrah and Khairudin, Nur Izzati and Ahmad Bakhtiar, Nurizatul Syarfinas and Shafii, Nor Hayati (2021) Implementation of long-short term memory neural network (LSTM) for predicting the water quality parameters in Sungai Selangor / Nur Natasya Mohd Anuar ... [et al.]. Journal of Computing Research and Innovation (JCRINN), 6 (4): 5. pp. 40-49. ISSN 2600-8793

Abstract

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Mohd Anuar, Nur Natasya
UNSPECIFIED
Fauzi, Nur Fatihah
fatihah@uitm.edu.my
Ab Halim, Huda Zuhrah
UNSPECIFIED
Khairudin, Nur Izzati
UNSPECIFIED
Ahmad Bakhtiar, Nurizatul Syarfinas
UNSPECIFIED
Shafii, Nor Hayati
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Neural networks (Computer science)
T Technology > TD Environmental technology. Sanitary engineering > Water supply for domestic and industrial purposes > Qualities of water. Water quality
Divisions: Universiti Teknologi MARA, Perlis > Arau Campus > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Journal of Computing Research and Innovation (JCRINN)
UiTM Journal Collections: UiTM Journal > Journal of Computing Research and Innovation (JCRINN)
ISSN: 2600-8793
Volume: 6
Number: 4
Page Range: pp. 40-49
Keywords: LSTM, water quality parameters, artificial neural network, monitoring stations, prediction model
Date: 2021
URI: https://ir.uitm.edu.my/id/eprint/60630
Edit Item
Edit Item

Download

[thumbnail of 60630.pdf] Text
60630.pdf

Download (350kB)

ID Number

60630

Indexing

Statistic

Statistic details