Abstract :

River water is a crucial natural resource utilized for various purposes, including agriculture and drinking. Human activities such as mining, industrial discharge, and improper waste management contribute to river water pollution, affecting its quality and posing risks to human health. Monitoring and predicting river water quality are essential for effective management and pollution control. The research focuses on Dissolved Oxygen (DO), and comparing of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) to developed prediction models. Evaluation of the models’ performance shows that the ANN model outperforms LSTM in predicting Dissolved Oxygen (DO) concentrations, achieving lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Although LSTM exhibits lower Mean Squared Error (MSE), the ANN model demonstrates better accuracy in minimizing the average distance between predicted and actual values. The findings suggest that ANN-based models offer good performance in river water quality prediction, with potential for further enhancement through additional variables or model architecture adjustments.

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Keywords :

ANN, Dissolved Oxygen, LSTM, Prediction, River Water.

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References :

1. Widodo, M. T. Sri Budiastuti, and Komariah, “Water Quality and Pollution Index in the Grenjeng River, Boyolali Regency, Indonesia,” Caraka Tani: Journal of Sustainable Agriculture, vol. 34, no. 2, pp. 150–161, Oct. 2019, doi: 10.20961/carakatani.v34i2.29186.
2. Wechmongkhonkon, N. Poomtong, and S. Areerachakul, “Application of Artificial Neural Network to clasification surface water quality,” 2012.
3. Wu and Z. Wang, “A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory,” Water (Switzerland), vol. 14, no. 4, Feb. 2022, doi: 10.3390/w14040610.
4. Wu, Q. Zhang, F. Wen, and Y. Qi, “A Water Quality Prediction Model Based on Multi-Task Deep Learning: A Case Study of the Yellow River, China,” Water (Switzerland), vol. 14, no. 21, Nov. 2022, doi: 10.3390/w14213408.
5. Chen, T. Wu, Z. Wang, X. Lin, and Y. Cai, “A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction,” Ecol Indic, vol. 146, Feb. 2023, doi: 10.1016/j.ecolind.2023.109882.
6. Kulisz and J. Kujawska, “Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Dec. 2021. doi: 10.1088/1742-6596/2130/1/012028.
7. Chen, L. Song, Y. Liu, L. Yang, and D. Li, “A review of the artificial neural network models for water quality prediction,” Applied Sciences (Switzerland), vol. 10, no. 17. MDPI AG, Sep. 01, 2020. doi: 10.3390/app10175776.
8. I. Ubah, L. C. Orakwe, K. N. Ogbu, J. I. Awu, I. E. Ahaneku, and E. C. Chukwuma, “Forecasting water quality parameters using artificial neural network for irrigation purposes,” Sci Rep, vol. 11, no. 1, Dec. 2021, doi: 10.1038/s41598-021-04062-5.
9. Niknam, H. K. Zare, H. Hosseininasab, and A. Mostafaeipour, “Developing an LSTM model to forecast the monthly water consumption according to the effects of the climatic factors in Yazd, Iran,” Journal of Engineering Research, vol. 11, no. 1, p. 100028, Mar. 2023, doi: 10.1016/j.jer.2023.100028.
10. Tan et al., “Application of CNN and Long Short-Term Memory Network in Water Quality Predicting,” Intelligent Automation & Soft Computing, vol. 34, no. 3, pp. 1943–1958, 2022, doi: 10.32604/iasc.2022.029660.
11. Zou, Q. Xiong, Q. Li, H. Yi, Y. Yu, and C. Wu, “A water quality prediction method based on the multi-time scale bidirectional long short-term memory network,” Environmental Science and Pollution Research, vol. 27, no. 14, pp. 16853–16864, May 2020, doi: 10.1007/s11356-020-08087-7.
12. Sahraei, L. Breuer, P. Kraft, and T. Houska, “Deep learning for water quality prediction: the application of LSTM model to predict water quality in catchment scale,” EGU General Assembly 2021, vol. EGU21-8756, 2021.
13. Li, L. Martínez López, Y. Li, and Institute of Electrical and Electronics Engineers, Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) : IEEE ISKE 2017 : November 24-26, 2017, NanJing, JiangSu, China.
14. Jaffar, N. M. Thamrin, M. S. A. M. Ali, M. F. Misnan, and A. I. M. Yassin, “WATER QUALITY PREDICTION USING LSTM-RNN: A REVIEW,” J Sustain Sci Manag, vol. 17, no. 7, pp. 204–225, 2022, doi: 10.46754/jssm.2022.07.015.
15. Yao, W. Zhou, M. Al Ghamdi, Y. Song, and W. Zhao, “An integrated D-CNN-LSTM approach for short-term heat demand prediction in district heating systems,” Energy Reports, vol. 8, pp. 98–107, Nov. 2022, doi: 10.1016/j.egyr.2022.08.087.
16. Sarkar and P. Pandey, “River Water Quality Modelling Using Artificial Neural Network Technique,” Aquat Procedia, vol. 4, pp. 1070–1077, 2015, doi: 10.1016/j.aqpro.2015.02.135.
17. Rustam et al., “An Artificial Neural Network Model for Water Quality and Water Consumption Prediction,” Water (Switzerland), vol. 14, no. 21, Nov. 2022, doi: 10.3390/w14213359.
18. H. H. Aldhyani, M. Al-Yaari, H. Alkahtani, and M. Maashi, “Water Quality Prediction Using Artificial Intelligence Algorithms,” Appl Bionics Biomech, vol. 2020, 2020, doi: 10.1155/2020/6659314.
19. Jaffar, N. M. Thamrin, M. S. A. M. Ali, M. F. Misnan, and A. I. M. Yassin, “WATER QUALITY PREDICTION USING LSTM-RNN: A REVIEW,” J Sustain Sci Manag, vol. 17, no. 7, pp. 204–225, 2022, doi: 10.46754/jssm.2022.07.015.
20. Liu, J. Wang, A. Sangaiah, Y. Xie, and X. Yin, “Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment,” Sustainability, vol. 11, no. 7, p. 2058, Apr. 2019, doi: 10.3390/su11072058.