Assess the Bottom Sediment Quality of the Saigon River and its Tributaries in Binh Duong Using an Artificial Neural Network
Main Article Content
Abstract
Assessment of bottom sediment quality is vital in water resources management. The study collected sediment data at five monitoring stations in the Saigon River and its tributaries from 2013 to 2023. These monitoring data was used to construct our feedforward neutral network (FFNN) model. The model’s accuracy is 95.5% in assessing sediment quality. The study also presents a speedy method for evaluating the quality of bottom sediments in Vietnam.
Keywords:
Artificial neural network, Binh Duong, Classify, Saigon River, and Sediment.
References
[2] G. Suresh, P. Sutharsan, V. Ramasamy, R. Venkatachalapathy, Assessment of Spatial Distribution and Potential Ecological Risk of the Heavy Metals in Relation to Granulometric Contents of Veeranam Lake Sediments, India, Ecotoxicology and Environmental Safety, Vol. 84, 2012, pp. 117-124, https://doi.org/10.1016/j.ecoenv.2012.06.027.
[3] A. Zahra, M. Z. Hashmi, R. N. Malik, Z. Ahmed, Enrichment and Geo-Accumulation of Heavy Metals and Risk Assessment of Sediments of the Kurang Nallah-Feeding Tributary of the Rawal Lake Reservoir, Pakistan, Science of the Total Environment, Vol. 470-471, 2014, pp. 925-933, https://doi.org/10.1016/j.scitotenv.2013.10.017.
[4] J. Zhou, K. Feng, Z. Pei, M. Lu, Pollution Assessment and Spatial Variation of Soil Heavy Metals in Lixia River Region of Eastern China, Journal Soils Sediments, Vol. 16, 2016, pp. 748-755, https://doi.org/10.1007/s11368-015-1289-x.
[5] K. O. Adebowale, F. O. Agunbiade, B. O. Owolabi, Fuzzy Comprehensive Assessment of Metal Contamination of Water and Sediments in Ondo Estuary, Nigeria, Chemistry Ecology, Vol. 24,
No. 4, 2008, pp. 269-283.
[6] S. A. Rounds, Development of a Neural Network Model for Dissolved Oxygen in the Tualatin River, Oregon, Second Federal Interagency Hydrologic Modeling Conference, Hydrology of the Interagency Advisory Committeeon Water Information, Las Vegas, Nevada, 2002.
[7] P. S. Rosman, Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks, ARPN Journal of Engineering and Applied Sciences, Vol. 11,
No. 11, 2016, pp. 7298-7303.
[8] M. A. Guerra, C. G. Piñuela, A. Andrés, B. Galán, J. R. Viguri, Assessment of Self-Organizing Map Artificial Neural Networks for the Classification of Sediment Quality, Environment International,
Vol. 34, 2008, pp. 782-790, https://doi.org/10.1016/j.envint.2008.01.006.
[9] R. Olawoyin, A. Nieto, R. L. Grayson, F. Hardisty, S. Oyewole, Application of Artificial Neural Network (ANN) - Self-Organizing Map (SOM) for the Categorization of Water, Soil and Sediment Quality in Petrochemical Regions. Expert Systems with Applications, Vol. 40, No. 9, 2013, pp. 3634-3648, https://doi.org/10.1016/j.eswa.2012.12.069.
[10] P. T. Duong, H. T. K. Tram, Study and Assessment of the Concentration of some Heavy Metals in Bottom Sediments of the Mekong River Estuary, Ho Chi Minh City University of Education Journal of Science, Vol. 9, No. 75, 2015, pp. 119-129, https://doi.org/10.54607/hcmue.js.0.9(75).702(2015) (in Vietnamese).
[11] T. T. M. Trang, N. T. M. Yen, N. X. Quang, Organic Pollution in Sediments at Selected Areas of the Saigon River, 7th National Scientific Conference on Ecology and Biological Resources, Hanoi, 2017, pp. 1970-1978 (in Vietnamese).
[12] L. T. Trinh, Assessment of the Accumulation and Ecological Risk of Some Heavy Metals in Sediments of the Han River Estuary, Da Nang City, VNU Journal of Science: Natural Sciences and Technology, Vol. 33, No. 3, 2017, pp. 112-119
(in Vietnamese).
[13] G. Steyl, Application of Artificial Neural Networks in the Field of Geohydrology, Master Science Degree, Natural and Agricultural Sciences, Institute of Groundwater Studies, Faculty of Natural and Agricultural Sciences University of the Free State, 2009.
[14] Q. Zhang, M. Feng, X. Hao, Application of Nemerow Index Method and Integrated Water Quality Index Method in Water Quality Assessment of Zhangze Reservoir, Earth and Environmental Science, Vol. 128, 2018, pp. 1-6.
[15] T. Tang, Y. Zhai, K. Huang, Water Quality Analysis and Recommendations through Comprehensive Pollution Index Method, Management Science and Engineering, Vol. 5,
No. 2, 2011, pp. 95-100, http://dx.doi.org/10.3968/j.mse.1913035X20110502.011.
[16] H. Banejad, E. Olyaie, Application of an Artificial Neural Network Model to Rivers Water Quality Indexes Prediction – a Case Study, Computer Science, Engineering, Environmental Science, Vol. 7, No. 1, 2011, pp. 60-65, https://api.semanticscholar.org/CorpusID:8993366.
[17] Y. Ju, C. Chen, Y. C. Lim, C. Tsai, C. Chen,
C. Dong, Developing Ecological Risk Assessment of Metals Released from Sediment based on Sediment Quality Guidelines Linking with the Properties: a Case Study for Kaohsiung Harbor, Science of the Total Environment, Vol. 852,
No. 158407, 2022, pp. 1-11, https://doi.org/10.1016/j.scitotenv.2022.158407.
[18] K.Su, Q. Wang, L. Li, R. Cao, Y. Xi, Water Quality Assessment of Lugu Lake Based on Nemerow Pollution Index Method, Scientific Reports,
Vol. 12, No. 13613, 2022, https://doi.org/10.1038/s41598-022-17874-w.
[19] T. Zeyneb, M. Nadir, R. Boualem, Modeling of Suspended Sediment Concentrations by Artificial Neural Network and Adaptive Neuro Fuzzy Interference System Method - Study of Five Largest Basins in Eastern Algeria, Water Practice & Technology, Vol. 17, No. 5, 2022, pp. 1058-1081, https://doi.org/10.2166/wpt.2022.050.
[20] B. Joshi, V. K. Singh, D. K. Vishwakarma, M. A. Ghorbani, S. Kim, S. Gupta, V. K. Chandola, J. Rajput, I. Chung, K. K. Yadav, E. Mirzania, N. Al-Ansari, M. A. Mattar, A Comparative Survey between Cascade Correlation Neural Network (CCNN) and Feedforward Neural Network (FFNN) Machine Learning Models for Forecasting Suspended Sediment Concentration, Scientific Reports, 2024, Vol. 14, No. 10638, 2024, https://doi.org/10.1038/s41598-024-61339-1.
[21] K. Su, Q. Wang, L. Li, R. Cao, Y. Xi, G. Li, Water Quality Assessment Based on Nemerow Pollution Index Method: a Case Study of Heilongtan Reservoir in Central Sichuan Province, China, PLoS ONE, Vol. 17, No. 8, 2022, https://doi.org/10.1371/journal.pone.0273305.
[22] N. X. Tong, N.T . T. Thao, L. H. Anh, Assessment of Water Quality, Heavy Metal Pollution and Human Health Risks in the Canal System of Ho Chi Minh City, Vietnam, Environmental Research Communications, Vol. 6, No. 7, 2024, https://doi.org/10.1088/2515-7620/ad5ad7.
[3] A. Zahra, M. Z. Hashmi, R. N. Malik, Z. Ahmed, Enrichment and Geo-Accumulation of Heavy Metals and Risk Assessment of Sediments of the Kurang Nallah-Feeding Tributary of the Rawal Lake Reservoir, Pakistan, Science of the Total Environment, Vol. 470-471, 2014, pp. 925-933, https://doi.org/10.1016/j.scitotenv.2013.10.017.
[4] J. Zhou, K. Feng, Z. Pei, M. Lu, Pollution Assessment and Spatial Variation of Soil Heavy Metals in Lixia River Region of Eastern China, Journal Soils Sediments, Vol. 16, 2016, pp. 748-755, https://doi.org/10.1007/s11368-015-1289-x.
[5] K. O. Adebowale, F. O. Agunbiade, B. O. Owolabi, Fuzzy Comprehensive Assessment of Metal Contamination of Water and Sediments in Ondo Estuary, Nigeria, Chemistry Ecology, Vol. 24,
No. 4, 2008, pp. 269-283.
[6] S. A. Rounds, Development of a Neural Network Model for Dissolved Oxygen in the Tualatin River, Oregon, Second Federal Interagency Hydrologic Modeling Conference, Hydrology of the Interagency Advisory Committeeon Water Information, Las Vegas, Nevada, 2002.
[7] P. S. Rosman, Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks, ARPN Journal of Engineering and Applied Sciences, Vol. 11,
No. 11, 2016, pp. 7298-7303.
[8] M. A. Guerra, C. G. Piñuela, A. Andrés, B. Galán, J. R. Viguri, Assessment of Self-Organizing Map Artificial Neural Networks for the Classification of Sediment Quality, Environment International,
Vol. 34, 2008, pp. 782-790, https://doi.org/10.1016/j.envint.2008.01.006.
[9] R. Olawoyin, A. Nieto, R. L. Grayson, F. Hardisty, S. Oyewole, Application of Artificial Neural Network (ANN) - Self-Organizing Map (SOM) for the Categorization of Water, Soil and Sediment Quality in Petrochemical Regions. Expert Systems with Applications, Vol. 40, No. 9, 2013, pp. 3634-3648, https://doi.org/10.1016/j.eswa.2012.12.069.
[10] P. T. Duong, H. T. K. Tram, Study and Assessment of the Concentration of some Heavy Metals in Bottom Sediments of the Mekong River Estuary, Ho Chi Minh City University of Education Journal of Science, Vol. 9, No. 75, 2015, pp. 119-129, https://doi.org/10.54607/hcmue.js.0.9(75).702(2015) (in Vietnamese).
[11] T. T. M. Trang, N. T. M. Yen, N. X. Quang, Organic Pollution in Sediments at Selected Areas of the Saigon River, 7th National Scientific Conference on Ecology and Biological Resources, Hanoi, 2017, pp. 1970-1978 (in Vietnamese).
[12] L. T. Trinh, Assessment of the Accumulation and Ecological Risk of Some Heavy Metals in Sediments of the Han River Estuary, Da Nang City, VNU Journal of Science: Natural Sciences and Technology, Vol. 33, No. 3, 2017, pp. 112-119
(in Vietnamese).
[13] G. Steyl, Application of Artificial Neural Networks in the Field of Geohydrology, Master Science Degree, Natural and Agricultural Sciences, Institute of Groundwater Studies, Faculty of Natural and Agricultural Sciences University of the Free State, 2009.
[14] Q. Zhang, M. Feng, X. Hao, Application of Nemerow Index Method and Integrated Water Quality Index Method in Water Quality Assessment of Zhangze Reservoir, Earth and Environmental Science, Vol. 128, 2018, pp. 1-6.
[15] T. Tang, Y. Zhai, K. Huang, Water Quality Analysis and Recommendations through Comprehensive Pollution Index Method, Management Science and Engineering, Vol. 5,
No. 2, 2011, pp. 95-100, http://dx.doi.org/10.3968/j.mse.1913035X20110502.011.
[16] H. Banejad, E. Olyaie, Application of an Artificial Neural Network Model to Rivers Water Quality Indexes Prediction – a Case Study, Computer Science, Engineering, Environmental Science, Vol. 7, No. 1, 2011, pp. 60-65, https://api.semanticscholar.org/CorpusID:8993366.
[17] Y. Ju, C. Chen, Y. C. Lim, C. Tsai, C. Chen,
C. Dong, Developing Ecological Risk Assessment of Metals Released from Sediment based on Sediment Quality Guidelines Linking with the Properties: a Case Study for Kaohsiung Harbor, Science of the Total Environment, Vol. 852,
No. 158407, 2022, pp. 1-11, https://doi.org/10.1016/j.scitotenv.2022.158407.
[18] K.Su, Q. Wang, L. Li, R. Cao, Y. Xi, Water Quality Assessment of Lugu Lake Based on Nemerow Pollution Index Method, Scientific Reports,
Vol. 12, No. 13613, 2022, https://doi.org/10.1038/s41598-022-17874-w.
[19] T. Zeyneb, M. Nadir, R. Boualem, Modeling of Suspended Sediment Concentrations by Artificial Neural Network and Adaptive Neuro Fuzzy Interference System Method - Study of Five Largest Basins in Eastern Algeria, Water Practice & Technology, Vol. 17, No. 5, 2022, pp. 1058-1081, https://doi.org/10.2166/wpt.2022.050.
[20] B. Joshi, V. K. Singh, D. K. Vishwakarma, M. A. Ghorbani, S. Kim, S. Gupta, V. K. Chandola, J. Rajput, I. Chung, K. K. Yadav, E. Mirzania, N. Al-Ansari, M. A. Mattar, A Comparative Survey between Cascade Correlation Neural Network (CCNN) and Feedforward Neural Network (FFNN) Machine Learning Models for Forecasting Suspended Sediment Concentration, Scientific Reports, 2024, Vol. 14, No. 10638, 2024, https://doi.org/10.1038/s41598-024-61339-1.
[21] K. Su, Q. Wang, L. Li, R. Cao, Y. Xi, G. Li, Water Quality Assessment Based on Nemerow Pollution Index Method: a Case Study of Heilongtan Reservoir in Central Sichuan Province, China, PLoS ONE, Vol. 17, No. 8, 2022, https://doi.org/10.1371/journal.pone.0273305.
[22] N. X. Tong, N.T . T. Thao, L. H. Anh, Assessment of Water Quality, Heavy Metal Pollution and Human Health Risks in the Canal System of Ho Chi Minh City, Vietnam, Environmental Research Communications, Vol. 6, No. 7, 2024, https://doi.org/10.1088/2515-7620/ad5ad7.