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Research Article

Predication of Water Pollution Peak Concentrations by Hybrid BP Artificial Neural Network Coupled with Genetic Algorithm

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Article: 2341356 | Received 11 Aug 2023, Accepted 25 Mar 2024, Published online: 13 Apr 2024

References

  • Abbaszade, G., D. Tserendorj, N. Salazar-Yanez, D. Zacháry, P. Völgyesi, E. Tóth, and C. Szabó. 2022. Lead and stable lead isotopes as tracers of soil pollution and human health risk assessment in former industrial cities of Hungary. Applied geochemistry 145:105397. doi:10.1016/j.apgeochem.2022.105397.
  • Abouelsaad, O., E. Matta, M. Omar, and R. Hinkelmann. 2022. Numerical simulation of dissolved oxygen as a water quality indicator in artificial lagoons–case study el gouna. Egypt Regional Studies in Marine Science 56:102697. doi:10.1016/j.rsma.2022.102697.
  • Agbeshie, A. A., and S. Abugre. 2021. Soil properties and tree growth performance along a slope of a reclaimed land in the rain forest agroecological zone of Ghana. Scientific African 13:e00951. doi:10.1016/j.sciaf.2021.e00951.
  • Alasl, M. K., M. Khosravi, M. Hosseini, G. R. Pazuki, and R. Nezakati Esmail Zadeh. 2012. Measurement and mathematical modelling of nutrient level and water quality parameters. Water Science & Technology 66 (9):1962–24. doi:10.2166/wst.2012.333.
  • Ambrose, R. B., T. A. Wool, and T. O. Barnwell. 2009. Development of water quality modeling in the United States. Environmental Engineering Research 14 (4):200–10. doi:10.4491/eer.2009.14.4.200.
  • Badalge, N. D. K., J. Kim, S. Y. Lee, B. J. Lee, and J. Hur. 2024. Land use effects on spatiotemporal variations of dissolved organic matter fluorescence and water quality parameters in watersheds, and their interrelationships. Journal of Hydrology 631:130840. doi:10.1016/j.jhydrol.2024.130840.
  • Borode, A., and P. Olubambi. 2023. Modelling the effects of mixing ratio and temperature on the thermal conductivity of GNP-Alumina hybrid nanofluids: A comparison of ANN, RSM, and linear regression methods. Heliyon 9 (8):e19228. doi:10.1016/j.heliyon.2023.e19228.
  • Burchard-Levine, A., S. Liu, F. Vince, M. Li, and A. Ostfeld. 2014. A hybrid evolutionary data driven model for river water quality early warning. Journal of Environmental Management 143:8–16. doi:10.1016/j.jenvman.2014.04.017.
  • Costa, C. M. D. S. B., I. R. Leite, A. K. Almeida, and I. K. de Almeida. 2021. Choosing an appropriate water quality model–A review. Environmental Monitoring and Assessment 193 (1):1–15. doi:10.1007/s10661-020-08786-1.
  • Cuss, C. W., M. Ghotbizadeh, I. Grant-Weaver, M. B. Javed, T. Noernberg, and W. Shotyk. 2021. Delayed mixing of iron-laden tributaries in large boreal rivers: Implications for iron transport, water quality and monitoring. Journal of Hydrology 597:125747. doi:10.1016/j.jhydrol.2020.125747.
  • Ding, Q., G. Cheng, Y. Wang, and D. Zhuang. 2017. Effects of natural factors on the spatial distribution of heavy metals in soils surrounding mining regions. Science of Total Environment 578:577–85. doi:10.1016/j.scitotenv.2016.11.001.
  • Ding, F., W. J. Zhang, S. H. Cao, S. L. Hao, L. Y. Chen, X. Xie, W. P. Li, and J. M. Jiang. 2023. Optimization of water quality index models using machine learning approaches. Water Research 243:120337. doi:10.1016/j.watres.2023.120337.
  • Duraisamy, K., G. Iaccarino, and H. Xiao. 2019. Turbulence modeling in the age of data. Annual Review of Fluid Mechanics 51 (1):357–77. doi:10.1146/annurev-fluid-010518-040547.
  • Feng, H. Y., J. Schyns, S. Maarten, M. Kroll, M. J. Yang, H. Su, Y. Y. Liu, Y. P. Lv, X. B. Zhang, K. Yang, et al. 2024. Water pollution scenarios and response options for China. Science of the Total Environment 914:169807–13. doi:10.1016/j.scitotenv.2023.169807.
  • García-Alba, J., J. F. Barcena, C. Ugarteburu, and A. García. 2019. Artificial neural networks as emulators of process-based models to analyse bathing water quality in estuaries. Water Research 150:283–95. doi:10.1016/j.watres.2018.11.063.
  • Georgescu, P. L., S. Moldovanu, C. Iticescu, M. Calmuc, V. Calmuc, C. Topa, and L. Moraru. 2023. Assessing and forecasting water quality in the Danube River by using neural network approaches. Science of the Total Environment 879:162998–14. doi:10.1016/j.scitotenv.2023.162998.
  • Haggerty, R. J. X. S., H. F. Yu, Y. S. Li, and Y. Li. 2023. Application of machine learning in groundwater quality modeling – a comprehensive review. Water Research 233:119745. doi:10.1016/j.watres.2023.119745.
  • Hao, J., Y. Lin, G. Ren, G. Yang, X. Han, X. Wang, and Y. Feng. 2021. Comprehensive benefit evaluation of conservation tillage based on BP neural network in the Loess Plateau. Soil and Tillage Research 205:104784. doi:10.1016/j.still.2020.104784.
  • Kim, H. G., K. H. Cho, and F. Recknagel. 2023. Time-series modelling of harmful cyanobacteria blooms by convolutional neural networks and wavelet generated time-frequency images of environmental driving variables. Water Research 246:120662. doi:10.1016/j.watres.2023.120662.
  • Kumar, M. A., N. Srinivas, P. Ramya, N. Ahlawat, J. Sharma, and F. Vinod. 2024. Monitoring the quality of ground water in pipelines using deep neural network model. Groundwater for Sustainable Development 24:101073. doi:10.1016/j.gsd.2023.101073.
  • Lei, K. G., Y. Li, Y. B. Zhang, S. Y. Wang, E. Yu, F. Li, F. Xiao, and F. Xia. 2023. Development of a new method framework to estimate the nonlinear and interaction relationship between environmental factors and soil heavy metals. Science of the Total Environment 905:167133. doi:10.1016/j.scitotenv.2023.167133.
  • Liao, X., T. Chen, H. Xie, and Y. Liu. 2005. Soil as contamination and its risk assessment in areas near the industrial districts of Chenzhou City, Southern China. Environment international 31 (6):791–98. doi:10.1016/j.envint.2005.05.030.
  • Li, T., Y. Liu, S. Lin, Y. Liu, and Y. Xie. 2019b. Soil pollution management in China: A brief introduction. Sustainability 11 (3):556. doi:10.3390/su11030556.
  • Lin, F., H. L. Ren, J. S. Qin, M. Q. Wang, M. Shi, Y. C. Li, R. J. Wang, and Y. M. Hu. 2024. Analysis of pollutant dispersion patterns in rivers under different rainfall based on an integrated water-land model. Journal of Environmental Management 354:120314. doi:10.1016/j.jenvman.2024.120314.
  • Li, R., C. Y. Tang, X. Li, T. Jiang, Y. P. Shi, and Y. J. Cao. 2019a. Reconstructing the historical pollution levels and ecological risks over the past sixty years in sediments of the Beijiang River, South China. Science of the Total Environment 649:448–60. doi:10.1016/j.scitotenv.2018.08.283.
  • Liu, Z. Y., Y. Fei, H. D. Shi, L. Mo, and J. X. Qi. 2022. Prediction of high-risk areas of soil heavy metal pollution with multiple factors on a large scale in industrial agglomeration areas. Science of the Total Environment 808:151874–12. doi:10.1016/j.scitotenv.2021.151874.
  • Li, D., Y. Wei, Z. Dong, C. Wang, and C. Wang. 2021. Quantitative study on the early warning indexes of conventional sudden water pollution in a plain river network. Journal of Cleaner Production 303:127067. doi:10.1016/j.jclepro.2021.127067.
  • Li, J. X., L. C. Wu, L. M. Chen, J. Zhang, Z. H. Shi, J. X. Li, L. C. Wu, L. M. Chen, J. Zhang, Z. H. Shi, et al. 2024. Effects of slopes, rainfall intensity and grass cover on runoff loss of mercury from floodplain soil in Oak Ridge TN: A laboratory pilot study. Geoderma 441:116750. doi:10.1016/j.geoderma.2023.116750.
  • Markus, M., M. I. Hejazi, P. Bajcsy, O. Giustolisi, and D. A. Savic. 2010. Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois. Journal of Hydroin-Formatics 12 (3):251–61. doi:10.2166/hydro.2010.064.
  • Mulia, I. E., H. Tay, K. Roopsekhar, and P. Tkalich. 2013. Hybrid ANN–GA model for predict-ing turbidity and chlorophyll-a concentrations. Journal of Hydro-Environment Research 7 (4):279–99. doi:10.1016/j.jher.2013.04.003.
  • Muttil, N., and J. H. Lee. 2005. Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecological Modelling 189 (3–4):363–76. doi:10.1016/j.ecolmodel.2005.03.018.
  • Pany, R., A. Rath, and P. C. Swain. 2023. Water quality assessment for River Mahanadi of Odisha, India using statistical techniques and artificial neural networks. Journal of Cleaner Production 417:137713. doi:10.1016/j.jclepro.2023.137713.
  • Poudyal, S., T. A. Cochrane, and R. Bello-Mendoza. 2021. Carpark pollutant yields from first flush stormwater runoff. Environmental Challenges 5:100301. doi:10.1016/j.envc.2021.100301.
  • Qiao, P. W., S. Wang, J. B. Li, Q. Y. Zhao, Y. Wei, M. Lei, J. Yang, and Z. G. Zhang. 2023. Process, influencing factors, and simulation of the lateral transport of heavy metals in surface runoff in a mining area driven by rainfall: A review. Science of the Total Environment 857:159119. doi:10.1016/j.scitotenv.2022.159119.
  • Radovanović, S., M. Milivojević, B. Stojanović, S. Obradović, D. Divac, and N. Milivojević. 2022. Modeling of Water Losses in Hydraulic Tunnels under Pressure Based on Stepwise Regression Method. Applied Sciences 12(18):9019. doi: 10.3390/app12189019.
  • Rossi, E., I. Pecorini, and R. Iannelli. 2022. Multilinear regression model for biogas production prediction from dry anaerobic digestion of OFMSW. Sustainability 14 (8):4393. doi:10.3390/su14084393.
  • Schmidt, S. I., J. Hejzlar, J. KopᡠCek, M. C. Paule-Mercado, P. Porcal, and Y. Vystavna. 2021. Relationships between a catchment-scale forest disturbance index, time delays, and chemical properties of surface water. Ecological Indicators 125:107558. doi:10.1016/j.ecolind.2021.107558.
  • Selim, A., S. N. A. Shuvo, M. M. Islam, M. Moniruzzaman, S. Shah, and M. Ohiduzzaman. 2023. Predictive models for dissolved oxygen in an urban lake by regression analysis and artificial neural network. Total Environment Research Themes 7 (7):100066. doi:10.1016/j.totert.2023.100066.
  • Sharma, M. J., and S. J. Yu. 2015. Stepwise regression data envelopment analysis for variable reduction. Applied Mathematics and Computation 253:126–34. doi:10.1016/j.amc.2014.12.050.
  • Sterc, T. B., B. Filipovic-Grcic, B. Fran, and K. Mesic. 2023. Methods for estimation of OHL conductor temperature based on ANN and regression analysis. International Journal of Electrical Power & Energy Systems 151:109192. doi:10.1016/j.ijepes.2023.109192.
  • Sun, H., X. Y. Hu, X. H. Yang, H. Wang, and J. H. Cheng. 2023. Estimating water pollution and economic cost embodied in the mining industry: An interprovincial analysis in China. Resources Policy 86:104284–12. doi:10.1016/j.resourpol.2023.104284.
  • Sun, Y. X., Q. S. Jiang, R. Zou, W. J. Ma, M. C. Hu, Y. H. Chen, and Y. Liu. 2024. Exploring nonlinear responses of lake nutrients and algal blooms to restoration measures: A three-dimensional flux network modelling approach. Journal of Hydrology 631:130723. doi:10.1016/j.jhydrol.2024.130723.
  • Su, H., R. Zou, X. L. Zhang, Z. Y. Liang, R. Ye, and Y. Liu. 2022. Exploring the type and strength of nonlinearity in water quality responses to nutrient loading reduction in shallow eutrophic water bodies: Insights from many numerical simulations. Journal of Environmental Management 313:115000. doi:10.1016/j.jenvman.2022.115000.
  • Syeed, M., M. Hossain, M. Karim, F. Uddin, M. Hasan, and R. Khan. 2023. Surface water quality profiling using the water quality index, pollution index and statistical methods: A critical review. Environmental and Sustainability Indicators 18 (3):100247–23. doi:10.1016/j.indic.2023.100247.
  • Tan, X. Y., Y. W. Jia, D. W. Yang, C. W. Niu, and C. Hao. 2024. Impact ways and their contributions to vegetation-induced runoff changes in the loess plateau. Journal of Hydrology: Regional Studies.51:101630. doi:10.1016/j.ejrh.2023.101630.
  • Uddin, M. G., M. T. M. Diganta, A. M. Sajib, A. Rahman, S. Nash, T. Dabrowski, R. Ahmadian, M. Hartnett, and A. I. Olbert. 2023. Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model. Environmental Pollution 336:122456. doi:10.1016/j.envpol.2023.
  • Uddin, M. G., S. Nash, and A. Olbert. 2021. A review of water quality index models and their use for assessing surface water quality. Ecological Indicators 122:107218–21. doi:10.1016/j.ecolind.2020.107218.
  • Uddin, M. G., S. Nash, A. Rahman, and A. Olbert. 2022a. A comprehensive method for improvement of water quality index (WQI). Water Research 219:118532. doi:10.1016/j.watres.2022.118532.
  • Uddin, M. G., S. Nash, A. Rahman, and A. Olbert. 2022b. A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment. Water Research 216:118532–20. doi:10.1016/j.watres.2022.118532.
  • Uddin, M. G., S. Nash, A. Rahman, and A. Olbert. 2023a. Assessing optimization techniques for improving water quality model. Journal of Cleaner Production 385:135671. doi:10.1016/j.jclepro.2022.135671.
  • Uddin, M. G., S. Nash, A. Rahman, and A. Olbert. 2023b. Marine waters assessment using improved water quality model incorporating machine learning approaches. Journal of Environmental Management 344:118368. doi:10.1016/j.jenvman.2023.118368.
  • Uddin, M. G., S. Nash, A. Rahman, and A. Olbert. 2023c. A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches. Water Research 229:119422–21. doi:10.1016/j.watres.2022.119422.
  • Uddin, M. G., S. Nash, A. Rahman, and A. Olbert. 2023d. Performance analysis of the water quality index model for predicting water state using machine learning techniques. Process Safety and Environmental Protection 169:808–28. doi:10.1016/j.psep.2022.11.073.
  • Uddin, M. G., S. Nash, A. Rahman, and A. Olbert. 2023e. A sophisticated model for rating water quality. Science of the Total Environment 868:161614. doi:10.1016/j.scitotenv.2023.161614.
  • Uddin, M. G., A. Rahman, S. Nash, M. T. M. Diganta, A. M. Sajib, M. Moniruzzaman, and A. Olbert. 2023. Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland. Science of the Total Environment 901:165960. doi:10.1016/j.scitotenv.2023.165960.
  • Ugochukwu, U. C., N. Chukwuone, C. Jidere, B. Ezeudu, C. Ikpo, and J. Ozor. 2022. Heavy metal contamination of soil, sediment, and water due to galena mining in Ebonyi State Nigeria: Economic costs of pollution based on exposure health risks. Journal of Environmental Management 321:115864–69. doi:10.1016/j.jenvman.2022.115864.
  • Wang, J., Y. Geng, Q. Zhao, Y. Zhang, Y. Miao, X. Yuan, W. Zhang, and W. Zhang. 2021. Water quality prediction of water sources based on meteorological factors using the CA-NARX approach. Journal of Environmental Modeling & Assessment 26 (4):529–41. doi:10.1007/s10666-021-09759-5.
  • Wiegand, R. E. 2010. Performance of using multiple stepwise algorithms for variable selection. Statistics in Medicine 29 (15):1647–59. doi:10.1002/sim.3943.
  • Wiering, M., S. Kirschke, and N. U. Akif. 2023. Addressing diffuse water pollution from agriculture: Do governance structures matter for the nature of measures taken? Journal of Environmental Management 332:117329–9. doi:10.1016/j.jenvman.2023.117329.
  • Wu, B., and T. B. Chen. 2010. Changes in hair arsenic concentration in a population exposed to heavy pollution: Follow-up investigation in Chenzhou City, Hunan Province, Southern China. Journal of Environmental Sciences 22 (2):283–89. doi:10.1016/S1001-0742(09)60106-6.
  • Xie, D. N., B. Zhao, R. H. Kang, X. X. Ma, T. Larssen, Z. D. Jin, and L. Duan. 2024. Delayed recovery of surface water chemistry from acidification in subtropical forest region of China. Science of the Total Environment 912:169126. doi:10.1016/j.scitotenv.2023.169126.
  • Xu, B., Q. Xu, C. Liang, L. Li, and L. Jiang. 2015. Occurrence and health risk assessment of trace heavy metals via groundwater in Shizhuyuan Polymetallic Mine in Chenzhou City, China. Front Environmental Science and Engineering 9 (3):482–93. doi:10.1007/s11783-014-0675-8.
  • Yi, X., Z. Wang, S. Liu, X. Hou, and Q. Tang. 2022. An accelerated degradation durability evaluation model for the turbine impeller of a turbine based on a genetic algorithms back-propagation neural network. Applied Sciences 12 (18):9302. doi:10.3390/app12189302.
  • Yuan, S., and J. Chen. 2018. Analysis on the construction of nonferrous metal logistics park in ZX City of Hunan Province. In 2018 International Conference on Economy, Management and Entrepreneurship (ICOEME 2018). Atlantis Press, 224–27. doi:10.1016/j.gexplo.2014.09.010
  • Yu, H., G. Jin, S. Jin, Z. Chen, W. Fan, and D. Xiao. 2022. Numerical modeling of COD transportation in Liaodong Bay: Impact of COD loads from rivers flowing into the sea. Water 14 (19):3114. doi:10.3390/w14193114.
  • Yu, Q. W., Z. H. Sun, J. Y. Shen, X. Xu, Q. Y. Han, and M. Zhu. 2023. The nonlinear effect of new urbanization on water pollutant emissions: Empirical analysis based on the panel threshold model. Journal of Environmental Management 345:118564. doi:10.1016/j.jenvman.2023.118564.
  • Zhang, Y., S. J. Granger, M. A. Semenov, H. R. Upadhayay, and A. L. Collins. 2022a. Diffuse water pollution during recent extreme wet-weather in the UK Environmental damage costs and insight into the future. Journal of Cleaner Production 338:130633–12. doi:10.1016/j.jclepro.2022.130633.
  • Zhang, Z. Y., J. L. Huang, J. Bian, Y. Huang, J. Cai, and J. Bian. 2022b. Use of interpretable machine learning to identify the factors influencing the nonlinear linkage between land use and river water quality in the Chesapeake Bay watershed. Ecological Indicators 140:108977. doi:10.1016/j.ecolind.2022.108977.
  • Zhou, L. F., X. L. Zhao, Y. B. Meng, Y. Yang, M. M. Teng, F. H. Song, and F. C. Wu. 2022. Identification priority source of soil heavy metals pollution based on source-specific ecological and human health risk analysis in a typical smelting and mining region of South China. Ecotoxicology & Environmental Safety 242:113864–11. doi:10.1016/j.ecoenv.2022.113864.
  • Zou, Y. W., S. Lou, Z. R. Zhang, S. G. Liu, X. S. Zhou, F. Zhou, L. D. Radnaeva, E. Nikitina, and I. V. Fedorova. 2024. Predictions of heavy metal concentrations by physiochemical water quality parameters in coastal areas of Yangtze river estuary. Marine Pollution Bulletin 199:115951. doi:10.1016/j.marpolbul.2023.115951.
  • Zounemat-Kermani, M., and M. Scholz. 2014. Modeling of dissolved oxygen applying stepwise regression and a template-based fuzzy logic system. Journal of Environmental Engineer-Ing 140 (1):69–76. doi:10.1061/(ASCE)EE.1943-7870.0000780.