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Combining artificial neural networks and genetic algorithms to model nitrate contamination in groundwater

Author

Listed:
  • Vahid Gholami

    (University of Guilan)

  • Hossein Sahour

    (Western Michigan University)

  • Mohammad Reza Khaleghi

    (Islamic Azad University)

  • Yasser Ebrahimian Ghajari

    (Babol Noshirvani University of Technology)

  • Soheil Sahour

    (Rouzbahan Institute of Higher Education)

Abstract

Increasing the concentration of nitrates in aquifer systems reduces water quality and causes serious diseases and complications for human health. Therefore, it is important to monitor nitrate levels in groundwater resources and identify contaminated aquifers. In this research, multiple artificial neural network (ANN) structures and a genetic algorithm (GA) were combined to predict groundwater nitrate levels using its affecting factors in Mazandaran plain (north of Iran). Five ANN algorithms were trained, and their performances were evaluated during the training, cross-validating, and testing stages. Then, GA was combined with the ANNs, and the process of training, cross-validation, and testing was repeated with the same data. The results showed the factors of distance from industrial centers (R = − 0.57), population density (R = 0.56), and groundwater depth (R = − 0.15) are the most important factors in groundwater nitrate contamination. Further, a modular neural network (MNN) model showed the highest performance among the networks used in nitrate concentration modeling. Additionally, combining ANNs with GA enhanced the performance of the models in predicting nitrate concentration. The MSE and R-srqr of the MNN model in the test stage were estimated to be 0.2 and 0.7, respectively. After combining with GA, these values were improved to 0.1 and 0.8, respectively. Finally, the zoning map of nitrate pollution in groundwater was prepared using the ANN-GA hybrid model in the GIS environment. The cost-effective methodology presented in this study can be used to predict the spatial and temporal changes of nitrates in the study area and other areas with similar settings.

Suggested Citation

  • Vahid Gholami & Hossein Sahour & Mohammad Reza Khaleghi & Yasser Ebrahimian Ghajari & Soheil Sahour, 2024. "Combining artificial neural networks and genetic algorithms to model nitrate contamination in groundwater," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(5), pages 4789-4809, March.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:5:d:10.1007_s11069-023-06387-y
    DOI: 10.1007/s11069-023-06387-y
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    3. Ramasamy, Nacha & Krishnan, Palaniappa & Bernard, John C. & Ritter, William F., 2003. "Modeling Nitrate Concentration In Ground Water Using Regression And Neural Networks," Staff Papers 15825, University of Delaware, Department of Food and Resource Economics.
    4. Sina Sadeghfam & Yousef Hassanzadeh & Ata Allah Nadiri & Mahdi Zarghami, 2016. "Localization of Groundwater Vulnerability Assessment Using Catastrophe Theory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4585-4601, October.
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