Spatio-Temporal Optimization of Long-term Groundwater Monitoring Networks Using PSO Algorithm

Document Type : Original Research Article

Authors

1 School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran

2 Water Research Institute, Ministry of Energy, Tehran, Iran

Abstract

Spatial and temporal variations of contamination in groundwater resources, necessitate long-term monitoring (LTM) at a given site. In this study, several groundwater quality parameters (EC, SAR, TH, TDS, pH, K, Na+, Ca2+, Mg2+, SO42-, HCO3-, and Cl-) for 113 samples sites clustered based on the particle swarm optimization (PSO) algorithm to significantly decrease cost and save time in LTM. The optimization of the clustering process was carried out according to the Silhouette index. For verification and validation of the results, Geology, soil order, land use, hydrological network, and TDS maps were used. According to the results, the best number of clusters was 5. An acceptable agreement was obtained between land conditions and clusters represented by the PSO algorithm. Consequently, it can be inferred that the clustering of the groundwater quality using the PSO algorithm and the Silhouette index optimizer could 70% decrease the number of spatio-temporal sampling in LTM.

Keywords

Main Subjects

References

Abu-khalaf, N., Khayat, S. & Natsheh, B., 2013. Multivariate Data Analysis to Identify the Groundwater Pollution Sources in Tulkarm Area / Palestine. Science and Technology, 3(4), 99–104. doi.org/10.5923/j.scit.20130304.01.
Barak, S. & Modarres, M., 2014. Developing an approach to evaluate stocks by forecasting effective features with data mining methods. Expert Systems with Applications, 42, 1325–1339.
Brida, J.G., Disegna, M. & Osti, L., 2012. Segmenting visitors of cultural events by motivation: A sequential non- linear clustering analysis of Italian Christmas Market visitors. Expert Systems with Applications, 39, 11349–11356.
Cardoso, M.F., Salcedo, R.L. & Feyo de Azevedo, S., 1996. The simplex-simulated annealing approach to continuous non-linear optimization. Computers & Chemical Engineering, 20, 1065–1080. https://doi.org/10.1016/j.jmgm.2005.11.005.
Clark, I., 2015. Groundwater geochemistry and isotopes. Taylor & Francis.
Covoes, T.F. & Hruschka, E.R., 2011. Towards improving cluster-based feature selection with a simplified silhouette filter. Information Sciences, 181, 3766–3782. https://doi.org/10.1080/01402390.2011.569130.
Dastorani, M., Mirzavand, M., Dastorani, M.T. & Khosravi, H., 2020. Simulation and prediction of surface water quality using stochastic models. Sustainable Water Resources Management, 6, 1-17.
Dorigo, M., Maniezzo, V. & Colorni, A., 1996. The ant system: optimisation by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B: Cybern, 26, 29–41.
Duan, Q., Sorooshian, S. & Gupta, V.K., 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research, 28, 1015–1031.
Eskandari, E., Mohammadzadeh, H., Nassery, H., Vadiati, M., Zadeh, A.M. & Kisi, O., 2022. Delineation of isotopic and hydrochemical evolution of karstic aquifers with different cluster-based (HCA, KM, FCM and GKM) methods. Journal of Hydrology, 609. https://doi.org/10.1016/j.jhydrol.2022.127706.
Geem, Z.W., Kim, J.H. & Loganathan, G.V., 2001. A new heuristic optimization algorithm: harmony search. Simulation, 76, 60–68.
Handl, J., Knowles, J. & Kell, D.B., 2005. Computational cluster validation in post-genomic data analysis. Journal of Biological Chemistry, 21, 3201–3212.
Hatamlou, A., 2012. In search of optimal centroids on data clustering using a binary search algorithm. Pattern Recognition Letters, 33, 1756–1760.
Hossain, M.G., Selim Reza, A.H.M., Lutfun-Nessa, M. & Ahmed, S.S., 2013. Factor and cluster analysis of water quality data of the groundwater wells of Kushtia, Bangladesh: Implication for arsenic enrichment and mobilization. Journal of the Geological Society of India, 81, 377–384. https://doi.org/10.1007/s12594-013-0048-0.
Hruschka, E.R., Campello, R.J. & De Castro, L.N., 2006. Evolving clusters in gene-expression data. Information Sciences, 176, 1898–1927.
Jha, M. & Datta, B., 2013. Three-Dimensional Groundwater Contamination Source Identification Using Adaptive Simulated Annealing. Journal of Hydrologic Engineering, 18, 307–317.
Karaboga, D., 2005. An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Department of Computer Engineering, Engineering Faculty, Erciyes University.
Kennedy, J. & Eberhart, R., 1995. Partic le swarm optimization, in: The Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan, pp. 39–43.
Khoshnevisan, B., Bolandnazar, E., Barak, S., Shamshirband, S., Maghsoudlou, H., Altameem, T.A. & Gani, A., 2014. A clustering model based on an evolutionary algorithm for better energy use in crop production. Stochastic Environmental Research and Risk Assessment, 1–15.
Kuri-Morales, A. & Rodriguez-Erazo, F., 2009. A search space reduction methodology for data mining in large databases. Engineering Applications of Artificial Intelligence, 22, 57–65.
Li, Y. & Chan Hilton, A.B., 2005. Reducing Spatial Sampling in Long-Term Groundwater Monitoring Networks Using Ant Colony Optimization. International Journal of Computational Intelligence Research, 1, 19–28.
Mehrabian, A.R. & Lucas, C., 2006. A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1, 355–366.
Mirzavand, M. & Ghazavi, R., 2015. A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods. Water Resources Management, 29. https://doi.org/10.1007/s11269-014-0875-9.
Mirzavand, M. & Walter, J., 2024. Delineating the mechanisms controlling groundwater salinization using chemo-isotopic data and meta-heuristic clustering algorithms (case study: Saguenay-Lac-Saint-Jean region in the Canadian Shield, Quebec, Canada). Environmental Science and Pollution Research, https://doi.org/10.1007/s11356-024-33922-6.
Nourani, V., Hosseini Baghanam, A. & Daneshvar Vousoughi, F., Alami, M.T., 2012. Classification of Groundwater Level Data Using SOM to Develop ANN-Based Forecasting Mode. International Journal of Soft Computing and Engineering, 2, 464–469.
Passino, K.M., 2002. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 52–67.
Saruhan, H., 2014. Differential evolution and simulated annealing algorithms for mechanical systems design. International journal of Engineering Science and Technology, 17, 131–1363. https://doi.org/10.1192/bjp.205.1.76a.
Shamshirband, S., Amini, A., Anuar, N.B., Kiah, M.L.M., Wah, T.Y. & Furnell, S., 2014. D-FICCA: a density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks. Measurement, 55, 212–226. https://doi.org/https://doi.org/10.1016/j.measurement.2014.04.034.
Storn, R. & Price, K., 1997. Differential Evolution - a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11, 341–359.
Yang, X.S. & Deb, S., 2010. Engineering optimization by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1, 330–343.
Sustainable Earth Trends
Volume 5, Issue 1 - Serial Number 1
Issue in Progress
2025
Pages 1-12

Files

Share

How to cite

Statistics