Abstract
Groundwater is the main source for water provision in the arid and semi-arid areas such as Iran. The groundwater quality was simulated by using a hybrid model integrating a Self-Organizing Map (SOM) and geographic information system (GIS). SOM and GIS were used as pre-processing and post-processing tools in the Mazandaran Plain. Further, the Ground Water Quality Index (GWQI) and its effective factors were estimated by using digital maps and the secondary data. NeuroSolutions software was used for simulating the groundwater quality. To do this, a model was trained and optimized in the SOM and then the optimized model was tested. In the next step, the performance of SOM in groundwater quality simulation was confirmed (test stage, Rsqr=0.8, and MSE=0.008). Then, the digital maps of the SOM inputs were converted to raster format in GIS. In the last step, a raster layer was generated by combining the model input layers which comprised the model inputs values. The tested SOM was used to simulate GWQI in the sites without the secondary data of the groundwater quality. Finally, the groundwater quality map was generated by coupling the results of SOM estimations and GIS capabilities. The results revealed that the coupling of SOM and GIS has high performance in the simulation of the groundwater quality. According to the results, a limited area of the studied plain has groundwater resources with low quality (GWQI>0.04). Therefore, that will be a threat to the life of humans, animals, and vegetative species. Therefore, it is necessary to plan for managing the groundwater quality in the Mazandaran plain.
Similar content being viewed by others
References
Abbaszadeha M, Hezarkhania A, Soltani-Mohammadi S (2013) A SOM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit. Chemie der Erde. https://doi.org/10.1016/j.chemer.2013.07.001
Affandi AK, Watanabe K (2007) Daily groundwater level fluctuation forecasting using soft computing technique. Nature and Science 5(2): 1–10.
Aleksandrova M, Lamers JPA, Martius C, Tischbein B (2014) Rural vulnerability to environmental change in the irrigated lowlands of Central Asia and options for policy-makers: A review. Environmental Science & Policy. https://doi.org/10.1016/j.envsci.2014.03.001
Bahrami S, Ardejani FD, Baafi E (2016) Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine. Journal of Hydrology 536: 471–484. https://doi.org/10.1016/j.jhydrol.2016.03.002
Bashi-Azghadi SN, Kerachian R, Bazargan-Lari MR, Solouki K (2010) Characterizing an unknown pollution source in groundwater resources systems using PSOM and PNN. Expert Systems with Applications 37:7154–7161. https://doi.org/10.1016/j.eswa.2010.04.019
Bedri Z, Corkery A, O’Sullivan JJ, et al. (2014) An integrated catchment-coastal modeling system for real-time water quality forecasts. Environmental Modelling & Software 61: 458–476. https://doi.org/10.1016/j.envsoft.2014.02.006
Besalatpour AA, Ayoubi S, Hajabbasi MA, et al. (2014) Feature selection using the parallel genetic algorithm for the prediction of the geometric mean diameter of soil aggregates by machine learning methods. Arid Land Research and Management 28(4):383–394. https://doi.org/10.1080/15324982.2013.871599
Bierman P, Lewis M, Ostendorf B, Tanner J (2011) A review of methods for analyzing spatial and temporal patterns in coastal water quality. Ecological Indicators 11: 103–114. https://doi.org/10.1016/j.ecolind.2009.11.001
Brown RM, McClelland NI, Deininger RA, Tozer RG (1970) A water quality index: Do we dare? Water & Sewage Works 117:339–343
Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in the reservoir. Advances in Water Resources 29:1–10. https://doi.org/10.1016/j.advwatres.2005.04.015
Chang LC, Amin MZM, Yang SN, Chang FJ (2018) Building ANN-based regional multi-step-ahead flood inundation forecast models. Water 10:1–18. https://doi.org/10.3390/w10091283.
Cracknell MJ, De Caritat P (2017) Catchment-based gold prospectivity analysis combining geochemical, geophysical and geological data across northern Australia. Geochemistry: Exploration. Environment, Analysis 17: 204–216. https://doi.org/10.1144/geochem2016-012
Dixon B (2005) Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. Journal of Hydrologyl 309 (1–4):17–38. https://doi.org/10.1016/j.jhydrol.2004.11.010
Dixon B (2009) A case study using support vector machines, neural networks, and logistic regression in a GIS to identify wells contaminated with nitrate-N. Hydrology Journal 17: 1507–1520. https://doi.org/10.1007/s10040-009-0451-1
Ehsani AH, Quiel F (2008) Geomorphometric feature analysis using morphometric parametrization and artificial neural networks. Geomorphology 99: 1–12. https://doi.org/10.1016/j.geomorph.2007.10.002
Farifteh J, Farshad A, George RJ (2006) Assessing salt-affected soils using remote sensing, solute modeling, and geophysics. Geoderma 130:191–206. https://doi.org/10.1016/j.geoderma.2005.02.003.
Gholami V, Yousefi Z, Rostami HZ (2010) Modeling of groundwater quality on the Caspian southern coasts. Water Resources Management 24(7): 1415–1424. https://doi.org/10.1007/s11269-009-9506-2
Gholami V, Khaleghi MR, Sebghati M (2016) A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS). Applied Water Science 7(7): 3633–3647. https://doi.org/10.1007/s13201-016-0508-y
Gholami V, Khaleghi MR (2018). A comparative study of the performance of artificial neural network and multivariate regression in simulating springs discharge in the Caspian Southern Watersheds, Iran. Applied Water Science 9(9):1–10. https://doi.org/10.1007/s13201-018-0886-4
Haider K, Engesgaard P, Sonnenborg TO, Kirkegaard C (2014) Numerical modeling of quality distribution and submarine groundwater discharge to a coastal lagoon in Denmark based on airborne electromagnetic data. Hydrogeology Journal 1–17. https://doi.org/10.1007/s10040-014-1195-0
He Z, Wen X, Liu H, Dua J (2014) A comparative study of the artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology 509:379–386. https://doi.org/10.1016/j.jhydrol.2013.11.054
Hentati A, Kawamura A, Amaguchi H, Iseri Y (2010) Evaluation of sedimentation vulnerability at small hillside reservoir in the semi-arid region of Tunisia using Self-Organizing Map. Geomorphology 122(1–2): 56–64. https://doi.org/10.1016/j.geomorph.2010.05.013
Horton RK (1965) An Index-Number System for Rating Water Quality. Journal of the Water Pollution Control Federation 37(3): 300–305. https://doi.org/10.12691/ajwr-1-3-3
Iwashita F, Friedel MJ, Roberto C, Filho S (2011) Using self-organizing maps to analyze high-dimensional geochemistry data across Paraná, Brazil. Conference: 15th Simpósio Brasileiro de Sensoriamento Remoto.
Kanevski M, Parkin R, Pozdnukhov A, et al. (2004) Environmental data mining and modeling based on machine learning algorithms and geostatistics. Environmental Modelling & Software 19(9): 845–855. https://doi.org/10.1016/j.envsoft.2003.03.004
Kisi Ö, Dailr AH, Çimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. Journal of Hydrology (450–451):48–58. https://doi.org/10.1016/j.jhydrol.2012.05.031.
Kisi O, Cengiz TM (2013) Fuzzy genetic approach for estimating reference evapotranspiration of Turkey: Mediterranean Region. Water Resources Management 27(10):3541–3553. https://doi.org/10.1007/s11269-013-0363-7
Klobucar D, Subasic M (2012) Using self-organizing maps in the visualization and analysis of forest inventory. iForest — Biogeosciences and Forestry 5(5):216–223. https://doi.org/10.3832/ifor0629-005
Kohonen T (1982) Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43 (1): 59–69. https://doi.org/10.1007/bf00337288
Kohonen T (2001) Self-Organizing Maps. Third, Extended Edition. Springer Series in Information Sciences vol. 30, Berlin, Germany, Springer-Verlag. ISBN 978-3-540-67921-9
Kohonen T, Hynninen J, Kangas J, Laaksonen J (2005) The Self-Organizing Map Program Package, Helsinki University of Technology. Laboratory of Computer and Information Science. http://www.cis.hut/research/som-pak/Apr 16th, 2005.
Kohonen T Timo H (2011) Kohonen network. Scholarpedia 2: 1568. https://doi.org/10.4249/scholarpedia.1568
Kohonen T (1984) Setf-Organization and Associative Memory. Springer, Berlin.
Lateef K (2011) Evaluation of groundwater quality for drinking purpose for Tikrit and Samarra cities using water quality index. European Journal of Scientific Research 58(4): 472–81.
Liao K, Xu S, Wu J, et al. (2014) Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City, China. Journal of Plant Nutrition and Soil Science 177(5): 775–782. https://doi.org/10.1002/jpln.201300176
Lin G, Chen G, Huang P, Chou Y (2009) Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. Journal of Hydrology 372: 17–29. https://doi.org/10.1016/j.jhydrol.2009.03.032
Lin GF, Lin HY, Wu MC (2013) Development of a support-vector-machine-based model for daily pan evaporation estimation. Hydrological Process 22:3115–3127. https://doi.org/10.1002/hyp.9428
Lin GF, Wang TC, Chen LH (2016) A forecasting approach combining self-organizing map with support vector regression for reservoir inflow during typhoon periods. Advances in Meteorology 1–12. https://doi.org/10.1155/2016/7575126
Maity R, Bhagwat PP, Bhatnagar A (2010) Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrological Process 24:917–923. https://doi.org/10.1002/hyp.7535
Muller SJ, Van Niekerk A (2016) An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level. International Journal of Applied Earth Observation and Geoinformation 49: 138–150. https://doi.org/10.1016/j.jag.2016.02.005
Myburgh G, van Niekerk A (2014) Impact of training set size on object-based land cover classification: a comparison of three classifiers. International Journal of Applied Geospatial Research 5: 49–67. https://doi.org/10.4018/ijagr.2014070104
Nourani V (2014) A review on applications of artificial intelligence-based models to estimate suspended sediment load. International Journal of Soft Computing and Engineering (IJSCE) 3(6):121–127.
Ouyang Y, Zhang JE, Parajuli P (2013) Characterization of shallow groundwater quality in the Lower St. Johns River Basin: a case study. Environmental Science and Pollution Research 20(12):8860–8870. https://doi.org/10.1007/s11356-013-1864-x
Penn BS (2005) Using self-organizing maps to visualize high-dimensional data. Computer & Geosciences 31(5): 531–544. https://doi.org/10.1016/j.cageo.2004.10.009
Ribeiro L, Paralta E, Nascimento J, et al. (2002) A agricultural a delimitac ao das zonas vulnera’veis aos nitratosdeorigem agrycola segundo a Directiva 91/676/CE. In: Proc. III Congreso Ibe’rico sobre Gestio’n e Planificacio’n del Agua; Universidad de Sevilla, Spain. pp 508–513.
Samani N, Gohari-Moghadam M, Safavi AA (2007) A simple neural network model for the determination of aquifer parameters. Journal of Hydrology 340(1): 1–11. https://doi.org/10.1016/j.jhydrol.2007.03.017
Scottish Development Department (1975) Towards cleaner water, Edinburgh: HMSO, Report of a River Pollution Survey of Scotland.
Solomatine DP, Ostfeld A (2008) Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics 10(1):3–22. https://doi.org/10.2166/hydro.2008.015
Tabari H, Kisi O, Ezani A, et al. (2012) SOM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology 444(445):78–89. https://doi.org/10.1016/j.jhydrol.2012.04.007
Tehrani EN, Sahour H, Booij MJ (2018) Trend analysis of hydro-climatic variables in the north of Iran. Theoretical and Applied Climatology 1–13. https://doi.org/10.1007/s00704-018-2470-0
Vapnik V (1998) Statistical Learning Theory, Wiley, New York.
Vapnik V (2000) The nature of statistical learning theory: Springer Verlag
Wang L (2005) Support Vector Machines: Theory and Applications. Springer-Verlag, New York.
Acknowledgments
We thank ABFAR (Mazandaran Rural Water and Sewer Company) for providing the groundwater quality secondary data and for helping us with data pre-processing.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gholami, V., Khaleghi, M.R. & Taghvaye Salimi, E. Groundwater quality modeling using self-organizing map (SOM) and geographic information system (GIS) on the Caspian southern coasts. J. Mt. Sci. 17, 1724–1734 (2020). https://doi.org/10.1007/s11629-019-5483-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-019-5483-y