Abstract
Artificial intelligence methods have been employed with regard to 26 sets of physical and chemical pollution data obtained from the Melen River by the Turkish State Hydraulic Works during the period of 1995–2006. Water-quality data are divided into two parts relating to the high- and low-flow periods for the 1 KMP, 2 BMP, and 3 BMA stations. The self organizing map–artificial neural networks (SOM–ANNs) is employed to evaluate the high–low flow period correlations in terms of water-quality parameters. This is done in order to extract the most important parameters in assessing high–low flow period variations in terms of river water quality. The map size chosen is 9 × 9 in order to ensure that the maximum number of groups would be obtained from the training data. The groups explaining the pollution sources are identified as being responsible for the data structure at each dataset. The SOM, supported by ANN, is applied to provide a nonlinear relationship between input variables and output variables in order to determine the most significant parameters in each group. The multilayer feed-forward NN is chosen for this study. The most crucial parameters are determined, and the groups are conditionally named as mineral structure; soil structure and erosion; domestic, municipal, and industrial effluents; agricultural activity waste-disposal sites; and seasonal effects factors. Based on the explanation of the parameters, we can have an opinion about other parameters which can lead to cost and time savings. The aim of this study is to illustrate the usefulness of artificial intelligence for the evaluation of complex data in river- and water-quality assessment identification, and pollution sources, for effective water-quality management.
Similar content being viewed by others
References
An YJ, Breindenbach GP (2005) Monitoring E. coli and total coliforms in natural spring water as related to recreational mountain areas. Environ Monit Assess 102:131–137
APHA-AWWA-WPCF (1999) Standard methods for the examination of water and wastewater, 20th edn. American Public Health Association, Washington
Alhoniemi E, Hollmén J, Sımula O, Vesanto J (1999) Process monitoring and modeling using the self-organizing map. Integr Comput Aided Eng 6(1):3–14
Brion GM, Lingireddy S (2003) Artificial neural network modelling: a summary of successful applications relative to microbial water quality. Water Sci Technol 47(3):235–240
Cereghino R, Giraudel JL, Compin A (2001) Spatial analysis of stream invertebrates distribution in the Adour-garonne drainage basin (France), using kohonen self-organizing maps. Ecol Model 146:167–180
Chon TS, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an artificial neural network. Ecol Model 90:69–78
Dogan E, Sengorur B, Koklu R (2009) Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. J Environ Manag 90(2):1229–1235
Duzce Governorship Directorate of Environment and Forestry (2007) Duzce Environment State Report 2007, Düzce-2005–2007
Gamble A, Babbar-Sebens M (2012) On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River Basin, Indiana, USA. Environ Monit Assess 184:845–875
Giraudel JL, Lek S (2001) A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol Model 146:329–339
Godwin KS, Hafner SD, Buff MF (2003) Long Term trends in sodium and chloride in The Mohawk River, New York: the effect of fifty years of road-salt application. Environ Pollut 124:273–281
Haykin S (1999) Neural networks a comprehensive foundation, second edition, Prentice-Hall, ISBN 0-13-273350-1
Hussain I, Shakeel M, Faisal M, Soomro ZA, Hussain M, Hussain T (2014a) Distribution of total dissolved solids in drinking water by means of bayesian kriging and gaussian spatial predictive process. Water Qual Expo Health 6:177–185
Hussain I, Mubarak N, Shabbir J, Hussain T, Faisal T (2014b) Spatial interpolation of sulfate concentration in groundwater including covariates using bayesian hierarchical models. Water Qual Expo Health. doi:10.1007/s12403-014-0154-2
Jin Y-H, Kawamura A, Park S-C, Nakagawa N, Amaguchi H, Olsson J (2011) Spatiotemporal classification of environmental monitoring data in the Yeongsan River basin, Korea, using self-organizing maps. J Environ Monit 13:2886–2894
Justin MZ, Zupančic M (2007) Boron in irrigation water and its interactions with soil and plants: an example of municipal landfill leachate reuse. Acta Agric Slov 8:1
Kalteh AM, Hjorth P, Berndtsson R (2008) Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environ Model Softw 23:835–845
Kambe J, Aoyama T, Yamauchi A, Nagashima U (2007) Extraction of a parameter as an index to assesswater quality of the Tamagawa, Tokyo, Japan, by using neural networks and multivariate analysis. J Comput Chem Jpn 6(1):19–26
Kohonen T (2001) Self organization maps. Springer, Berlin
Kohonen T (2013) Essentials of the self-organizing map. Neural Netw 37:52–65
Lucho-Constantino CA, Prieto-Garcı F, Delrazo LM, Rodríguez-Vázquez R, Poggi-Varaldo HM (2005) Chemical fractionation of boron and heavy metals in soils irrigated with wastewater in central Mexico. Agric Ecosyst Environ 108:57–71
Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32(4):1013–1022
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124
Maillard P, Santos NAP (2008) A spatial-statistical approach for modeling the effect of non-point source pollution on different water quality parameters in The Velhas River watershed-Brazil. J Environ Manag 86:158–170
Méndeza MC, Manteiga WG, Bande MF, Sánchez JMP, Calderón RL (2004) Modelling of the monthly and daily behaviour of the runoff of The Xallas River using box-jenkins and neural networks methods. J Hydrol 296:38–58
Şengörür B, Dogan E, Koklu R, Samandar A (2006) Dissolved oxygen estimation using artificial neural network for water quality control fresenius. Environ Bull 15–9:1064–1067
Smart R, White CC, Townend J, Cresser MS (2001) A model for predicting chloride concentrations in river water in a relatively unpolluted catchment in North-East Scotland. Sci Total Environ 265:131–141
Suen JP, Eheart JW, Asce M (2003) Evaluation of neural networks for modelling nitrate concentration in rivers. J Water Res Plan Manag 129:505–510
Tran LT, Knight CG, O’neill RV, Smith ER, O’connell M (2003) Self-organizing maps for integrated environmental assessment of the mid-Atlantic region. Environ Manag 31(6):822–835
Zaher I, Bai CG (2003) Application of artificial neural network for water quality management. Lowl Technol Int 5(2):10–15
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sengorur, B., Koklu, R. & Ates, A. Water Quality Assessment Using Artificial Intelligence Techniques: SOM and ANN—A Case Study of Melen River Turkey. Water Qual Expo Health 7, 469–490 (2015). https://doi.org/10.1007/s12403-015-0163-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12403-015-0163-9