Development of a Fuzzy Water Quality Index (FWQI) – Case study: Saveh Plain

آب و خاک

Article 4, Volume 29, Issue 5, November 2015, Pages 1117-11130   

Document Type: Research Article

DOI: 10.22067/jsw.v29i5.32505

Authors

S.M. Hosseini-Moghari; K. Ebrahimi^*

University of Tehran

Abstract

Introduction: Groundwater resources are the main source of fresh water in many parts of Iran. Groundwater resources are limited in quantity and recently due to increase of withdrawal, these resources are facing great stress. Considering groundwater resources scarcity, maintaining the quality of them are vital. Traditional methods to evaluate water quality insist on determining water quality parameter and comparison between them and available standards. The decisions in these methods rely on just specific parameters, in order to overcome this issue, water quality indices (WQIs) are developed. Water quality indexes include a range of water quality parameters and using mathematical operation represent an index to classify water quality. Applying the classic WQI will cause deterministic and inflexible classifications associated with uncertainties and inaccuracies in knowledge and data. To overcome this shortcoming, using the fuzzy logic in water resources problems under uncertainty is highly recommended. In this paper, two approaches are adopted to assess the water quality status of the groundwater resources of a case study. The first approach determined the classification of water samples, whilst the second one focused on uncertainty of classification analysis with the aid of fuzzy logic. In this regard, the paper emphasizes on possibility of water quality assessment by developing a fuzzy-based quality index even if required parameters are inadequate.
Materials and Methods: The case study is located in the northwest of Markazi province, Saveh Plain covers an area of 3245 km2 and lies between 34º45′-35º03′N latitude and 50º08′-50º50′E longitudes. The average height of the study area is 1108 meter above mean sea level. The average precipitation amount is 213 mm while the mean annual temperature is 18.2oC. To provide a composite influence from individual water quality parameters on total water quality, WQI is employed. In other words, WQI is a weighting average of multiple parameters. The present research used nine water quality parameters (Table 2). In this paper Fuzzy Water Quality Indices (FWQIs) have been developed, involving fuzzy inference system (FIS), based on Mamdani Implication. Firstly, five linguistic scales, namely: Excellent, Good, Poor, Very poor, and Uselessness were taken into account, and then, with respect to ‘If→then’ rules the FWQIs were developed. Later, the seven developed FIS-based indexes were compared with a deterministic water quality index. Indeed seven FWQIs based on different water quality available parameters have been developed. Then developed indices were used to evaluate the water quality of 17 wells of Saveh Plain, Iran.
Results and Discussion: The present study analysed groundwater quality status of 17 wells of Saveh Plain using FWQI and WQI. Based on the driven results from WQI and its developed fuzzy index, similar performance was observed in most of the cases. Both of them indicated that the water quality in six wells including NO.1, 2, 6, 12, 13, and 17 were suitable for drinking. Due to the fact that the values of both indexes were under 100, the mentioned wells could be considered as drinking water supplies. The indexes illustrated the very poor quality of wells NO.7, 9, 10, 11, 14, and 16. As a result, according to FWQI1 along with WQI, nearly 35% of wells have proper drinking water quality, while approximately 30% and 35% of them suffered from poor and very poor quality, respectively. The overall picture of water quality within the study area was not satisfying, hence, an accurate site selection for discovering water recourses with appropriate quality for drinking purpose must be responsible authorities’ priority. Analysis of FWQI2, FWQI3 and FWQI4 revealed that elimination of the parameters slightly changed the result of FWQI2; however, FWQI3 and FWQI4 did not vary considerably. Thus, Cl influenced the water quality slightly, but Ca and K did not affect the water quality of the plain. The results showed that inexistence of one of the mentioned parameters would not affect the computational process adversely. A glance at FWQI5, FWQI6 and FWQI7 revealed the improper performance of FWQI5 to show wells’ water quality status. Throughout the FWQI5 evaluation process, all the wells’ water quality stood in Excellent category. Due to the considerable values of TDS in the Plain, elimination of this parameter in FWQI5 caused inappropriate evaluation. Hence, whenever a case study deals with a high value of a specific quality parameter, elimination of that parameter would negatively demote validation of the analysis. Figures (3)-(6) represented the results of WQI along with seven FWQIs for 17 utilized wells’ water quality assessment in the study area during the proposed periods.
Conclusion: Throughout the present study, the capability of seven FIS-based indexing procedures in modelling the water quality analysis of 17 wells of Save Plain was discussed. The proposed FWQIs were developed on the basis of Mamdani approach by applying triangular and trapezoidal membership functions to determine the groundwater quality of the case study according to the nine parameters. The results revealed that FWQI1-4 outperformed others. On the other hand, FWQI5-7 which eliminated three out of the nine parameters, did not made a valid contribution to the computational context. This might be related to omitting the effective water quality parameters from the inputs of the model. The results also illustrated that, only six out of 17 wells of the region could be considered as suitable sources for the drinking purpose. The water quality status in five wells was not satisfying, and six wells were plagued by very poor quality of water.

Keywords

Groundwater; Mamdani Implication; fuzzy inference system; water quality

References

1- Alayon S., Robertson R., Warfield S.K., and Ruiz-Alzola J. 2007. A fuzzy system for helping medical diagnosis of malformations of cortical development. Journal of biomedical informatics, 40(3): 221-235.

2- Backman B., Bodiš D., Lahermo P., Rapant S., and Tarvainen T. 1998. Application of a groundwater contamination index in Finland and Slovakia. Environmental Geology, 36(1-2): 55-64.

3- Dahiya S., Singh B., Gaur S., Garg V.K., and Kushwaha H.S. 2007. Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials, 147(3): 938-946.

4- Gharibi H., Mahvi A.H., Nabizadeh R., Arabalibeik H., Yunesian M., and Sowlat, M.H. 2012. A novel approach in water quality assessment based on fuzzy logic. Journal of environmental management, 112: 87-95.

5- Ghomeshion, M., Malekian, A., Hoseini, K., Gharachelo, S., and Khamoushi, M.R. 2012. A survey on spatial variations of groundwater quality in Semnan/Sorkheh plain using geostatistical techniques. Iranian journal of Range and Desert Reseach, 19(3):545-535. (in Persian with English abstract(

6- Hashemi S.E., Mousavi S.F., Taheri S.M., and Ghareh-Chahi A. 2010. Analysis of Groundwater Quality Acceptability for Drinking purposes in Nine Cities in Isfahan Province Using Fuzzy Inference System. Iran-Water Resources Research, 6(18): 25-34. (in Persian with English abstract(

7- Hassani G., Mahvi A.H., Nasseri S., Arabalibeik H., Yunesian M., and Gharibi H. 2011. Designing Fuzzy-Based Ground Water Quality Index. Journal of health (Ardabil University of medical sciences), 3(1):18-31. (in Persian with English abstract(

8- Icaga Y. 2007. Fuzzy evaluation of water quality classification. Ecological Indicators, 7(3): 710-718.

9- Korepazan Dezfoli A. 2006. Fuzzy sets theory and its applications in modeling water engineering problems. Jahad Daneshgahi of Amirkabir University, Tehran. (in Persian(

10- Lermontov A., Yokoyama L., Lermontov M., and Machado M.A.S. 2009. River quality analysis using fuzzy water quality index: Ribeira do Iguape river watershed, Brazil. Ecological Indicators, 9(6): 1188-1197.

11- Lu X., Li L.Y., Lei K., Wang L., Zhai Y., and Zhai, M. 2010. Water quality assessment of Wei River, China using fuzzy synthetic evaluation. Environmental Earth Sciences, 60(8): 1693-1699.

12- Mamdani E.H. 1976. Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies, 8(6): 669-678.

13- Milovanovic M. 2007. Water quality assessment and determination of pollution sources along the Axios/Vardar River, Southeastern Europe. Desalination, 213(1): 159-173.

14- Mishra N., and Jha P. 2014. Fuzzy expert system for drinking water quality index. Recent Research in Science and Technology, 6(1): 122-125.

15- Mohammadi Ghaleni M., Ebrahimi K., and Araghinejad Sh. 2010. Groundwater Quantity and Quality Evaluation: A Case Study for Saveh and Arak Aquifers. Journal of Water and Soil Sciences, 21(2):93-108. (in Persian with English abstract(

16- Nakhei M., and Vadeei M. 2012. Fuzzy analysis of groundwater of Tehran province with drinking purpose. Journal of the Geological of Iran, 6(23): 37-46. (in Persian(

17- Nasseri M., Tajrishy M., reza Nikoo M., and Zaherpour J. 2013. Recognition and Spatial Mapping of Multivariate Groundwater Quality Index using Combined Fuzzy Method. Journal of Water and Wastewater, 24(85): 82-93. (in Persian with English abstract(

18- Ocampo-Duque W., Ferre-Huguet N., Domingo J.L., and Schuhmacher M. 2006. Assessing water quality in rivers with fuzzy inference systems: A case study. Environment International, 32(6): 733-742.

19- Ocampo-Duque W., Osorio C., Piamba C., Schuhmacher M., and Domingo J.L. 2013. Water quality analysis in rivers with non-parametric probability distributions and fuzzy inference systems: application to the Cauca River, Colombia. Environment international, 52: 17-28.

20- Rizwan R., and Gurdeep S. 2010. Assessment of Ground Water Quality Status by Using Water Quality Index Method in Orissa, India. World Applied Sciences Journal, 9(12): 1392-1397.

21- Saberi Nasr A., Rezaei M., and Dashti Barmaki M. 2013. Groundwater contamination analysis using Fuzzy Water Quality index (FWQI): Yazd province, Iran. Geopersia, 3(1): 47-55.

22- Saberi Nasr A., Rezaei M., Dashti Barmaki M., and Mansouri Majoumerd J. 2013. Evaluating Mamdani Fuzzy Inference System Usage in the Analysis of Groundwater Quality, Case Study: Tabas Aquifer. Iranian Journal of Water & Environment Engineering, 1(1): 25:34. (in Persian with English abstract(

23- Sadat-Noori S.M., Ebrahimi K., and Liaghat A.M. 2013. Groundwater quality assessment using the Water Quality Index and GIS in Saveh-Nobaran aquifer, Iran. Environmental Earth Sciences: 71(9): 3827-3843.

24- Saeedi M., Abessi O., Sharifi F., and Meraji H. 2010. Development of groundwater quality index. Environmental monitoring and assessment, 163(1-4): 327-335.

25- Sahebjalal E., Dehghany F., and Tabatabaeezade M.S. 2013. Investigating Spatio-Temporal Variations of Groundwater Quality Using Kriging Method. Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science, 17(65): 51-61. (in Persian with English abstract(

26- Scannapieco D., Naddeo V., Zarra T., and Belgiorno V. 2012. River water quality assessment: A comparison of binary-and fuzzy logic-based approaches. Ecological Engineering, 47: 132-140.

27- Sugeno M. 1985. Industrial applications of fuzzy control. Elsevier Science Inc.

28- Water Quality Data Report of Saveh Plain. 2011. Regional Water Organization of Arak. (in Persian(

29- WHO. 2004. Guidelines for drinking water quality: training pack. WHO, Geneva, Switzerland.

30- Yager R.R., and Filev D.P. 1994. Essentials of fuzzy modeling and control. John Wiley and Sons, New York.

31- Zadeh L.A. 1965. Fuzzy sets. Information and control, 8(3): 338-353.