News

Mokarram, M., Mokarram, M., Zarei, A., Safarinejadian, B. (2017). Using adaptive Neuro-Fuzzy network (ANFIS) to predict underground water quality in west of Fars province during 2003 to 2013 period. , 4(2), 547-559. doi: 10.22059/ije.2017.61491
Marzieh Mokarram; Mohammad Jaafar Mokarram; Abdol Rassoul Zarei; Behrouz Safarinejadian. "Using adaptive Neuro-Fuzzy network (ANFIS) to predict underground water quality in west of Fars province during 2003 to 2013 period". , 4, 2, 2017, 547-559. doi: 10.22059/ije.2017.61491
Mokarram, M., Mokarram, M., Zarei, A., Safarinejadian, B. (2017). 'Using adaptive Neuro-Fuzzy network (ANFIS) to predict underground water quality in west of Fars province during 2003 to 2013 period', , 4(2), pp. 547-559. doi: 10.22059/ije.2017.61491
Mokarram, M., Mokarram, M., Zarei, A., Safarinejadian, B. Using adaptive Neuro-Fuzzy network (ANFIS) to predict underground water quality in west of Fars province during 2003 to 2013 period. , 2017; 4(2): 547-559. doi: 10.22059/ije.2017.61491

Using adaptive Neuro-Fuzzy network (ANFIS) to predict underground water quality in west of Fars province during 2003 to 2013 period

مجله اکوهیدرولوژی
Article 21, Volume 4, Issue 2, June 2017, Pages 547-559    PDF (1.79 M)
Document Type: Research Article
DOI: 10.22059/ije.2017.61491
Authors
Marzieh Mokarram1; Mohammad Jaafar Mokarram2; Abdol Rassoul Zarei* 3; Behrouz Safarinejadian4
1Professor, Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, Iran
2PhD Student of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
3Professor, Department of Range and Watershed Management, College of Agriculture, University of Fasa, Iran
4Professor, Electrical and Electronics Engineering Department, Shiraz University of Technology, Shiraz, Iran
Abstract
Due to the reduced rainfall and overuse of underground water, checking the water quality is one of the most important challenges discussed in various areas, such as Iran. Estimation of water quality using models such as neural network results in costs reduction and better management. The current study aims is to assess ground water quality using adaptive fuzzy neural network (ANFIS) in the west of Fars province during 2003 to 2013 period. Three methods including grid partitioning, sub-clustering and FCM with two models of Hybrid and back propagation were used to predict the quality of ground water for the study area. In this study, electrical conductivity (EC) and sodium adsorption ratio (SAR) were used to train the neural network. In addition, water quality class diagram Wilcox was used to train the network. In chemical pollution, according to Wilcox diagram, EC and SAR are the most important factors based on which waters can be classified in different classes such as very appropriate, suitable and unsuitable for agriculture. Results show that among various models provided to predict groundwater quality, Hybrid models in FCM method have the greatest accuracy for the prediction of water quality in the study area with a maximum R (0.99) and minimum error.
Keywords
Water quality; adaptive Neuro -Fuzzy inference system (ANFIS); EC; SAR
References

 

  1. Ebn al-BalḵrG. Le Strange as Description of the Province of Fars.1912. London.
  2. Kurepazan A. The principles of fuzzy set theory and its applications. Publications Amir Kabir University Jihad. 2004.
  3. Minhaj MB. Fundamentals of artificial neural networks." Amirkabir University Press. 2005.
  4. Sowlat MH. A novel, fuzzy-based air quality index (FAQI) for air quality assessment. Atmospheric Environment. 2011: (45): 2050-2059.
  5. Lermontov A. River quality analysis using fuzzy water quality index: Ribeira do Iguape river watershed, Brazil. Ecological Indicators. 2009: (9): 1188-1197.
  6. Ocampo-Duque W. Assessing water quality in rivers with fuzzy inference systems: A case study. Environment International. 2006: (32): 733-742.
  7. Alireza V. Cropping Pattern Optimization by Using of TOPSIS and Genetic Algorithm Based on the Capabilities of GIS. Eco hydrology journal. 2016: 3(1): 69-82.
  8. Mirabbasi R, Mazloumzadeh SM, Rahnama MB. Evaluation of irrigation water using fuzzy logic. Research Journal of Environmental Sciences. 2008: (2): 340-353.
  9. Muhammetoglu A, Yardimci A. A Fuzzy Logic Approach to Assess Groundwater Pollution Levels below Agricultural Fields. Environmental Monitoring and Assessment. 2006: 118:337-354.
  10. Dahiya S. Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials. 2007: (147): 938-946.
  11. Sharifi-Rad J, Hoseini-Alfatemi M, Sharifi-Rad M, William NS. Chemical composition, antifungal and antibacterial Activities of essential oil from lallemantia Royleana (benth. In wall.) Benth. Journal of Food Safety. 2015: (35): 19–25.
  12. Nowshadi M, Salmyh R, Ahmadzada M. Simulation and forecasting of water quality parameters of Zayandehrood using artificial neural network. Journal of Water and Wastewater. 2009: 18(4): 49-65.
  13. Oliaei A, Banzhad H, Samadi MT, Rahmani A, Saghi MH. Evaluation of artificial neural network performance in predicting indicators of quality (BOD and DO) in Moradbeik River, Hamedan. Water and soil science (agricultural knowledge). 2012: 20(1): 199 - 210.
  14. Keshavarz A, Khashei Siooki A, Najafi MH. Locating appropriate water extraction using fuzzy hierarchical analysis (Case study: Birjand aquifer). Journal of Water and Wastewater. 2015: 142-135.
  15. Raii R, Fallahpour S. The use of support vector machine to predict corporate financial distress using financial ratio. Accounting and Auditing review. 2010: 15(53): 21-32.
  16. Karami B, Golabi M. Simulate and predict water quality parameters using artificial neural networks, fuzzy neural and statistical regression (Case study: Karun river, Khuzestan province)." The ninth international seminar river. Ahvaz martyr Chamran University. 2013.
  17. Darvari Z, Gholami V, Derakhshan S. Simulated groundwater salinity using artificial neural network (ANN) in coast of the Mazandaran province. Journal of Water and Irrigation. 2014: 36(2): 61-70.
  18. Alaii M, Zanguy M, Zanguy H. Prediction of water quality using neural networks MLP and adaptive fuzzy inference system (ANFIS) in Neyshabur plain. The National Conference of Environmental Science and Engineering. 2015. Ahvaz.
  19. Bisht DCS, Jangid A. Discharge modeling using adaptive neuro-fuzzy inference system. International Journal of advances science and technology. 2011: (31): 99–114.
  20. Mohammad NT, Seyed Reza H, Farshad A, Zahra NT. Evaluation the Accuracy of ANFIS, SVM and GP Models to Modeling the River Flow Discharge. Eco hydrology journal. 2016: 3(3): 347-361.
  21. Afshin H,  Mahdi P, Maryam Z, Moslem H. Land Use Optimization Using Combination of Fuzzy Linear Programming and Multi Objective Land Allocation Methods (Case Study: ChelgerdWatershed). Eco hydrology journal. 2016: 3(3): 363-377.
  22. Ghandi A, Asghari Moghadam A. Evaluation of effective parameters in underground water quality (Tasouj Plain), 9th conference of geology, Tarbiyat Moaalem University, Iran. 2005.
  23. Baharvand S, Ahmadi Khalaji A, Adib A, Uosefi Rad M. Role of different formation of geology in underground water quality (North of Khoram Abad), 3th conference of geology and environment, Eslamshahr, Azad university. 2006.
  24. Taalohi M, Tabatabaee H. Predicting bar dam water quality using neural-fuzzy inference system. Indian Journal of Fundamental and Applied Life Sciences. 2014: 2231– 6345.
  25. Najah AA, Shafie AEK, Othman AJO. Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation. Eural Computing and Applications. 2012: 21(5):1-9.  
  26. Alavi N, Nozari, V, Mazloumzadeh S, Nezamabadi-pour H. Irrigation water quality evaluation using adaptive network-based fuzzy inference system. Paddy & Water Environment. 2010: 8(3): 259-264.
 

Statistics
Article View: 4,732
PDF Download: 1,352


Journal Management System. Powered by Sinaweb