Volume 12 Issue 1
Mar.  2019
Turn off MathJax
Article Contents
Article Navigation >  Water Science and Engineering  > 2019  >  12(1): 45-54
Armin Azad, Hojat Karami, Saeed Farzin, Sayed-Farhad Mousavi, Ozgur Kisi. 2019: Modeling river water quality parameters using modified adaptive neuro fuzzy inference system. Water Science and Engineering, 12(1): 45-54. doi: 10.1016/j.wse.2018.11.001
Citation: Armin Azad, Hojat Karami, Saeed Farzin, Sayed-Farhad Mousavi, Ozgur Kisi. 2019: Modeling river water quality parameters using modified adaptive neuro fuzzy inference system. Water Science and Engineering, 12(1): 45-54. doi: 10.1016/j.wse.2018.11.001
shu

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

doi: 10.1016/j.wse.2018.11.001

  • a Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran

  • b Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi 0162, Georgia

More Information
  • Corresponding author: Hojat Karami
  • Received Date: 2018-05-18
  • Rev Recd Date: 2019-01-03
    Abstract
  • Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling
    water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the
    adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more
    suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being
    trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO)
    and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood
    River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and
    gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved
    solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using
    SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with
    ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.

     

    • FullText(HTML)
  • loading
    • References(31)
  • Adriaenssens, V., Baets, B.D., Goethals, P.L.M., Pauw, N.D., 2004. Fuzzy rule-based models for decision support in ecosystem management. Science of the Total Environment, 319(1-3), 1-12. https://doi.org/10.1016/S0048-9697(03)00433-9.
    Ay, M., Kisi, Ö., 2017. Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques. KSCE Journal of Civil Engineering, 21(5), 1631-1639. https://doi.org/10.1007/s12205-016-0728-6.
    Azad, A., Karami, H., Farzin, S., Saeedian, A., Kashi, H., Sayyahi, F., 2018a. Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (Case study: Gorganrood River). KSCE Journal of Civil Engineering, 22(7), 2206-2213. https://doi.org/10.1007/s12205-017-1703-6.
    Azad, A., Mousavi, S.F., Karami, H., Farzin, S., Singh, V.P. 2018b. The effect of vermiculite and quartz in porous concrete on reducing storm-runoff pollution. ISH Journal of Hydraulic Engineering. https://doi.org/10.1080/09715010.2018.1528482.
    Azad, A., Manoochehri, M., Kashi, H., Farzin, S., Karami, H., Nourani, V., Shiri, J., 2019. Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling. Journal of Hydrology, 571, 214-221. https://doi.org/10.1016/j.jhydrol.2019.01.062.
    Barzegar, R., Moghaddam, A.A., Adamowski, J., Ozga-Zielinski, B., 2017. Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model. Stochastic Environmental Research and Risk Assessment, 32(3), 799-813. https://doi.org/10.1007/s00477-017-1394-z.
    Dogan, E., Sengorur, B., Koklu, R., 2009. Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. Journal of Environmental Management, 90(2), 1229-1235. https://doi.org/10.1016/j.jenvman.2008.06.004.
    Dorigo, M. 1992. Optimization, Learning and Natural Algorithms. Ph. D. Dissertation. Dipartimento di Elettronica, Politecnico di Milano (in Italian).
    Eberhart, R., Kennedy, J., 1995. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE, Nagoya, pp. 39-43. https://doi.org/10.1109/MHS.1995.494215.
    Emamgholizadeh, S., Kashi, H., Marofpoor, I., Zalaghi, E., 2014. Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. International Journal of Environmental Science and Technology, 11(3), 645-656. https://doi.org/10.1007/s13762-013-0378-x.
    He, J.P., Jiang, Z.X., Zhao, C., Peng, Z.Q., Shi, Y.Q., 2018. Cloud-Verhulst hybrid prediction model for dam deformation under uncertain conditions. Water Science and Engineering, 11(1), 61-67. https://doi.org/10.1016/j.wse.2018.03.002.
    Heidarzadeh, N., 2017. A practical low-cost model for prediction of the groundwater quality using artificial neural networks. Journal of Water Supply: Research and Technology-Aqua, 66(2), 86-95. https://doi.org/10.2166/aqua.2017.035.
    Jalalkamali, A., 2015. Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters. Earth Science Informatics, 8(4), 885-894. https://doi.org/10.1007/s12145-015-0222-6.
    Jang, J.S., 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541.
    Kisi, O., Keshavarzi, A., Shiri, J., Zounemat-Kermani, M., Omran, E.S.E., 2017a. Groundwater quality modeling using neuro-particle swarm optimization and neuro-differential evolution techniques. Hydrology Research, 48(6), 1508-1519. https://doi.org/10.2166/nh.2017.206.
    Kisi, O., Alizamir, M., Zounemat-Kermani, M., 2017b. Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Natural Hazards, 87(1), 367-381. https://doi.org/10.1007/s11069-017-2767-9.
    Kisi, O., Azad, A., Kashi, H., Saeedian, A., Hashemi, S.A.A., Ghorbani, S., 2019. Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods. Water Resources Management, 33(2), 847-861. https://doi.org/10.1007/s11269-018-2147-6.
    May, D. B., Sivakumar, M., 2009. Prediction of urban stormwater quality using artificial neural networks. Environmental Modelling and Software, 24(2), 296-302. https://doi.org/10.1016/j.envsoft.2008.07.004.
    Mirrashid, M., 2014. Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Natural Hazards, 74(3), 1577-1593. https://doi.org/10.1007/s11069-014-1264-7.
    Nadaf Fahmideh, S., Allahyari, M.S., Damalas, C.A., Masouleh, Z.D., Ghazi, M., 2017. Predicting adoption of double cropping in paddy fields of northern Iran: A comparison of statistical methods. Paddy and Water Environment, 15(4), 907-917. https://doi.org/10.1007/s10333-017-0601-3.
    Nguyen, V., Li, Q., Nguyen, L., 2017. Drought forecasting using ANFIS-a case study in drought prone area of Vietnam. Paddy and Water Environment, 15(3), 605-616. https://doi.org/10.1007/s10333-017-0579-x.
    Nie, S.Y., Bian, J.M., Wan, H.L., Sun, X.Q., Zhang, B.J., 2017. Simulation and uncertainty analysis for groundwater levels using radial basis function neural network and support vector machine models. Journal of Water Supply: Research and Technology-Aqua, 66(1), 15-24. https://doi.org/10.2166/aqua.2016.069.
    Olyaie, E., Banejad, H., Chau, K.W., Melesse, A.M., 2015. A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: A case study in United States. Environmental Monitoring and Assessment, 187(4), 189. https://doi.org/10.1007/s10661-015-4381-1.
    Orzepowski, W., Paruch, A. M., Kowalczyk, T., Pok?adek, R., Pulikowski, K., 2017. Modelling of water reserves in mineral soils with different retention properties. Water and Environment Journal, 31(3), 388-400. https://doi.org/10.1111/wej.12255.
    Rezaei, F., Safavi, H.R., Ahmadi, A., 2013. Groundwater vulnerability assessment using fuzzy logic: A case study in the Zayandehrood aquifers. Environmental Management, 51(1), 267-277. https://doi.org/10.1007/s00267-012-9960-0.
    Socha, K., Dorigo, M., 2008. Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173. https://doi.org/10.1016/j.ejor.2006.06.046.
    Tabari, M.M.R., 2016. Prediction of river runoff using fuzzy theory and direct search optimization algorithm coupled model. Arabian Journal for Science and Engineering, 41(10), 4039-4051. https://doi.org/10.1007/s13369-016-2081-y.
    Taormina, R., Chau, K.W., 2015. Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and extreme learning machines. Journal of Hydrology, 529, 1617-1632. https://doi.org/10.1016/j.jhydrol.2015.08.022.
    Wang, W.C., Xu, D.M., Chau, K.W., Lei, G.J., 2014. Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resources Management, 28(12), 4183-4200. https://doi.org/10.1007/s11269-014-0738-4.
    Wu, C.L., Chau, K.W., 2011. Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology, 399(3-4), 394-409. https://doi.org/10.1016/j.jhydrol.2011.01.017.
    Zadeh, L.A., 1965. Information and control. Fuzzy Sets, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (589) PDF downloads(538) Cited by()
    Proportional views
    Related

    Copyright © 2009 Editorial office of Water Science and Engineering 苏ICP备12023610号-1

    Supported by: Beijing Renhe Information Technology Co. Ltd   support: info@rhhz.net

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return