Modeling groundwater quality using three novel hybrid support vector regression models

Document Type : Research Paper

Authors

1 Department of Water Engineering, University of Tabriz, Tabriz, Iran.

2 Department of Computer Engineering, University of Bonab, Bonab, Iran.

3 Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

Abstract

During recent decades, the excessive use of water has led to the scarcity of the available surface and groundwater resources. Quantitative and qualitative surveys of groundwater resources indicate that accurate and efficient optimization methods can help to overcome the numerous challenges in assessment of groundwater quality. For this purpose, three optimization meta-heuristic algorithms, including imperialist competitive (ICA), election (EA), and grey wolf (GWO), as well as the support vector regression method (SVR), were used to simulate the groundwater quality of the Salmas Plain. To achieve this goal, the data of the groundwater quality for the Salmas plain were utilized in a statistical period of 10 years (2002-2011). The results were evaluated according to Wilcox, Schuler, and Piper standards. The results indicated higher accuracy of the GWO-SVR method compared to the other two methods with values of R2=0.981, RMSE=0.020 and NSE=0.975. In general, a comparison of the results obtained from the hybrid methods and different diagrams showed that the samples had low hardness and corrosion. Also, the results indicated the high capability and accuracy of the GWO-SVR method in estimating and simulating the groundwater quality.

Keywords

Main Subjects

References

[1] Mirsanjouri, M. M., Mohammadyari, F., Basiri, R., Hamidipour, F. (2015). Modeling the EC, SAr and TDS in groundwater using artificial neural network (case study: Mehran and Dehloran Plain). Human and environmental quarterly journal, 42, 1-12.
[2] Khudair, B. H., Jasim, M.M., Alsaqqar, A.S. (2018). Artificial neural network model for the prediction of groundwater quality. Civil engineering journal, 4(12), 2959-2970.
[3] Jalalkamali, A. (2015). Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters. Earth science informatics, 8(4), 885-894.
[4] Mokarram, M. (2016). Modeling of multiple regression and multiple linear regressions for prediction of groundwater quality (case study: north of Shiraz). Modeling earth systems and environment, 2(1), 3.
[5] Adhikary, P. P., Dash, C. J., Chandrasekharan, H., Rajput, T. B. S., Dubey, S. K. (2012). Evaluation of groundwater quality for irrigation and drinking using GIS and geostatistics in a peri-urban area of Delhi, India. Arabian journal of geosciences, 5(6), 1423-1434.
[6] Adiat, K. A. N., Nawawi, M. N. M., Abdullah, K. (2013). Application of multi-criteria decision analysis to geoelectric and geologic parameters for spatial prediction of groundwater resources potential and aquifer evaluation. Pure and applied geophysics, 170(3), 453-471.
[7] Arabgol, R., Sartaj, M., Asghari, K. (2016). Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model. Environmental modeling and assessment, 21(1), 71-82.
[8] Khan, R., Jhariya, D. C. (2017). Groundwater quality assessment for drinking purpose in Raipur City, Chhattisgarh using water quality index and geographic information system. Journal of the geological society of India, 90(1), 69-76.
[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.
[10] Wagh, V. M., Panaskar, D. B., Muley, A. A., Mukate, S. V., Lolage, Y. P., Aamalawar, M. L. (2016). Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India. Modeling earth systems and environment, 2(4), 1-10.
[11] Kisi, O., Keshavarzi, A., Shiri, J., Zounemat-Kermani, M., Omran, E. S. E. (2017). Groundwater quality modeling using neuro-particle swarm optimization and neuro-differential evolution techniques. Hydrology research, 48(6), 1508-1519
[12] 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.
[13] Al-Rekabi, H., Al-Ghanimy, D. B. G. A. (2016). Determine the validity of the Euphrates River (Middle Euphrates) for drinking purpose using a water quality index (CCME WQI). Mesopotamia environmental journal, 2(1), 1-11.
[14] Pramada, S. K., Mohan, S., Sreejith, P. K. (2018). Application of genetic algorithm for the groundwater management of a coastal aquifer. ISH journal of hydraulic engineering, 24(2), 124-130.
[15] Gaikwad, S., Gaikwad, S., Meshram, D., Wagh, V., Kandekar, A., Kadam, A. (2020). Geochemical mobility of ions in groundwater from the tropical western coast of Maharashtra, India: implication to groundwater quality. Environment, development and sustainability, 22(3), 2591-2624.
[16] Shyamala, G., Ramesh, S., Saravanakumar, N. (2020). Major ion chemistry and groundwater quality evaluation for irrigation. Journal of applied sciences and environmental management, 24(4), 699-705.
[17] Zareh-Abianeh, H., Bayat-Vorkeshi, M., Akhavan, S., Mohammadi, M. (2011). Estimation of groundwater nitrate in Hamedan-Bahar plain using artificial neural network and data separation effect on prediction precision. Ecology, 37(58), 129-140. 
[18] Rafati, L., Mokhtari, M., Fazelinia, F., Momtaz, S. M., Mahvi, A. H. (2013). Evaluation of ground water fluoride concentration in Hamadan Province west of IRAN. Iranian journal of health sciences, 1(3), 71-76.
[19] Moasheri, S. A., Rezapour, O. M., Beyranvand, Z., Poornoori, Z. (2013). Estimating the spatial distribution of groundwater quality parameters of Kashan plain with integration method of geostatistics - artificial neural network optimized by genetic-algorithm. International journal of agriculture and crop science, 5(20), 2434-2442.
[20] Emami, S., Arvanaghi, A., Hemmati, M. (2017). Evaluation and comparison imperialist competitive and genetic algorithms in estimation of groundwater quality parameters. Journal of hydrogeology, 2(2), 44-53.
[21] Jalalkamali, A., Jalalkamali, N. (2018). Adaptive network-based fuzzy inference system-genetic algorithm models for prediction groundwater quality indices: a GIS-based analysis. Journal of artificial intelligence and data mining, 6(2), 439-445.
[22] Asefi, M., Zamani-Ahmadmahmoodi, R. (2018). Analysis of physiochemical and microbial quality of waters of the Karkheh River in southwestern Iran using multivariate statistical methods. Advances in environmental technology, 4(2), 75-81.
[23] Bhat, B., Parveen, S., Hassan, T. (2018). Seasonal assessment of physicochemical parameters and evaluation of water quality of river Yamuna, India. Advances in environmental technology, 4(1), 41-49.
[24] Movagharnejad, K., Tahavvori, A., Moghaddam Ali, F. (2017). Artificial neural network modeling for predicting of some ion concentrations in the Karaj River. Advances in environmental technology, 3(2), 109-117.
[25] Jafari, R., Torabia,n A., Ghorbani, M. A., Mirbagheri, S. A., Hassani, A. H. (2019). Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods. Journal of water supply, 68(7), 573-584.
[26] Maroufpoor, S., Jalali, M., Nikmehr, S., Shiri, N., Shiri, J., Maroufpoor, E. (2020). Modeling groundwater quality by using hybrid intelligent and geostatistical methods. Environmental science and pollution research international, 27, 28183-28197.
[27] Banadkooki, F. B., Ehteram, M., Ahmed, A. N., Teo, F. Y., Fai, C. M., Afan, H. A., El-Shafie, A. (2020). Enhancement of groundwater-level prediction using an integrated machine learning model optimized by whale algorithm. Natural resources research, 29(5), 3233-3252.
[28] Hosseinzadeh Arabloyeyekan, E., Charbghoo, T. (2013). Hydro-geochemical study of Salmas plain groundwater resources, 17th Iranian geological society, 7-9 November.
[29] Mirjalili, S., Mirjalili, S. M., Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
[30] Emami, H., Derakhshan, F. (2015). Election algorithm: A new socio-politically inspired strategy. AI Communications,28(3), 591-603.
[31] Atashpaz-Gargari, E., Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee.
[32] Drucker, H., Surges, C. J. C., Kaufman, L., Smola, A., Vapnik, V. (2013). Support vector regression machines. Advances in neural information processing systems, 155–161, 1997.
[33] Gorbani, M. A., Shahabboddin, Sh., Zare Haghi, D., Azani, A., Bonakdari, H., Ebtehaj, I. (2017). Application of firefly algorithm-based support vector machines for prediction of filed capacity and permanent wilting point. Soil and tllage research, 172, 32-38.
[34] Isazadeh, M., Arabzadeh, R., Darbandi, S. (2016). Performance evaluation of geostatistical methods and artificial neural network in estimation of aquifer quality parameters (Case Study: Qorveh Dehghan Plain). JWSS-Isfahan university of technology, 20(77), 197-210.
[35] Mirzavand, M., Ghasemiyeh, H., Sadatinejad, S.J., Akbari, M. (2015). Simulation of changes in groundwater quality using artificial neural network (case study: Kashan aquifer). Journal of iranian natural resource, 68(1), 159-171.
[36] Nourani, V., Alami, M.T., Vousoughi, F.D. (2016). Self-organizing map clustering technique for ANN-based spatiotemporal modeling of groundwater quality parameters. Journal of hydro informatics, 18(2), 288-309.
 
 
Advances in Environmental Technology
Volume 6, Issue 2
April 2020
Pages 99-110

Files

History

  • Receive Date: 03 July 2020
  • Revise Date: 17 February 2021
  • Accept Date: 21 February 2021

Share

How to cite

Statistics