Irrigation Sciences and Engineering

Irrigation Sciences and Engineering

Estimation of sodium absorption ratio (SAR) and electrical conductivity (EC) using hybrid models of support vector regression and random forest-case study of Qatur river, west of Iran

Document Type : Research Paper

Authors
Milad Sharafi 1
Javad Behmanesh 2
Vahid Rezaverdinejad 2
Saeed Samadianfard 3
1 Ph.D. Student, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran.
2 Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran.
3 Associate Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
10.22055/jise.2024.47590.2131
Abstract
In the field of irrigation, water quality has an effective role in determining the compatibility of water with soil and different crops. As a valuable tool for water quality assessment, the Wilcox diagram enables stakeholders to promote sustainable agricultural practices by reducing potential risks related to water salinity and sodium content. However, measuring sodium absorption ratio (SAR) and electrical conductivity (EC) parameters is associated with various challenges such as cost and time, calibration error, and maintenance problems. So, this research was carried out to predict SAR and EC parameters using easily accessible parameters including sulfate and chloride in Qatur river located in the northwest of Iran from 1993 to 2019. The models used included random forest (RF) and support vector regression (SVR) which have been optimized by genetic algorithm (GA), fruit fly algorithm and Golden ball optimization (GBO). The analysis of the results of this research showed that the RF-GA model had the least error in the Qatur river station with root mean square error of 0.809 meq.lit-1 and 0.146 dS.m-‌1 for SAR and EC parameters, respectively. Also, The SVR-GBO model with the root mean square error of 0.809 meq.lit-1 and 0.146 dS.m-1 and SVR-FOA with the root mean square error of 0.998 meq.lit-1 and 0.169 dS.m-1 for SAR and EC parameters, respectively had suitable performance. Finally, the evaluation of the utilized parameters indicated that chloride plays an important role in predicting SAR and EC parameters, and if sulfate is not measured, it can be used as a promising alternative.
Keywords
Irrigation water quality
Sodium absorption ratio
Machine learning
Data mining
Wilcox diagram
Hydrology
Subjects
  • determining the cause of its changes along the way. In the 8th National Environmental Health Conference, University of Tehran, Tehran, Iran.

 

  • Ahmadianfar, I., Jamei, M. and Chu, X., 2020. A novel hybrid wavelet-locally weighted linear regression (W-LWLR) model for electrical conductivity (EC) prediction in surface water. Journal of Contaminant Hydrology, 232(1), pp.103-121. https://doi.org/10.1016/j.jconhyd.2020.103641.

 

  • Albadr, M.A., Tiun, S., Ayob, M. and Al-Dhief, F., 2020. Genetic algorithm based on natural selection theory for optimization problems. Symmetry, 12(11), pp.1758-1771. https://doi.org/10.3390/sym12111758.

 

  • Alkharabsheh, H.M., Seleiman, M.F., Hewedy, O.A., Battaglia, M.L., Jalal, R.S., Alhammad, B.A., Schillaci, C., Ali, N. and Al-Doss, A., 2021. Field crop responses and management strategies to mitigate soil salinity in modern agriculture: A review. Agronomy, 11(11), pp.2299-2317. https://doi.org/10.3390/agronomy11112299.

 

  • Altman, N. and Krzywinski, M., 2017. Ensemble methods: bagging and random forests. Nature Methods, 14(10), pp.933-935.

 

  • Amezketa, E. and de Lersundi, J.D.V., 2008. Soil classification and salinity mapping for determining restoration potential of cropped riparian areas. Land Degradation & Development, 19(2), pp.153-164. https://doi.org/10.1002/ldr.820.

 

 

 

  • Cavins, T.J., Gibson, J.L., Whipker, B.E. and Fonteno, W.C., 2000. pH and EC meters-tool for substrate analysis. North Carolina State University, 1(1), pp.1-14.

 

  • Costa-Carrapiço, I., Raslan, R. and González, J.N., 2020. A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency. Energy and Buildings, 210(12), pp.109-130. https://doi.org/10.1016/j.enbuild.2019.109690.

 

  • El Bilali, A. and Taleb, A., 2020. Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment. Journal of the Saudi Society of Agricultural Sciences, 19(7), 439-451. https://doi.org/10.1016/j.jssas.2020.08.001.

 

  • Giri, S., 2021. Water quality prospective in Twenty First Century: Status of water quality in major river basins, contemporary strategies and impediments: A review. Environmental Pollution, 271(4), 116-132. https://doi.org/10.1016/j.envpol.2020.116332.

 

  • Haghiabi, A.H., Nasrolahi, A.H. and Parsaie, A., 2018. Water quality prediction using machine learning methods. Water Quality Research Journal, 53(1), pp.3-13. https://doi.org/10.2166/wqrj.2018.025.

 

  • Hamdi, T., Ali, J.B., Di Costanzo, V., Fnaiech, F., Moreau, E. and Ginoux, J.-M., 2018. Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm. Biocybernetics and Biomedical Engineering, 38(2), 362-372. https://doi.org/10.1016/j.bbe.2018.02.005.

 

  • Hassan, M., Hassan, M., Akter, L., Rahman, M., Zaman, S., Khan, H., Jahan, N., Smrity, R.N., Farhana, J. and Raihan, M., 2021. Efficient prediction of water quality index (WQI) using machine learning algorithms. Human-Centric Intelligent Systems, 1(3), pp.86-97. https://doi.org/10.2991/hcis.k.211203.001.

 

  • Hassanat, A., Almohammadi, K., Alkafaween, E.a., Abunawas, E., Hammouri, A. and Prasath, V.S., 2019. Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach. Information, 10(12), pp.390-402. https://doi.org/10.3390/info10120390.

 

17- Hüe, X., 1997. Genetic algorithms for optimization. Rapport technique, Citeseer.

 

18- Jamei, M., Ali, M., Karimi, B., Karbasi, M., Farooque, A.A. and Yaseen, Z.M., 2023. Surface water electrical conductivity and bicarbonate ion determination using a smart hybridization of optimal Boruta package with Elman recurrent neural network. Process Safety and Environmental Protection, 174(5), pp.115-134. https://doi.org/10.1016/j.psep.2023.03.062.

 

19- Janitza, S. and Hornung, R., 2018. On the overestimation of random forest’s out-of-bag error. PloS one, 13(8), pp.201-217. https://doi.org/10.1371/journal.pone.0201904.s001.

 

20- Karal, O., 2017. Maximum likelihood optimal and robust support vector regression with lncosh loss function. Neural networks, 94(3), pp.1-12. https://doi.org/10.1016/j.neunet.2017.06.008.

 

21- Kisi, O., Azad, A., Kashi, H., Saeedian, A., Hashemi, S.A.A. and Ghorbani, S., 2019. Modeling groundwater quality parameters using hybrid neuro-fuzzy methods. Water Resources Management, 33(1), pp.847-861. https://doi.org/10.1007/s11269-018-2147-6.

 

22- Koohestani, B., 2020. A crossover operator for improving the efficiency of permutation-based genetic algorithms. Expert Systems with Applications, 151(4), pp.113-130. https://doi.org/10.1016/j.eswa.2020.113381.

 

23- Liu, S., Tai, H., Ding, Q., Li, D., Xu, L. and Wei, Y., 2013. A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Mathematical and Computer Modelling, 58(3-4), pp.458-465. https://doi.org/10.1016/j.mcm.2011.11.021.

 

24- Machado, R.M.A. and Serralheiro, R.P., 2017. Soil salinity: effect on vegetable crop growth. Management practices to prevent and mitigate soil salinization. Horticulturae, 3(2), pp.30-42. https://doi.org/10.3390/horticulturae3020030.

 

25- Manikandan, M. and Pant, R.S., 2021. Research and advancements in hybrid airships—A review. Progress in Aerospace Sciences, 127(17), pp.100-118. https://doi.org/10.1016/j.paerosci.2021.100741.

 

26- McKinney, D.C. and Lin, M.D., 1994. Genetic algorithm solution of groundwater management models. Water Resources Research, 30(6), pp.1897-1906. https://doi.org/10.1029/94WR00554.

 

27- Mokhtar, A., Elbeltagi, A., Gyasi-Agyei, Y., Al-Ansari, N. and Abdel-Fattah, M.K., 2022. Prediction of irrigation water quality indices based on machine learning and regression models. Applied Water Science, 12(4), pp.76-91. https://doi.org/10.1007/s13201-022-01590-x.

 

28- Mousazadeh, M., Paital, B., Naghdali, Z., Mortezania, Z., Hashemi, M., Karamati Niaragh, E., Aghababaei, M., Ghorbankhani, M., Lichtfouse, E. and Sillanpää, M., 2021. Positive environmental effects of the coronavirus 2020 episode: a review. Environment, Development and Sustainability, 23(2), pp.12738-12760. https://doi.org/10.1007/s10668-021-01240-3.

 

29- Nasehi, F., Hassani, A., Monavvari, M., Karbassi, A. and Khorasani, N., 2013. Evaluating the metallic pollution of riverine water and sediments: a case study of Aras River. Environmental Monitoring and Assessment, 185(4), pp.197-203. https://doi.org/10.1007/s10661-012-2543-y.

 

30- Nouraki, A., Alavi, M., Golabi, M. and Albaji, M., 2021. Prediction of water quality parameters using machine learning models: A case study of the Karun River, Iran. Environmental Science and Pollution Research, 28(40), pp.57060-57072. https://doi.org/10.1007/s11356-021-14560-8.

 

31- Osaba, E., Diaz, F. and Onieva, E., 2014. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied Intelligence, 41(1), pp.145-166. https://doi.org/10.1007/s10489-013-0512-y.

 

32- Palmer, M. and Ruhi, A., 2019. Linkages between flow regime, biota, and ecosystem processes: Implications for river restoration. Science, 365(64), pp.2087-2099. https://doi.org/10.1126/science.aaw2087.

 

33- Pan, W.-T., 2012. A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26(1), pp.69-74. https://doi.org/10.1016/j.knosys.2011.07.001.

 

34- Pincay, J.M.C. and Cantos, M.F.P., 2024. Analysis of Soil Salinization as an Environmental Issue in Latin America. Journal of Ecological Engineering, 25(1), pp.146-152. http://dx.doi.org/10.12911/22998993/174378.

 

35- Qadir, M., Wichelns, D., Raschid-Sally, L., Minhas, P.S., Drechsel, P., Bahri, A., McCornick, P.G., Abaidoo, R.C., Attia, F. and El-Guindy, S., 2007. Agricultural use of marginal-quality Water: Opportunities and Challenges. IWMI, 12(1), pp.1-34.

 

36- Qishlaqi, A., Kordian, S. and Parsaie, A., 2017. Hydrochemical evaluation of river water quality—a case study. Applied Water Science, 7, pp.2337-2342. https://doi.org/10.1007/s13201-016-0409-0.

 

37- Rad, M.A.V. and Kasaeian, A., 2024. Evaluation of stand-alone hybrid renewable energy system with excess electricity minimizer predictive dispatch strategy. Energy Conversion and Management, 299(18), pp.117-131. https://doi.org/10.1016/j.enconman.2023.117898.

 

38- Rahimi, L., Amanipoor, H. and Battaleb-Looie, S., 2021. Effect of salinity of irrigation water on soil properties (abadan plain, SW Iran). Geocarto International, 36(16), pp.1884-1903. https://doi.org/10.1080/10106049.2019.1678678.

 

39- Rahnama, E., Bazrafshan, O. and Asadollahfardi, G., 2020. Application of data-driven methods to predict the sodium adsorption rate (SAR) in different climates in Iran. Arabian Journal of Geosciences, 13(1), pp.1-19. https://doi.org/10.1007/s12517-020-06146-4.

 

40- Schmitt, L.M., 2001. Theory of genetic algorithms. Theoretical Computer Science, 259(1), pp.1-61. https://doi.org/10.1016/S0304-3975(00)00406-0.

 

41- Sepahvand, A., Singh, B., Sihag, P., Nazari Samani, A., Ahmadi, H. and Fiz Nia, S., 2021. Assessment of the various soft computing techniques to predict sodium absorption ratio (SAR). ISH Journal of Hydraulic Engineering, 27(1), pp.124-13. https://doi.org/10.1080/09715010.2019.1595185.

 

42- Shao, Z., Pi, D. and Shao, W., 2020. Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment. Expert Systems with Applications, 145(17), pp.113-147. https://doi.org/10.1016/j.eswa.2019.113147.

 

43- Sposito, G. and Mattigod, S.V., 1977. On the chemical foundation of the sodium adsorption ratio. Soil Science Society of America Journal, 41(2), pp.323-329. https://doi.org/10.2136/sssaj1977.03615995004100020030x.

 

44- Su, X., He, X., Zhang, G., Chen, Y. and Li, K., 2022. Research on SVR water quality prediction model based on improved sparrow search algorithm. Computational Intelligence and Neuroscience, 2022(1), pp.7327072. https://doi.org/10.1155/2022/7327072.

 

45- Vapnik, V. and Chervonenkis, A.Y., 1974. The method of ordered risk minimization, I. Avtomatika i Telemekhanika, 8(1), pp.21-30.

 

46- Wang, H. and He, G., 2022. Rivers: Linking nature, life, and civilization. River, 1(1), pp.25-36. https://doi.org/10.1002/rvr2.7.

 

47- Wang, R.-Y., Hu, P., Hu, C.-C. and Pan, J.-S., 2022. A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application. International Journal of Distributed Sensor Networks, 18(2), pp.155-168. https://doi.org/10.1177/15501477211073037.

 

48- Waqas, M., Humphries, U.W., Wangwongchai, A., Dechpichai, P., Zarin, R. and Hlaing, P.T., 2024. Incorporating novel input variable selection method for in the different water basins of Thailand. Alexandria Engineering Journal, 86(1), pp.557-576. https://doi.org/10.1016/j.aej.2023.11.046.

 

49- Worawattawechai, T., Intiyot, B., Jeenanunta, C. and Ferrell Jr, W.G., 2022. A learning enhanced golden ball algorithm for the vehicle routing problem with backhauls and time windows. Computers & Industrial Engineering, 168, pp.108044. https://doi.org/10.1016/j.cie.2022.108044.

 

50- Woźniak, M., Szczotka, J., Sikora, A. and Zielonka, A., 2024. Fuzzy logic type-2 intelligent moisture control system. Expert Systems with Applications, 238(18), pp.121-137. https://doi.org/10.1016/j.eswa.2023.121581.

 

51- Wu, J. and Wang, Z., 2022. A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water, 14(4), pp.610. https://doi.org/10.3390/w14040610.

 

 

 

 

 

 

 

 

 

 

 

Irrigation Sciences and Engineering
Volume 48, Issue 3
Autumn 2025
Pages 1-20

  • Receive Date 29 July 2024
  • Revise Date 18 November 2024
  • Accept Date 20 November 2024
  • Publish Date 05 November 2025