Abstract
The electrical conductivity (EC) is one of the main characteristics of river water quality, and its prediction is important for river water quality management. The simple regression models are widely used in water and environment studies and can be used for EC prediction when there are limited recorded data. The statistical improvement methods can be used to improve the accuracy of the simple regression models. The objectives of this study are the improvement of the prediction of EC of river flow based on river flow discharge data. Statistical methods include different combinations of logarithmic and inverse transformation with conventional simple linear and non-linear and novel segmented linear models used to improve EC prediction. Two data sets belonging to Zarringol and Ramian station located in northern Iran are used and root mean square error (RMSE) and mean bias error (MBE) are selected to analyze the results. The results show underestimation behavior for investigated combinations of models and transformations. The logarithmic transformation has an improvement effect while inverse transformation decreases the accuracy of models. The segment linear model, due to its ability to fit on the non-monotonic behavior of data, provides better results than other studied simple linear and non-linear regression models. Finally, it is clear that the combination of logarithmic transformation and segmented linear model leads to the best improvement among different combinations of examined statistical improvement methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- y i :
-
Ec values (depended variable)
- x i :
-
discharge values (independent variable)
- β 0, β 1, β 2 :
-
parameters of models
- ϵ i :
-
residuals
- n :
-
number of paired data
- \( \overline{x} \) :
-
mean values of discharge
- \( \overline{y} \) :
-
mean values of EC
References
Aghel B, Rezaei A, Mohadesi M (2018) Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach. Int J Environ Sci Technol:1–10. https://doi.org/10.1007/s13762-018-1896-3
Aminoroayaie Yamini O, Kavianpour MR, Mousavi SH, Movahedi A, Bavandpour M (2018) Experimental investigation of pressure fluctuation on the bed of compound flip buckets. J Hydraul Eng 24(1):45–52. https://doi.org/10.1080/09715010.2017.1344572
Archontoulis SV, Miguez FE (2015) Nonlinear regression models and applications in agricultural research. Agron J 107(2):786–798. https://doi.org/10.2134/agronj2012.0506
Ayyub BM, McCuen RH (2016) Probability, statistics, and reliability for engineers and scientists. CRC press. https://doi.org/10.1201/b12161
Barzegar R, Adamowski J, Moghaddam AA (2016) Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran. Stoch Env Res Risk A 30(7):1797–1819. https://doi.org/10.1007/s00477-016-1213-y
Chang FJ, Tsai YH, Chen PA, Coynel A, Vachaud G (2015) Modeling water quality in an urban river using hydrological factors—data driven approaches. J Environ Manag 151:87–96. https://doi.org/10.1016/j.jenvman.2014.12.014
Chelsea Nagy R, Graeme Lockaby B, Kalin L, Anderson C (2012) Effects of urbanization on stream hydrology and water quality: the Florida Gulf Coast. Hydrol Process 26(13):2019–2030. https://doi.org/10.1002/hyp.8336
Choubin B, Malekian A, Samadi S, Khalighi-Sigaroodi S, Sajedi-Hosseini F (2017) An ensemble forecast of semi-arid rainfall using large-scale climate predictors. Meteorol Appl 24(3):376–386. https://doi.org/10.1002/met.1635
Choubin B, Darabi H, Rahmati O, Sajedi-Hosseini F, Kløve B (2018) River suspended sediment modelling using the CART model: a comparative study of machine learning techniques. Sci Total Environ 615:272–281. https://doi.org/10.1016/j.scitotenv.2017.09.293
Ekeleme AC, Agunwamba JC (2018) Experimental determination of dispersion coefficient in soil. Emerging Sci J 2(4):213–218. https://doi.org/10.28991/esj-2018-01145
Ghorbani MA, Khatibi R, Goel A, FazeliFard MH, Azani A (2016) Modeling river discharge time series using support vector machine and artificial neural networks. Environ Earth Sci 75(8):685. https://doi.org/10.1007/s12665-016-5435-6
Ghorbani K, Salarijazi M, Abdolhosseini M, Eslamian S (2017) Assessment of minimum variance unbiased estimator and beta coefficient methods to improve the accuracy of sediment rating curve estimation. International Journal of Hydrology Science and Technology 7(4):350–363. https://doi.org/10.1504/ijhst.2017.087925
Hooshmand A, Salarijazi M, Bahrami M, Zahiri J, Soleimani S (2013) Assessment of pan evaporation changes in South Western Iran. Afr J Agric Res 8(16):1449–1456. https://doi.org/10.5897/ajar12.371
Isles PD, Xu Y, Stockwell JD, Schroth AW (2017) Climate-driven changes in energy and mass inputs systematically alter nutrient concentration and stoichiometry in deep and shallow regions of Lake Champlain. Biogeochemistry 133(2):201–217. https://doi.org/10.1007/s10533-017-0327-8
Kim JY, Rastogi G, Do Y, Kim DK, Muduli PR, Samal RN, Pattnaik AK, Joo GJ (2015) Trends in a satellite-derived vegetation index and environmental variables in a restored brackish lagoon. Global Ecol Conserv 4:614–624. https://doi.org/10.1016/j.gecco.2015.10.010
Machekposhti KH, Sedghi H, Telvari A, Babazadeh H (2017) Flood analysis in Karkheh River basin using stochastic model. Civil Engineering Journal 3(9):794–808. https://doi.org/10.21859/cej-030915
Marčiukaitis M, Žutautaitė I, Martišauskas L, Jokšas B, Gecevičius G, Sfetsos A (2017) Non-linear regression model for wind turbine power curve. Renew Energy 113:732–741. https://doi.org/10.1016/j.renene.2017.06.039
Moslemzadeh M, Salarijazi M, Soleymani S (2011) Application and assessment of kriging and cokriging methods on groundwater level estimation. J Am Sci 7(7):34–39
Najah AA, El-Shafie A, Karim OA, Jaafar O (2012) Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation. Neural Comput & Applic 21(5):833–841. https://doi.org/10.1007/s00521-010-0486-1
Najah A, El-Shafie A, Karim OA, El-Shafie AH (2013) Application of artificial neural networks for water quality prediction. Neural Comput & Applic 22(1):187–201. https://doi.org/10.1007/s00521-012-0940-3
Nazif S, Karamouz M (2014) Evaluation of climate change impacts on streamflow to a multiple reservoir system using a data-based mechanistic model. J Water Clim Change 5(4):610–624. https://doi.org/10.2166/wcc.2014.012
Nielsen A, Trolle D, Søndergaard M, Lauridsen TL, Bjerring R, Olesen JE, Jeppesen E (2012) Watershed land use effects on lake water quality in Denmark. Ecol Appl 22(4):1187–1200. https://doi.org/10.1890/11-1831.1
Oosterbaan RJ (1994) Frequency and regression analysis. Drainage principles and applications, 16, 175–224
Oosterbaan RJ, Sharma DP, Singh KN, Rao KVGK (1990) Crop production and soil salinity: evaluation of field data from India by segmented linear regression with breakpoint. In Proceedings of the symposium on land drainage for salinity control in arid and semi-arid regions, Vol 3, pp 373–383
Parsa N, Khajouei G, Masigol M, Hasheminejad H, Moheb A (2018) Application of electrodialysis process for reduction of electrical conductivity and COD of water contaminated by composting leachate. Civil Engineering Journal 4(5):1034–1045. https://doi.org/10.28991/cej-0309154
Phung D, Huang C, Rutherford S, Dwirahmadi F, Chu C, Wang X, Nguyen M, Nguyen NH, Do CM, Nguyen TH, Dinh TAD (2015) Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam. Environ Monit Assess 187(5):229. https://doi.org/10.1007/s10661-015-4474-x
Poonam T, Tanushree B, Sukalyan C (2013) Water quality indices—important tools for water quality assessment: a review. International Journal of Advances in Chemistry 1(1):15–28. https://doi.org/10.5121/ijac.2015.1102
Rahaman H, Roy N, Roy A, Ray S, Roy MN (2018) Exploring existence of host-guest inclusion complex of β-cyclodextrin of a biologically active compound with the manifestation of diverse interactions. Emerging Sci J 2(5):251–260. https://doi.org/10.28991/esj-2018-01149
Ravansalar M, Rajaee T (2015) Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model. Environ Monit Assess 187(6):366. https://doi.org/10.1007/s10661-015-4590-7
Sadeghian MS, Salarijazi M, Ahmadianfar I, Heydari M (2016) Stage-discharge relationship in tidal rivers for tidal flood condition. Feb-Fresenius Environ Bull:4111
Salarijazi M, Abdolhosseini M, Ghorbani K, Eslamian S (2016) Evaluation of quasi-maximum likelihood and smearing estimator to improve sediment rating curve estimation. International Journal of Hydrology Science and Technology 6(4):359–370. https://doi.org/10.1504/ijhst.2016.079352
Seeboonruang U (2012) A statistical assessment of the impact of land uses on surface water quality indexes. J Environ Manag 101:134–142. https://doi.org/10.1016/j.jenvman.2011.10.019
Teke A, Yıldırım HB, Çelik Ö (2015) Evaluation and performance comparison of different models for the estimation of solar radiation. Renew Sust Energ Rev 50:1097–1107. https://doi.org/10.1016/j.rser.2015.05.049
Vystavna Y, Hejzlar J, Kopáček J (2017) Long-term trends of phosphorus concentrations in an artificial lake: socio-economic and climate drivers. PLoS One 12(10):e0186917. https://doi.org/10.1371/journal.pone.0186917
Whitehead PG, Barbour E, Futter MN, Sarkar S, Rodda H, Caesar J, Butterfield D, Jin L, Sinha R, Nicholls R, Salehin M (2015) Impacts of climate change and socio-economic scenarios on flow and water quality of the Ganges, Brahmaputra and Meghna (GBM) river systems: low flow and flood statistics. Environ Sci Processes Impacts 17(6):1057–1069. https://doi.org/10.1039/c4em00619d
Yu S, Xu Z, Wu W, Zuo D (2016) Effect of land use types on stream water quality under seasonal variation and topographic characteristics in the Wei River basin, China. Ecol Indic 60:202–212. https://doi.org/10.1016/j.ecolind.2015.06.029
Zheng B, Campbell JB, de Beurs KM (2012) Remote sensing of crop residue cover using multi-temporal Landsat imagery. Remote Sens Environ 117:177–183. https://doi.org/10.1016/j.rse.2011.09.016
Acknowledgements
We thank the Gorgan University of agricultural sciences and natural resources for support of the project entitled “Application of statistical correction methods for improvement of river EC and TDS estimation (Code: 95-354-49)”.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Editorial handling: Broder J. Merkel
Rights and permissions
About this article
Cite this article
Salarijazi, M., Ghorbani, K. Improvement of the simple regression model for river’ EC estimation. Arab J Geosci 12, 235 (2019). https://doi.org/10.1007/s12517-019-4392-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-019-4392-2