Iranian Water Research Journal

Iranian Water Research Journal

Reducing water quality parameters of Zayandeh-Rood River using principal component analysis and simulating the impact of land use and rainfall on water quality parameters

Document Type : Original Article

Authors
Kamran Mohsenifar 1
Ebrahim Pazira 2
Sayyed-Hassan Tabatabaei 3
Payam Najafi 4
1 Department of Soil Science, Ahv.C., Islamic Azad University, Ahvaz, Iran.
2 Department of Soil Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran
3 Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, CHB, Iran
4 Department of Soil and Water Sciences, Islamic Azad University, Khorasgan Branch, Isfahan, Iran
10.22034/IWRJ.2025.15017.2642
Abstract
Introduction:
Land use around rivers is one of the key factors contributing to their pollution. Burying waste and garbage in the soil, agricultural activities, and the use of various fertilizers and pesticides ultimately lead to river pollution. Spatial and temporal changes in the chemical quality of river water depend on various factors. Due to the importance of quantitative parameters in determining the expected response of the river, it is essential to measure these parameters using models. Measuring these parameters in large volumes is time-consuming, costly, and requires high accuracy; so, the need for some indirect methods to estimate these parameters is already apparent. Principal component analysis (PCA) is one of the methods used to overcome this problem. So that the most accurate answer is achieved with less cost and with the least information. Artificial neural networks (ANNs) are suitable for water quality assessment due to their ability to solve nonlinear problems compared to conventional techniques. The aim of this research is to investigate the efficiency of parameter reduction using PCA and the effect of land use and precipitation on some water quality factors of the Zayandeh-Rood River using ANN.
Materials and Methods:
To evaluate the effect of land use and precipitation on some water quality factors, data were collected from the Water and Environment Organization, the General Directorate of Natural Resources and Watershed Management, and the Provincial Research Center. These data were collected daily from 12 hydrological stations in the two provinces of Chaharmahal and Bakhtiari and Isfahan for the Zayandeh-Rood River over a 10-year period. According to the hydrological stations monitoring the water quality of the river from upstream to downstream, the Zayandeh-Rood River basin was divided into 12 sub-basins. The area of each sub-basin was calculated using partitioning software. PCA was applied to some water quality parameters, including total dissolved solids (TDS), electrical conductivity (EC), water reaction (pH), carbonate (CO3), bicarbonate (HCO3), chlorine (Cl), sulfate (SO4), calcium

(Ca), magnesium (Mg), sodium (Na), potassium (K), and sodium absorption ratio (SAR), as well as discharge. This was done to reduce the main data arrays affecting the water quality of the Zayandeh-Rood River and to identify pollution sources using XLSTAT 2018 software. To check the suitability of the dataset for PCA, the Kaiser-Meyer-Olkin test and the Bartlett sphericity test were used. After extracting land use and rainfall data for the Zayandeh-Rood basin, modeling was performed using MATLAB software, with 60% of the data randomly selected for network training and the remaining 40% for validation and entered into the network. The effect of these two factors on some river water properties was analyzedusing the backpropagation algorithm, and by changing the number of neurons and layers, the mean square error, coefficient of determination, and Mean Absolute Error (MAE) were calculated for each factor.
Result and Discussion:
According to the results, PCA can clarify the order of the data in detail by eliminating unimportant data as the Kaiser Meyer Olkin (KMO) sample adequacy index was 0.618 and Bartlett's Sphericity test was 0.001, indicating that the number of samples for the test was sufficient. The results of PCA analysis showed that the first six components accounted for 94.8% of the total variance. This indicates that PCA is effective in reducing the slope of change among variables. Because the sum of the variances of the first six components is close to one. This implies a linear correlation among variables, because PCA uses a linear transformation to transform variables into principal components. The ANN results showed that the total coefficient of determination was 0.79 and the root mean square error was 0.20, and the coefficient of determination for SAR, TDS, EC, and Na were 0.88, 0.84, 0.83, and 0.83, respectively, indicating the high accuracy of the network predictions. The lowest coefficient of determination for the three factors pH, CO3, and discharge (0.16, 0.10, and 0.43, respectively), were all below0.50, and for other water quality factors, the coefficient of determination was above 0.50. The results indicated that the coefficient of determination for land use and rainfall on the three factors of discharge, pH and CO3 is very weak and the model provides very little explanation of the variance of the dependent variable. The results of the Nash-Sutcliffe coefficient showed that it is appropriate for all measured factors (except pH, CO3 and discharge) and is above 0.6 and this value indicates that the model has been able to explain the observed changes well and provide relatively accurate predictions. The outcomes also revealed that the reduced values in PCA are the same factors predicted with the highest coefficient of determination in the ANN model.
Keywords
River
Nash coefficient
Artificial Neural Network
water quality
1.     Ali, S. H., Cook, T., Ewaid, S. H. and Gamagedara, S., 2023. Development of a Water Quality Index Using Sparse Principal Component Analysis for the Tigris River in Iraq. Water Resources, 50(1), pp. 152-167. https://doi.org/10.1134/S0097807823010037
 
2.     Al-Maqaleh, B. M., Al-Mansoub, A. A. and andAl-Badani, F. N., 2016. Forecasting using artificial neural network and statistics models. International Journal Education and Management Engineering, 3, pp. 20-32. https://doi.org/10.5815/ijeme.2016.03.03
 
3.     Al-Tamir, M., 2005. water quality index for a group of wells in northwest of mosul city. Rafidain Journal of Science, 16(6), pp. 27-40. https://doi.org/10.33899/rjs.2005.41816
 
4.     Argatov, I., 2019. Artificial Neural Networks (ANNs) as a Novel Modeling Technique in Tribology. Frontiers in Mechanical Engineering, 5. https://doi.org/10.3389/fmech.2019.00030.
 
5.     Benti, F. and Kebede, B. 2024. Modeling the impacts of extreme climate scenarios on soil acidity (pH and exchangeable aluminum) in Abbay River Basin, Ethiopia. Heliyon, 10, e33448. https://doi.org/10.1016/j.heliyon.2024.e33448.
 
6.     Çamdevýren, H., Demýr, N., Kanik, A. and Keskýn, S., 2005. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecological Modelling, 181(4), pp. 581-589. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2004.06.043.
 
7.     Chicco, D., Warrens, M. J. and Jurman, G., 2021. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peer J. Comput Sci., 7, e623. https://doi.org/10.7717/peerj-cs.623.
 
8.     Fuller, R., Landrigan, P. J., Balakrishnan, K., Bathan, G., Bose-O'Reilly, S., Brauer, M., Caravanos, J., Chiles, T., Cohen, A. and Corra, L., 2022. Pollution and health: a progress update. The Lancet Planetary Health, 6(6), pp. e535-e547. https://doi.org/10.1016/s2542-5196(22)00090-0.
 
9.     Haghnadri, F., Mirzaei, R. and Afzali, A., 2018. Environmental Vulnerability Assessment of Lorestan Province Using Multi Criteria Decision Analysis. Geography and Environmental Sustainability, 7(4), 19-34. https://ges.razi.ac.ir/article_868_ [In Persian]
 
10. Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J.M. and Fernandez, L., 2000. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Research, 34(3), pp. 807-816. https://doi.org/https://doi.org/10.1016/S0043-1354(99)00225-0
 
11. Ibrahim, A., Ismail, A., Juahir, H., Iliyasu, A. B., Wailare, B. T., Mukhtar, M. and Aminu, H., 2023. Water quality modelling using principal component analysis and artificial neural network. Marine Pollution Bulletin, 187, 114493. https://doi.org/https://doi.org/10.1016/j.marpolbul.2022.114493.
 
12. Jamab. 1998. Comprehensive water plan of the country Zayandehroud catchment area. Jamab Consulting Engineers Company affiliated with the Ministry of Energy. [In Persian].
 
13. Juahir, H., Ghazali, A., Ismail, A., Mohamad, M., Mohamad Hamzah, F., Lokman, M., Mohd Lasim, M. L., Shahriz, M., Elfithri, R., Sunardi, S. and Syaukat, Y., 2018. Spatial and Temporal Assessment of Titiwangsa Lake Water Quality Using Chemometrics Analysis. International Journal of Engineering and Technology (UAE), 7, pp. 20-25. https://doi.org/10.14419/ijet.v7i3.14.16856
 
14. Junninen, H., Niska, H., Tuppurainen, K., Ruuskanen, J. and Kolehmainen, M., 2004. Methods for imputation of missing values in air quality data sets. Atmospheric Environment, 38(18), pp. 2895-2907. https://doi.org/https://doi.org/10.1016/j.atmosenv.2004.02.026
 
15. Kang, S., Kim, J. H., Ryu, J., Bong, Y. and Shin, K., 2024. Contribution of carbonate-derived dissolved inorganic carbon into autochthonous particulate organic carbon in two small temperate Korean rivers (Geum and Seomjin). Heliyon, 10, e31154. https://doi.org/10.1016/j.heliyon.2024.e31154.
 
16. Khalili, R., Sabzehmeidani, M. M., Parvinnia, M. and Ghaedi, M., 2022. Removal of hexavalent chromium ions and mixture dyes by electrospun PAN/graphene oxide nanofiber decorated with bimetallic nickel–iron LDH. Environmental Nanotechnology, Monitoring and Management, 18, 100750. 2022.100750 [In Persian] https://doi.org/https://doi.org/10.1016/j.enmm.2022.100750
 
17. Legates, D. R. and McCabe Jr., G.J., 1999. Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), pp. 233-241.
 
18. Liu, C.W., Lin, K.H. and Kuo, Y.M., 2003. Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Science of The Total Environment, 313(1), pp. 77-89. https://doi.org/https://doi.org/10.1029/1998WR900018.
 
19. Looi, L. J., Aris, A. Z., Wan Johari, W. L., Md. Yusoff, F. and Hashim, Z., 2013. Baseline metals pollution profile of tropical estuaries and coastal waters of the Straits of Malacca. Marine Pollution Bulletin, 74(1), pp. 471-476. https://doi.org/https://doi.org/10.1016/j.marpolbul.2013.06.008.
 
20. Ma, G. and Jia, L., 2022. Heavy Metal Water Pollution: Transport and Transformation, Impacts and Treatment Technologies. In K. Ujikawa, M. Ishiwatari, and E. v. Hullebusch, Environment and Sustainable Development Singapore. https://doi.org/10.1007/978-981-19-1704-2_10.
 
21. Mohsenifar, K. and Tabatabaei, S., 2010. Effect of drought on pollution of LENJ station of Zayndedood River by artificial neural network. XVIIth World Congress of the International Commission of Agricultural Engineering (CIGR), CSBE/SCGAB, Quebec City, Canada.
 
22. Mohsenifar, K., Pazira, E., Panahpour, E. and Beni, Z.H.M., 2010. Effect of Drought on Pollution of LENJ Station of Zayandeh Rood River by Artificial Neural Network (ANN) and Effect of Salinity in This Region Agriculture. International Journal of Agronomy and Plant Production, 1, pp. 55-60.
 
23. Mohsenifar, K., Pazira, E., Tabatabaei, S.-H., Najafi, P., and Yarali, N. 2011. Evaluation of landuse and rainfall on Zayanderood river water quality using ANN. Advances in Environmental Biology, 5, pp. 756-764.
 
24. Nash, J. E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), pp. 282-290. https://doi.org/https://doi.org/10.1016/0022-1694(70)90255-6
 
25. Parinet, B., Lhote, A. and Legube, B., 2004. Principal Component Analysis: An Appropriate Tool for Water Quality Evaluation and Management-Application to a Tropical Lake System. Ecological Modelling, 178, 295-311.https://doi.org/10.1016/j.ecolmodel.2004.03.007.
 
26. Qadir, A., Malik, R. N. and Husain, S. Z., 2008. Spatio-temporal variations in water quality of Nullah Aik-tributary of the river Chenab, Pakistan. Environmental Monitoring and Assessment, 140(1), 43-59. https://doi.org/10.1007/s10661-007-9846-4
 
27. Satari, M., shirini, k. and javidan, S., 2024. Evaluating the efficiency of parameter reduction methods in improving the accuracy of water quality index modeling in Qezal Ozen River using machine learning algorithms. Water and soil modeling and management, 4(2), 89-104. https://doi.org/10.22098/mmws.2023.12434.1241 [In Persian]
 
28. Simeonov, V., Stratis, J. A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., Sofoniou, M. and Kouimtzis, T., (2003). Assessment of the surface water quality in Northern Greece. Water Research, 37(17), pp. 4119-4124.
 
29. Singh, K. P., Malik, A. and Sinha, S., 2005. Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques—a case study. Analytica Chimica Acta, 538(1), pp. 355-374. https://doi.org/https://doi.org/10.1016/j.aca.2005.02.006.
 
30. Soleimani, P., Hoseini, A. and Sabzzadeh, I., 2021. Water quality modeling of Abdanan river using HEC-RAS model the 4th National Conference on Technology Development of Water Science, Watershed Management and River Engineering., https://doi.org/https://doi.org/10.1016/S0043-1354(03)00398-1 [In Persian]
 
31. Xie, H., Ma, F. and Bai, Q., 2009. Prediction of Indoor Air Quality Using Artificial Neural Networks. 2009 Fifth International Conference on Natural Computation, https://doi.org/10.1109/ICNC.2009.502
 
32. Yüksel, B., Ustaoğlu, F. and Arıca, E., 2021. Impacts of a Garbage Disposal Facility on the Water Quality of Çavuşlu Stream in Giresun, Turkey: A Health Risk Assessment Study by a Validated ICP-MS Assay. Aquatic sciences and engineering, 36, pp. 181-192. https://doi.org/10.26650/ASE2020845246.
 

  • Receive Date 21 November 2024
  • Accept Date 09 January 2025
  • Publish Date 21 March 2025