77
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Prediction of nitrate concentrations using multiple linear regression and radial basis function neural network in the Cheliff River basin, Algeria

, , ORCID Icon, , &
Pages 77-89 | Received 18 Jun 2022, Accepted 20 Apr 2023, Published online: 09 May 2023

References

  • Abdalrahman G, Lai SH, Kumar P, Ahmed AN, Sherif M, Sefelnasr A, Chau KW, Elshafie A. 2022. Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques. Eng Appl Comput Fluid Mech. 16(1):397–421. doi:10.1080/19942060.2021.2019126.
  • Akkoyunlu A, Altun H, Cigizoglu HK. 2011. Depth-Integrated estimation of dissolved oxygen in a lake. J Environ Eng. 137(10):961–967. doi:10.1061/(ASCE)EE.1943-7870.0000376.
  • Alizadeh MJ, Kavianpour MR. 2015. Development of wavelet-ANN models to predict water quality parameters in hilo Bay, Pacific Ocean. Mar Pollut Bull. 98(1–2):171–178. doi:10.1016/j.marpolbul.2015.06.052.
  • Antanasijević D, Pocajt V, Povrenović D, Perić-Grujić A, Ristić M. 2013. Modelling of dissolved oxygen content using artificial neural networks: Danube river, north Serbia, case study. Environ Sci Pollut Res. 20(12):9006–9013. doi:10.1007/s11356-013-1876-6.
  • Arabgol R, Sartaj M, Asghari K. 2016. Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model. Environ Model Assess. 21(1):71–82. doi:10.1007/s10666-015-9468-0.
  • Boudibi S, Sakaa B, Benguega Z. 2021. Spatial variability and risk assessment of groundwater pollution in El-outaya region, Algeria. J Afr Earth Sci. 176:104135. doi:10.1016/j.jafrearsci.2021.104135.
  • Broomhead D, Lowe D. 1988. Multivariable functional interpolation and adaptive networks. Complex Syst. 2:321–355.
  • Csábrági A, Molnár S, Tanos P, Kovács J. 2017. Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube. Ecol Eng. 100:63–72. doi:10.1016/j.ecoleng.2016.12.027.
  • Del Águila MMR, Benítez-Parejo N. 2011. Simple linear and multivariate regression models. Allergol Immunopathol. 39(3):159–173. doi:10.1016/j.aller.2011.02.001.
  • Ehteram M, Ahmed AN, Latif SD, Huang YF, Alizamir M, Kisi O, Mert C, El-Shafie A. 2021a. Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environ Sci Pollut Res. 28(2):1596–1611. doi:10.1007/s11356-020-10421-y.
  • Ehteram M, Ahmed AN, Ling L, Fai CM, Latif SD, Afan HA, Banadkooki FB, El-Shafie A. 2020. Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm. Water (Switzerland). 12(3):902. doi:10.3390/w12030902.
  • Ehteram M, Teo FY, Ahmed AN, Latif SD, Huang YF, Abozweita O, Al-ansari N, El-shafie A. 2021b. Performance improvement for infiltration rate prediction using hybridized adaptive neuro-fuzzy inferences system (ANFIS) with optimization algorithms. Ain Shams Eng J. 12(2):1665–1676. doi:10.1016/j.asej.2020.08.019.
  • Garc EM, Mateo LF, Garc S, Isabel MM, Quijano MÁ. 2022. Use of artificial neural networks as a predictive tool of dissolved oxygen present in surface water discharged in the coastal lagoon of the Mar menor (murcia, Spain). Int J Environ Health Res. 19(8):4531. doi:10.3390/ijerph19084531.
  • Heddam S. 2016. New modelling strategy based on radial basis function neural network (RBFNN) for predicting dissolved oxygen concentration using the components of the gregorian calendar as inputs: case study of clackamas river, Oregon, USA, Oregon, USA. Model Earth Syst Environ. 2(4):1–5. doi:10.1007/s40808-016-0232-5.
  • Heddam S, Kisi O. 2017. Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environ Sci Pollut Res. 24(20):16702–16724. doi:10.1007/s11356-017-9283-z.
  • Kişi Ö. 2008. River flow forecasting and estimation using different artificial neural network techniques. Hydrol Res. 39(1):27–40. doi:10.2166/nh.2008.026.
  • Kumar P, Lai SH, Mohd NS, Kamal MR, Afan HA, Ahmed AN, Sherif M, Sefelnasr A, El-Shafie A. 2020. Optimised neural network model for river-nitrogen prediction utilizing a new training approach. PLOS ONE. 15(9):e0239509. doi:10.1371/journal.pone.0239509.
  • Lai V, Malek MA, Abdullah S, Latif SD, Ahmed AN. 2020. Time-series prediction of sea level change in the east coast of peninsular Malaysia from the supervised learning approach. Int J Des Nat Ecodynamics. 15(3):409–415. doi:10.18280/ijdne.150314.
  • Latif SD, Ahmed AN. 2021. Application of deep learning method for daily streamflow time-series prediction: a case study of the kowmung river at cedar ford, Australia. Int J Sustain Dev Plan. 16(3):497–501. doi:10.18280/ijsdp.160310.
  • Latif SD, Ahmed AN, Sathiamurthy E, Huang YF, El-Shafie A. 2021a. Evaluation of deep learning algorithm for inflow forecasting: a case study of durian tunggal reservoir, peninsular Malaysia. Nat Hazards [Internet]. 109(1):351–369. doi:10.1007/s11069-021-04839-x.
  • Latif SD, Ahmed AN, Sherif M, Sefelnasr A, El-Shafie A. 2021b. Reservoir water balance simulation model utilizing machine learning algorithm. Alex Eng J. 60(1):1365–1378. doi:10.1016/j.aej.2020.10.057.
  • Latif SD, Azmi MSBN, Ahmed AN, Fai CM, El-Shafie A. 2020. Application of artificial neural network for forecasting nitrate concentration as a water quality parameter: a case study of feitsui reservoir, Taiwan. Int J Des Nat Ecodyn. 15(5):647–652. doi:10.18280/ijdne.150505.
  • Latif SD, Birima AH, Ahmed AN, Hatem DM, Al-ansari N, Fai CM, El-shafie A. 2022. Development of prediction model for phosphate in reservoir water system based machine learning algorithms. Ain Shams Eng. J. 13(1):101523. doi:10.1016/j.asej.2021.06.009.
  • Lima AR, Cannon AJ, Hsieh WW. 2015. Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation. Environ Model Softw. 73:175–188. doi:10.1016/j.envsoft.2015.08.002.
  • Liu S, Xu L, Jiang Y, Li D, Chen Y, Li Z. 2014. A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture. Eng Appl Artif Intell. 29:114–124. doi:10.1016/j.engappai.2013.09.019.
  • Mateo-Sagasta J, Marjani S, Turral H, Burke J. 2017. Water pollution from agriculture: a global review. [place unknown]. http://www.fao.org/3/a-i7754e.pdf.
  • Najafzadeh M, Ghaemi A, Emamgholizadeh S. 2019. Prediction of water quality parameters using evolutionary computing-based formulations. Int J Environ Sci Technol. 16(10):6377–6396. doi:10.1007/s13762-018-2049-4.
  • Najafzadeh M, Homaei F, Farhadi H. 2021. Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models. Artif Intell Rev. 54:4619–4651. doi:10.1007/s10462-021-10007-1.
  • Najafzadeh M, Saberi Movahed F, Sarkamaryan S. 2017. NF-GMDH based self-organized systems to predict bridge pier scour depth under debris flow effects. Mar Georesources Geotechnol. doi:10.1080/1064119X.2017.1355944.
  • Najah A, Teo FY, Chow MF, Huang YF, Latif SD, Abdullah S, Ismail M, El-Shafie A. 2021. Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: case studies in Malaysia. Int J Environ Sci Technol. 18(4):1009–1018. doi:10.1007/s13762-021-03139-y.
  • Nourani V, Andalib G, Dąbrowska D. 2017. Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based multi-station nitrate modeling of watersheds. J Hydrol. 548:170–183. doi:10.1016/j.jhydrol.2017.03.002.
  • Parsaie A, Haghiabi AH, Latif SD, Tripathi RP. 2021. Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models. Environ Sci Pollut Res. 28(43):60842–60856. doi:10.1007/s11356-021-15029-4.
  • Rezaeian-Zadeh M, Tabari H, Abghari H. 2013. Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J Geosci. 6(7):2529–2537. doi:10.1007/s12517-011-0517-y.
  • Sakaa B, Chaffai H, Hani A. 2020. ANNs approach to identify water demand drivers for Saf-Saf river basin. J Appl Water Eng Res. 8(1):44–54. doi:10.1080/23249676.2020.1719220.
  • Šiljić A, Antanasijević D, Perić-Grujić A, Ristić M, Pocajt V. 2015. Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on monte carlo simulations. Environ Sci Pollut Res. 22(6):4230–4241. doi:10.1007/s11356-014-3669-y.
  • Stamenković LJ, Kurilić SM, Ulniković VP. 2020. Prediction of nitrate concentration in Danube river water by using artificial neural networks. Water Supply. 20(6):2119–2132. 1–14. doi:10.2166/ws.2020.104
  • Touhari F, Meddi M, Mehaiguene M, Razack M. 2015. Hydrogeochemical assessment of the upper cheliff groundwater (north west Algeria). Environ Earth Sci. 73(7):3043–3061. doi:10.1007/s12665-014-3598-6.
  • Uyanık GK, Güler N. 2013. A study on multiple linear regression analysis. Procedia Soc Behav Sci. 106:234–240. doi:10.1016/j.sbspro.2013.12.027.
  • Zhou Y, Mu T, Pang ZH, Zheng C. 2019. A survey on hyper basis function neural networks. Syst Sci Control Eng. 7(1):495–507. doi:10.1080/21642583.2019.1699474.
  • Zounemat-kermani Mohammad, Kisi Ozgur, Rajaee Taher. 2013. Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Applied Soft Computing. 13(12):4633–4644. doi:10.1016/j.asoc.2013.07.007.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.