Abstract
Artificial neural network (ANN) mathematical models, such as the radial basis function neural network (RBFNN), have been used successfully in different environmental engineering applications to provide a reasonable match between the measured and predicted concentrations of certain important parameters. In the current study, two RBFNNs (one conventional and one based on particle swarm optimization (PSO)) are employed to accurately predict the removal of chemical oxygen demand (COD) from polluted water streams using submerged biofilter media (plastic and gravel) under the influence of different variables such as temperature (18.00–28.50 °C), flow rate (272.16–768.96 m3/day), and influent COD (55.50–148.90 ppm). The results of the experimental study showed that the COD removal ratio had the highest value (65%) when two plastic biofilter media were used at the minimum flow rate (272.16 m3/day). The mathematical model results showed that the closeness between the measured and obtained COD removal ratios using the RBFNN indicates that the neural network model is valid and accurate. Additionally, the proposed RBFNN trained with the PSO method helped to reduce the difference between the measured and network outputs, leading to a very small relative error compared with that using the conventional RBFNN. The deviation error between the measured value and the output of the conventional RBFNN varied between + 0.20 and − 0.31. However, using PSO, the deviation error varied between + 0.058 and − 0.070. Consequently, the performance of the proposed PSO model is better than that of the conventional RBFNN model, and it is able to reduce the number of iterations and reach the optimum solution in a shorter time. Thus, the proposed PSO model performed well in predicting the removal ratio of COD to improve the drain water quality. Improving drain water quality could help in reducing the contamination of groundwater which could help in protecting water resources in countries suffering from water scarcity such as Egypt.
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Abdel daiem MM, Said N, El-Gohary EH, Mansi AH, Abd-Elhamid HF (2019) The effects of submerged biofilter on water quality of polluted drains in Egypt. Int J Civ Struct Eng 6(2):52–55
Abdel-Dayem S (2011) Water quality management in Egypt. Int J Water Resour Dev 27:181–202
Abdel-Gawad S (2008) Actualizing the right to water: an Egyptian perspective for an action plan. Water as a Hum. right Middle East North Africa. Int Dev Res Centre, Ottawa, Canada, pp 133–146
Abd-Elhamid HF, Abdel daiem MMA, El-Gohary EH, Abou Elnaga Z (2017) Safe reuse of treated wastewater for agriculture in Egypt. EXCEED-SWINDON EXPERT WORKSHOP, Water Efficient Cities", Marrakesh, Morocco, November 03-08, 2017
Abd-Elhamid HF, Abdelaal GM, Abd-Elaty I, Said AM (2019) Efficiency of using different lining materials to protect groundwater from leakage of polluted streams. J Water Supply: Res Technol AQUA 68(6):448–459
Akratos CS, Papaspyros JNE, Tsihrintzis VA (2009) Total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands: use of artificial neural networks and development of a design equation. Bioresour Technol 100:586–596
Asempa (2010) The Battle of the Nile, North-East Africa. Afr Confid 51:6–8
Çinar Ö, Hasar H, Kinaci C (2006) Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network. J Biotechnol 123:204–209
Çoruh S, Geyikçi F, Kılıç E, Çoruh U (2014) The use of NARX neural network for modeling of adsorption of zinc ions using activated almond shell as a potential biosorbent. Bioresour Technol 151:406–410
Dash CSK, Behera AK, Dehuri S, Cho S-B (2016) Radial basis function neural networks: a topical state-of-the-art survey. Open Comput Sci 1
Di Baldassarre G, Elshamy M, van Griensven A, Soliman E, Kigobe M, Ndomba P, Mutemi J, Mutua F, Moges S, Xuan Y (2011) Future hydrology and climate in the River Nile basin: a review. Hydrol Sci J 56:199–211
El Monayeri DS, Atta NN, El Mokadem S, Aboul-Fotoh AM (2007a) Effect of organic loading rate and temperature on the performance of horizontal biofilters, in: Proceedings of Eleventh International Water Technology Conference, IWTC11.
El Monayeri DS, Atta NN, El Mokadem S, Gohary E (2007b) Enhancement of bilbeas drain water quality using submerged biofilters (SBS). In: Eleventh International Water Technology Conference, IWTC11
Elbana TA, Bakr N, Elbana M (2017) Reuse of treated wastewater in Egypt: challenges and opportunities, in: Unconventional Water Resources and Agriculture in Egypt. Springer, pp 429–453
El-Gohary EH (2007) Enhancement of streams water quality using in-situ filters, master’s thesis, Zagazig University, Zagazig, Egypt
El-Shatoury S, El-Baz A, Abdel Daiem M, El-Monayeri D (2014) Enhancing wastewater treatment by commercial and native microbial Inocula with factorial design. Life Sci J 11
Ezzeldin R, Hatata A (2018) Application of NARX neural network model for discharge prediction through lateral orifices. Alexandria Eng J 57:2991–2998
Gad WA (2017) Water scarcity in Egypt: causes and consequences. IIOAB J 8:40–47
Galal TM, Eid EM, Dakhil MA, Hassan LM (2018) Bioaccumulation and rhizofiltration potential of Pistia stratiotes L. for mitigating water pollution in the Egyptian wetlands. Int J Phytoremediation 20:440–447
Jia W, Zhao D, Shen T, Su C, Hu C, Zhao Y (2014) A new optimized GA-RBF neural network algorithm. Comput Intell. Neurosci 2014
Kasiviswanathan KS, Agarwal A (2012) Radial basis function artificial neural network: spread selection. Int J Adv Comput Sci 2:394–398
Kumar S (2004) Neural networks: a classroom approach. Tata McGraw-Hill Education
Loutfy NM (2010) Reuse of wastewater in Mediterranean region, Egyptian experience, in: Waste Water Treatment and Reuse in the Mediterranean Region. Springer, pp 183–213
Mahanta B, Singh TN, Ranjith PG (2016a) Influence of thermal treatment on mode I fracture toughness of certain Indian rocks. Eng Geol 210:103–114
Mahanta R, Pandey TN, Jagadev AK, Dehuri S (2016b) Optimized radial basis functional neural network for stock index prediction, in: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, pp 1252–1257
Mohie El Din MO, Moussa AMA (2016) Water management in Egypt for facing the future challenges. J Adv Res 7:403–412
MWRI: Ministry of Water Resources and Irrigation (2014) Water scarcity in Egypt: the urgent need for regional cooperation among the Nile Basin countries, Technical report Egypt Water Resources Management Program.
MWRI: Ministry of Water Resources and Irrigation (2015) EGYPT 2012 State of the water report, monitoring evaluation for water in North Africa (MEwina) project, resources & irrigation. Egypt Water Resources Management Program
Naz M, Uyanik S, Yesilnacar MI, Sahinkaya E (2009) Side-by-side comparison of horizontal subsurface flow and free water surface flow constructed wetlands and artificial neural network (ANN) modelling approach. Ecol Eng 35:1255–1263
Osman R, Ferrari E, McDonald S (2016) Water scarcity and irrigation efficiency in Egypt. Water Econ Policy 2:1650009
Talaat M, Farahat MA, Mansour N, Hatata AY (2020) Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach. Energy 196:117087
Tufaner F, Demirci Y (2020) Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions model. Clean Techn Environ Policy 22:713–724
Tufaner F, Özbeyaz A (2020) Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms. Environ Monit Assess 192:576–1–14
Tufaner F, Avsar Y, Gonullu MT (2017) Modeling of biogas production from cattle manure with co-digestion of different organic wastes using an artificial neural network. Clean Techn Environ Policy 19:2255–2264
Yalcuk A (2012) The macro nutrient removal efficiencies of a vertical flow constructed wetland fed with demineralized cheese whey powder solution. Int J Phytoremediation 14:114–127
Funding
This study is funded by the Zagazig University and the National Organization for Potable Water and Sanitary Drainage (NOPWASD) of Egypt and the Ministry of Housing, Utilities and Urban Communities of the Egyptian government.
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Abdel daiem, Said, and Hatata planned and designed the research; Hatata and El-Gohary conducted the field experiments and modeling; Said, Abdel daiem, and Hatata conducted the statistical analysis and wrote the first draft; Abdel daiem, Hatata, Said, Abd-Elhamid, and El-Gohary wrote, reviewed, and edited the manuscript. All authors read and approved the manuscript.
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Abdel daiem, M.M., Hatata, A., El-Gohary, E.H. et al. Application of an artificial neural network for the improvement of agricultural drainage water quality using a submerged biofilter. Environ Sci Pollut Res 28, 5854–5866 (2021). https://doi.org/10.1007/s11356-020-10964-0
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DOI: https://doi.org/10.1007/s11356-020-10964-0