New Approach to Predict Fecal Coliform Removal for Stormwater Biofilters Application

Sai Hin Lai; Chun Hooi Bu; Ren Jie Chin; Xiang Ting Goh; Fang Yenn Teo

doi:10.31436/iiumej.v23i2.2173

Authors

Sai Hin Lai University of Malaya https://orcid.org/0000-0002-7143-4805
Chun Hooi Bu University of Malaya https://orcid.org/0000-0002-2186-5447
Ren Jie Chin Universiti Tunku Abdul Rahman https://orcid.org/0000-0001-5422-5123
Xiang Ting Goh University of Malaya https://orcid.org/0000-0002-2186-5447
Fang Yenn Teo University of Nottingham (Malaysia Campus) https://orcid.org/0000-0002-5529-1381

DOI:

https://doi.org/10.31436/iiumej.v23i2.2173

Keywords:

Artificial intelligence, Biofilters, Fecal coliform, Neural network, Stormwater

Abstract

Fecal coliform removal using stormwater biofilters is an important aspect of stormwater management. A model that can provide an accurate prediction of fecal coliform removal is essential. Therefore, feedforward backpropagation neural network (FBNN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using a range of input features, namely grass type, the thickness of biofilter, and initial concentration of E. coli, while the estimated final concentration of E. coli was the output variable. The ANFIS model shows a better overall performance than the FBNN model, as it has a higher R²-value of 0.9874, lower MAE and RMSE values of 3.854 and 6.004 respectively, and a smaller average percentage error of 14.2%. Hence, the proposed ANFIS model can be served as an advanced alternative to replace the need for laboratory work.

ABSTRAK: Penyingkiran kolifom tinja menggunakan turas biologi (bioturas) air hujan merupakan aspek penting dalam pengurusan air hujan. Model yang dapat menunjukkan anggaran tepat tentang penyingkiran kolifom tinja adalah penting. Oleh itu, model rangkaian suapan neural perambatan belakang (FBNN) dan sistem adaptasi inferen neuro-fuzi (ANFIS) telah dibentukkan menggunakan pelbagai ciri input, iaitu jenis rumput, ketebalan bioturas dan kepekatan awal E. coli, manakala anggaran kepekatan akhir bagi E. coli merupakan hasil pembolehubah. Model ANFIS menunjukkan peningkatan keseluruhan yang lebih baik berbanding model FBNN, kerana ia mempunyai nilai R² yang lebih tinggi iaitu 0.9874, nilai MAE dan RMSE yang lebih rendah iaitu sebanyak 3.854 dan 6.004 masing-masing, dan ralat peratusan purata yang lebih kecil sebanyak 14.2%. Oleh itu, model ANFIS yang dicadangkan boleh dijadikan alternatif awal bagi menggantikan keperluan kerja makmal.

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Author Biographies

Sai Hin Lai, University of Malaya

Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

Chun Hooi Bu, University of Malaya

Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

Ren Jie Chin, Universiti Tunku Abdul Rahman

Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman

Xiang Ting Goh, University of Malaya

Department of Parasitology, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia

Fang Yenn Teo, University of Nottingham (Malaysia Campus)

Department of Civil Engineering, Faculty of Engineering, University of Nottingham (Malaysia Campus), 43500 Semenyih, Malaysia

References

Payne EG, Fletcher TD, Cook PL, Deletic A, Hatt BE. (2014) Processes and drivers of nitrogen removal in stormwater biofiltration. Crit. Rev. Environ. Sci. Technol., 44: 796-846. DOI: https://doi.org/10.1080/10643389.2012.741310

Kanda R, Kishimoto N, Hinobayashi J, Hashimoto T, Tanaka S, Murakami Y. (2017) Influence of temperature and COD loading on biological nitrification-denitrification process using a trickling filter: An empirical modeling approach. Int. J. Environ. Res., 11: 71-82. DOI: https://doi.org/10.1007/s41742-017-0008-4

Samhan SA, Al-Sa'ed RM, Mahmoud NJ. (2007) Removal of pathogenic microorganisms in pilot?scale UASB?septic tanks and Albireh urban wastewater treatment plant in Palestine. Water Int., 32: 798-809. DOI: https://doi.org/10.1080/02508060.2007.9671999

Wang R, Li X, Feng Y, Tariq F, Li K, Wei Y, . . . Chen L. (2019) Removal of formaldehyde from the air with a suspended growth bioreactor. Int. J. Environ. Res., doi:https://doi.org/10.1007/s41742-019-00228-2 DOI: https://doi.org/10.1007/s41742-019-00228-2

Chandrasena GI, Pham T, Payne EG, Deletic A, McCarthy DT. (2014) E. coli removal in laboratory scale stormwater biofilters: Influence of vegetation and submerged zone. J. Hydrol., 519: 814-822 DOI: https://doi.org/10.1016/j.jhydrol.2014.08.015

Li Y, McCarthy DT, Deletic A. (2016) Escherichia coli removal in copper-zeolite-integrated stormwater biofilters: Effect of vegetation, operational time, intermittent drying weather. Ecol. Eng., 90: 234-243. DOI: https://doi.org/10.1016/j.ecoleng.2016.01.066

Chandrasena GI, Shirdashtzadeh M, Li Y, Deletic A, Hathaway, JM, Mccarthy DT. (2017) Retention and survival of E. coli in stormwater biofilters: Role of vegetation, rhizosphere microorganisms and antimicrobial filter media. Ecol. Eng., 102: 166-177. DOI: https://doi.org/10.1016/j.ecoleng.2017.02.009

Galbraith P, Henry R, McCarthy DT. (2019) Rise of the killer plants: investigating the antimicrobial activity of Australian plants to enhance biofilter-mediated pathogen removal. J. Biol. Eng., 13: 52. DOI: https://doi.org/10.1186/s13036-019-0175-2

Hunt WF, Smith JT, Jadlocki SJ, Hathaway JM, Eubanks PR. (2008) Pollutant removal and peak flow mitigation by a bioretention cell in urban Charlotte, N.C. J. Environ. Eng., 134: 403-408. DOI: https://doi.org/10.1061/(ASCE)0733-9372(2008)134:5(403)

Bu CH, Lai SH, Goh XT, Chong WT, Chin RJ. (2021) Influence of filter media depth and vegetation on Faecal Coliform removal by stormwater biofilters. Water Environ. J., 35: 181-189. DOI: https://doi.org/10.1111/wej.12618

Yetilmezsoy K, Ozkaya B, Cakmakci M. (2011) Artificial intelligence-based prediction models for environmental engineering. Neural Netw. World, 3: 193-218. DOI: https://doi.org/10.14311/NNW.2011.21.012

Chau, K-w. (2006) A review on integration of artificial intelligence into water quality modelling. Mar. Pollut. Bull., 52: 726-733. DOI: https://doi.org/10.1016/j.marpolbul.2006.04.003

Thompson KA, Dickenson ER. (2021) Using machine learning classification to detect simulated increases of de facto reuse and urban stormwater surges in surface water. Water Res., 204: 117556. DOI: https://doi.org/10.1016/j.watres.2021.117556

Fang H, Jamali B, Deletic A, Zhang K. (2021) Machine learning approaches for predicting the performance of stormwater biofilters in heavy metal removal and risk mitigation. Water Res., 200: 117273. DOI: https://doi.org/10.1016/j.watres.2021.117273

DID Malaysia (2012) Urban stormwater management manual for Malaysia 2nd Edition. Kuala Lumpur, Department of Irrigation and Drainage Malaysia.

Chin RJ, Lai SH, Shaliza I, Wan Zurina WJ, Ahmed Elshafie AH. (2019) New approach to mimic rheological actual shear rate under wall slip condition. Eng. Comput., 35: 1409-1418. DOI: https://doi.org/10.1007/s00366-018-0670-y

Deng B, Chin RJ, Tang Y, Jiang C, Lai SH. (2019) New approach to predict the motion characteristics of single bubbles in still water. Appl. Sci., 9: 3981. DOI: https://doi.org/10.3390/app9193981

Alimissis A, Philippopoulos K, Tzanis CG, Deligiorgi D. (2018) Spatial estimation of urban air pollution with the use of arti?cial neural network models. Atmos. Environ., 191: 205-213. DOI: https://doi.org/10.1016/j.atmosenv.2018.07.058

Zhang Y, Chen H, Yang B, Fu S, Yu J, Wang Z. (2018) Prediction of phosphate concentrate grade based on artificial neural network modeling. Results Phys., 11: 625-628. DOI: https://doi.org/10.1016/j.rinp.2018.10.011

Akter T, Desai S. (2018) Developing a predictive model for nanoimprint lithography usingarti?cial neural networks. Mater. Des., 160: 836-848. DOI: https://doi.org/10.1016/j.matdes.2018.10.005

Chin RJ, Lai SH, Shaliza I, Wan Zurina WJ, Elshafie A. (2019) Rheological wall slip velocity prediction model based on artificial neural network. J. Exp. Theor. Artif. Intell., 31: 659-676. DOI: https://doi.org/10.1080/0952813X.2019.1592235

Pham DT, Sagiroglu S. (2001) Training multilayered perceptrons for pattern recognition: a comparative study of four training algorithms. Int. J. Mach. Tools Manuf., 41: 419-430. DOI: https://doi.org/10.1016/S0890-6955(00)00073-0

Demuth H, Beale M. (2014) Neural Network Toolbox for Use with MATLAB User's Guide Version 4. Natick, MA, The MathWorks Inc.

Sharma B, Venugopalan K. (2014) Comparison of Neural Network Training Functions for Hematoma Classification in Brain CT Images. IOSR J. Comput. Eng., 16: 31-35. DOI: https://doi.org/10.9790/0661-16123135

Alsmadi MK, Omar KB, Noah SA (2009) Back propagation algorithm: The best algorithm among the multi-layer perceptron. Int. J. Comput. Sci. Netw., 9: 378-383.

Chin RJ, Lai SH, Shaliza I, Wan Zurina WJ, Elsha?e A. (2020) ANFIS-based model for predicting actual shear rate associated with wall slip phenomenon. Soft Comput., 24: 9639-9649. DOI: https://doi.org/10.1007/s00500-019-04475-5

Ghasemi E, Kalhori H, Bagherpour R. (2016) A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting. Eng. Comput., 32: 607-614. DOI: https://doi.org/10.1007/s00366-016-0438-1

Korotkikh V, Korotkikh G (2008) On principles in engineering of distributed computing systems. Soft Comput., 12: 201-206. DOI: https://doi.org/10.1007/s00500-007-0191-x

Jang JS (1993) Adaptive network-based Fuzzy Inference System. IEEE Trans. Syst. Man Cybern. Syst., 23: 665-685. DOI: https://doi.org/10.1109/21.256541

Jang JS, Sun CT. (1997) Neuro-Fuzzy and soft computing: A computing approach to learning and machine intelligence, Englewood Cliffs, New Jersey, Prentice Hall. DOI: https://doi.org/10.1109/TAC.1997.633847

Jang JS, Sun CT, Mizutani E. (1997) A computational approach to learning and machine intelligence. Neuro-Fuzzy and Soft Computing. Upper Saddle River, New Jersey, Prentice-Hall.

Barrett ME, Limouzin M, Lawler DF. (2013) Effects of media and plant selection on biofiltration performance. J. Environ. Eng., 139: 462-470. DOI: https://doi.org/10.1061/(ASCE)EE.1943-7870.0000551

Read J, Fletcher TD, Wevill T, Deletic A. (2009) Plant traits that enhance pollutant removal from stormwater in biofiltration systems. Int. J. Phytoremediation, 12: 34-53. DOI: https://doi.org/10.1080/15226510902767114

Kim MH, Sung CY, Li MC (2012). Bioretention for stormwater quality improvement in Texas: removal effectiveness of Escherichia coli. Sep. Purif. Technol., 84: 120-124. DOI: https://doi.org/10.1016/j.seppur.2011.04.025

Li YL, Deletice A, Alcazar L, Bratieres K, Fletcher TD, McCarthy DT. (2012) Removal of Clostridium perfringens, Escherichia coli and F-RNA coliphages by stormwater biofilters. Ecol. Eng., 49: 137-145. DOI: https://doi.org/10.1016/j.ecoleng.2012.08.007

Hermawan AA, Chang JW, Pasbakhsh P, Hart F, Talei A. (2018) Halloysite nanotubes as a fine grained material for heavy metal ions removal in tropical biofiltration systems. Appl. Clay Sci., 160: 106-115. DOI: https://doi.org/10.1016/j.clay.2017.12.051

Sileshi R, Pitt R, Clark S. (2016) Prediction of flow rates through various stormwater biofilter media mixtures. Proceedings of the World Environmental and Water Resources Congress, West Palm Beach. DOI: https://doi.org/10.1061/9780784479889.021