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Groundwater is a vital resource that provides drinking water to over half of the world's population. However, groundwater contamination has become a serious issue due to human activities such as industrialization, agriculture, and improper waste disposal. The impacts of groundwater contamination can be severe, including health risks, environmental damage, and economic losses. A list of unknown groundwater contamination sources has been developed for the Wang-Tien landfill using a groundwater modeling system (GMS). Further, AI-based models have been developed which accurately predict the contamination from the sources at this site. A serious complication with most previous studies using artificial neural networks (ANN) for contamination source identification has been the large size of the neural networks. We have designed the ANN models which use three different ways of presenting inputs that are categorized by hierarchical K-means clustering. Such an implementation reduces the overall complexity of the model along with high accuracy. The predictive capability of developed models was assessed using performance indices and compared with the ANN models. The results show that the hybrid model of hierarchical K-means clustering and ANN model (HCA-ANN) is a highly accurate model for identifying pollution sources in contaminated water.
Czasopismo
Rocznik
Tom
Strony
105--122
Opis fizyczny
BIbliogr. 25 poz., rys., tab.
Twórcy
autor
- Department of Civil Engineering, Madhav Institute of Technology and Science, Gwalior 474011, India
autor
- Department of Electronics Engineering, Madhav Institute of Technology and Science, Gwalior 474011, India
autor
- Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India
autor
- Civil Engineering Department, Kamala Institute of Technology and Science. Singapur, Karimnagar, Telangana, India
autor
- Department of Mechatronics Engineering, KCG College of Technology, Karapakkam Chennai 600097, Tamil Nadu, India
autor
- Department of Civil Engineering, KCG College of Technology, Karapakkam, Chennai 600097, India
autor
- QIS College of Engineering and Technology, Vengumukpalem, Ongole-523272, AP, India
Bibliografia
- [1] MICHALEC B., CUPAK A., Assessment of quality of water and sediments in small reservoirs in Southern Poland. A case study, Environ. Eng. Res., 2022, 27 (2), 200660. DOI: 10.4491/eer.2020.660.
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- [3] KABSCH-KORBUTOWICZ M., KUTYLOWSKA M., The possibilities of modelling the membrane separation processes using artificial neural networks, Environ. Prot. Eng., 2008, 34 (1), 15–36.
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- [7] ABABOU R., BAGTZOGLOU A.C., MALLET A., Anti-diffusion and source identification with the RAW scheme: A particle-based censored random walk, J. Environ. Fluid Mech., 2010, 10 (1–2), 41–76. DOI: 10.1007 /s10652-009-9153-4.
- [8] GORELICK S.M., EVANS B., REMSON I., Identifying sources of groundwater pollution. An optimization approach, Water Res. Res., 1983, 19, 779–790. DOI: 10.1029/WR019i003p00779.
- [9] BAGTZOGLOU A.C., HOSSAIN F., Radial basis function neural network for hydrologic inversion: An appraisal with classical and spatiotemporal geostatistical techniques in the context of site character-ization, Stoch. Environ. Res. Risk Assess., 2009, 23 (7), 933–945. DOI: 10.1007/s00477-008-0262-2.
- [10] SRIVASTAVA D., SINGH R.M., Breakthrough curves characterization and identification of an unknown pollution source in groundwater system using an artificial neural network (ANN), Environ. Forens., 2014, 15 (2), 175–189. DOI: 10.1080/15275922.2014.890142.
- [11] SUN A.Y., A robust geostatistical approach to contaminant source identification, Water Res. Res., 2007, 43, 1–12. DOI: 10.1029/2006WR005106.
- [12] MAHINTHAKUMAR G., SAYEED M., Hybrid genetic algorithm. Local search methods for solving groundwa-ter source identification inverse problems, J. Water Res. Plan. Manage., 2005, 131 (1), 45–57. DOI: 10.1061 /(ASCE)0733-9496 (2005)131:1(45).
- [13] YEH H.D., CHANG T.H., LIN Y.C., Groundwater contaminant source identification by a hybrid heuris-tic approach, Water Res. Res., 2007, 43 (9), W09420. DOI: 10.1029/2005WR004731.
- [14] ZHOU S., WU Y., NI Z., ZHOU X., WEN H., ZOU Y., Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients, 2016, arXiv preprint arXiv:1606.06160.
- [15] NAYAK P.C., RAO Y.S., SUDHEER K.P., Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Res. Manage., 2006, 20, 77–90. DOI: 10.1007/s11269-006-4007-z.
- [16] BEHZAD M., ASGHARI K., COPPOLA Jr. E.A., Comparative study of SVMs and ANNs in aquifer water level prediction, J. Comp. Civ. Eng., 2010, 24 (5), 408–413. DOI: 10.1061/ (ASCE)CP.1943-5487. 0000043.
- [17] JALALKAMALI A., SEDGHI H., MANSHOURI M., Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran, J. Hydroinf., 2011, 13 (4), 867–876. DOI: 10.2166 / hydro.2010.034.
- [18] LEE J.Y., YI M.J., YOO Y.K., AHN K.H., KIM G.B.,WON J.H., A review of the national groundwater monitoring network in Korea, Hydrol. Proc., 2007, 21 (7), 907–919. DOI: 10.1002/hyp.6282.
- [19] PING W.L., PHUAN A.T.L., JIAN X., Hierarchical fast learning artificial neural network: progressive learning in high dimensional spaces, 2004, International Report, https://neuro.bstu.by/ai/To-dom /My_research/failed%201%20subitem/For-research/D-mining/Iris-kmean/InternalReport2004.pdf
- [20] SHEIKHOLESLAMI R., RAZAVI S., Progressive Latin hypercube sampling. An efficient approach for ro-bust sampling-based analysis of environmental models, Environ. Model. Softw., 2017, 93, 109–126. DOI: 10.1016/j.envsoft.2017.03.010.
- [21] RINK K., KALBACHER T., KOLDITZ O., Visual data exploration for hydrological analysis, Environ, Earth Sci., 2012, 65, 1395–1403. DOI: 10.1007/s12665-011-1230-6.
- [22] SINGH P., SINGH R.M., Identification of pollution sources using artificial neural network (ANN) and multilevel breakthrough curve (BTC) characterization, Environ. Forens., 2019, 20 (3), 219–227. DOI: 10.1080/15275922.2019.1629548.
- [23] BREDEHOEFT J.D., PINDER G.F., Mass transport in flowing groundwater, Water Res. Res., 1973, 9 (1), 194–210. DOI: 10.1029/WR009i001p00194.
- [24] CHEN, C.S., TU C.H., CHEN S.J., CHEN C.C., Simulation of groundwater contaminant transport at a de-commissioned landfill site. Case study, Tainan City, Taiwan, Int. J. Environ. Res. Public Health, 2016, 13 (5), 467. DOI: 10.3390/ijerph13050467.
- [25] LI S., SARI Y.A., KUMRAL M., Optimization of mining. Mineral processing integration using unsupervised machine learning algorithms, Nat. Res. Res., 2020, 29, 3035–3046. DOI: 10.1007/s11053-020-09628-0.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1f1e8bee-fdc1-4a70-8816-d8eeb0413449
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