170
Views
25
CrossRef citations to date
0
Altmetric
Articles

Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries

, , , , &
Pages 12227-12236 | Received 05 Feb 2015, Accepted 04 May 2015, Published online: 17 Jun 2015

References

  • P. Bagla, J. Kaiser, India’s spreading health crisis draws global arsenic experts, Science 274 (1996) 174–175.10.1126/science.274.5285.174
  • American Water Works Association (AWWA), Arsenic Rule, Mainstream, 45, 2001.
  • M. Berg, S. Luzi, P.T. Kim, P.H. Viet, W. Giger, D. Stuben, Arsenic removal from groundwater by household sand filters: Comparative field study, model calculations, and health benefits, Environ. Sci. Technol. 40 (2006) 5567–5573.10.1021/es060144z
  • B.D. Kocar, C. Fendorf, Thermodynamic constraints on reductive reactions influencing the biogeochemical of Arsenic in soils and sediments, Environ. Sci. Technol. 43 (2009) 4871–4877.10.1021/es8035384
  • S. Sthiannopkao, K.W. Kim, K.H. Cho, K. Wantala, S. Sotham, C. Sokuntheara, J.H. Kim, Arsenic levels in human hair, Kandal Province, Cambodia: The influences of groundwater arsenic, consumption period, age and gender, Appl. Geochem. 25 (2010) 81–90.10.1016/j.apgeochem.2009.10.003
  • M. Berg, H.C. Tran, T.C. Nguyen, H.V. Pham, R. Schertenleib, W. Giger, Arsenic contamination of groundwater and drinking water in Vietnam: A human health threat, Environ. Sci. Technol. 35 (2001) 2621–2626.10.1021/es010027y
  • M. Berg, H.C. Tran, T.C. Nguyen, H.V. Pham, R. Schertenleib, W. Giger, Magnitude of arsenic pollution in the Mekong and Red River Deltas—Cambodia and Vietnam, Sci. Total Environ. 372 (2001) 413–425.
  • P.L. Smedley, D.G. Kinniburgh, A review of the source, behaviour and distribution of arsenic in natural waters, Appl. Geochem. 17 (2002) 517–568.10.1016/S0883-2927(02)00018-5
  • G. Sun, J. Liu, T.V. Luong, D. Sun, L. Wang, Endemic Arsenicosis: A Clinical Diagnostic with Photo Illustrations, UNICEF East Asia and Pacific Regional Office, Bangkok, 2002.
  • D.A. Polya, A.G. Gault, N.J. Bourne, P.R. Lythgoe, D.A. Cooke, Coupled HPLC-ICP-MS analysis indicates highly hazardous concentrations of dissolved arsenic species in Cambodian groundwaters, in: J. Holland, S.D. Tanner (Eds.), Plasma Source Mass Spectrometry: Applications and Emerging Technologies, Royal Society of Chemistry, Cambridge, 2003, pp. 127–140.
  • D.A. Polya, A.G. Gault, N. Diebe, P. Feldman, J.W. Rosenboom, E. Gilligan, D. Fredericks, A.H. Milton, M. Sampson, H.A.L. Rowland, P.R. Lythgoe, C. Middleton, D.A. Cooke, Arsenic hazard in shallow Cambodian groundwaters, Mineral Mag. 69 (2005) 807–823.10.1180/0026461056950290
  • G. Stanger, T.V. Truong, K.S.L.T. My Ngoc, T.V. Luyen, T.T. Tuyen, Arsenic in groundwaters of the Lower Mekong, Environ. Geochem. Health 27 (2005) 341−357.
  • A. Kohnhorst, Arsenic in groundwater in selected countries in South and Southeast Asia: A review, J. Tropical Medicine Parasitology 28 (2005) 73–82.
  • A. Tetsuro, K. Takashi, F. Junko, K. Reiji, B.M. Tu, T.K.T. Pham, I. Hisato, S. Annamalai, H.V. Pham, T. Shinsuke, Contamination by arsenic and other trace elements in tube-well water and its risk assessment to humans in Hanoi, Vietnam, Environ. Pollut. 139 (2006) 95–106.
  • H. Chiew, M.L. Sampson, S. Huch, S. Ken, B.C. Bostick, Effect of groundwater iron and phosphate on the efficacy of arsenic removal by iron-amended biosand filters, Environ. Sci. Technol. 43 (2009) 6295–6300.10.1021/es803444t
  • N.K.C. Twarakavi, D. Mishra, S. Bandopadhyay, Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data, Nat. Resour. Res. 15 (2006) 15–26.10.1007/s11053-006-9013-6
  • M. Norgaard, Neural Network Based System Identification Toolbox, Version 2, Department of Automation, Technical Report 00-E-891, Technical University of Denmark, Lungby, 2000.
  • B. Widrow, D.E. Rumelhart, M.A. Lehr, Neural networks: Applications in industry, business and science, Commun. ACM 37 (1994) 93–105.10.1145/175247.175257
  • H.R. Maier, G.C. Dandy, The use of artificial neural networks for the prediction of water quality parameters, Water Resour. Res. 32 (1996) 1013–1022.10.1029/96WR03529
  • C.G. Wen, C.S. Lee, A neural network approach to multiobjective optimization for water quality management in a river basin, Water Res. 34 (1998) 427–436.10.1029/97WR02943
  • G.M. Brion, S. Lingireddy, A neural network approach to identifying non-point sources of microbial contamination, Water Res. 33 (1999) 3099–3106.10.1016/S0043-1354(99)00025-1
  • J.H. Lee, M.J. Yu, K.W. Bang, J.S. Choe, Evaluation of the methods for first flush analysis in urban watersheds, Water Sci. Technol. 48 (2003) 167–176.
  • S. Riad, J. Mania, L. Bouchaou, Y. Najjar, Rainfall-runoff model using an artificial neural network approach, Math. Comput. Model. 40 (2004) 839–846.10.1016/j.mcm.2004.10.012
  • A. Sarangi, A.K. Bhattacharya, Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India, Agr. Water Manage. 78 (2005) 195–208.10.1016/j.agwat.2005.02.001
  • G. Tayfur, D. Swiatek, A. Wita, V.P. Singh, Case study: Finite element method and artificial neural network models for flow through Jeziorsko earthfill dam in Poland, J. Hydraul. Eng. 131 (2005) 431–440.10.1061/(ASCE)0733-9429(2005)131:6(431)
  • M. Holmberg, M. Forsius, M. Starr, M. Huttunen, An application of artificial neural networks to carbon, nitrogen and phosphorus concentrations in three boreal streams and impacts of climate change, Ecol. Model. 195 (2006) 51–60.10.1016/j.ecolmodel.2005.11.009
  • J.T. Kuo, P.H. Hsieh, W.S. Jou, Lake eutrophication management modeling using dynamic programming, J. Environ. Manage. 88 (2008) 677–687.10.1016/j.jenvman.2007.03.027
  • B. Purkait, S.S. Kadam, S.K. Das, Application of artificial neural network model to study arsenic contamination in groundwater of Malda Disrict, Eastern India, J. Environ. Inf. 12 (2008) 140–149.10.3808/jei.200800132
  • F.J. Chang, L.S. Kao, Y.M. Kuo, C.W. Liu, Artificial neural networks for estimating regional arsenic concentrations in a blackfoot disease area in Taiwan, J. Hydrol. 388 (2010) 65–76.10.1016/j.jhydrol.2010.04.029
  • K.H. Cho, S. Sthiannopkao, Y.A. Pachepsky, K.W. Kim, J.H. Kim, Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network, Water Res. 45 (2011) 5535–5544.
  • C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn. 20 (1995) 273–297.
  • V.N. Vapnik, S. Golowich, A.J. Smola, Support vector method for function approximation, regression estimation, and signal processiong, Adv. Neural Inf. Process. Syst. 9 (1997) 281–287.
  • V. Vapnik, Statistical Learning Theory, Wiley, New York, NY, 1998.
  • R.S. Govindaraju, Artificial neural networks in hydrology. II: Hydrologic applications, J. Hydrol. Eng. 5 (2000) 124–137.
  • B. Schölkopf, A.J. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 2002.
  • S. Osowski, K. Garanty, Forecasting of the daily meteorological pollution using wavelets and support vector machine, Eng. Appl. Artif. Intell. 20 (2007) 745–755.10.1016/j.engappai.2006.10.008
  • H.R. Maier, G.C. Dandy, The effect of internal parameters and geometry on the performance of back-propagation neural networks: An empirical study, Environ. Model. Softw. 13 (1998) 193–209.10.1016/S1364-8152(98)00020-6
  • G. Bebis, M. Georgiopoulos, Feed-forward neural networks, IEEE Potentials 13 (1994) 27–31.10.1109/45.329294
  • V. Cherkassky, Y. Ma, Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks 17 (2004) 113–126.10.1016/S0893-6080(03)00169-2
  • Y. Ren, G. Bai, Determination of optimal SVM parameters by using GA/PSO, J. Comput. 5 (2010) 1160–1168.
  • H.R. Maier, G.C. Dandy, Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications, Environ. Model. Softw. 15 (2000) 101–124.10.1016/S1364-8152(99)00007-9
  • R. Patuelli, A. Reggiani, P. Nijkamp, N. Schanne, Neural networks for regional employment forecasts: Are the parameters relevant? J. Geog. Sci. 13 (2011) 67–85.10.1007/s10109-010-0133-5
  • W. Wang, Z. Xu, W. Lu, X. Zhang, Determination of the spread parameter in the Gaussian kernel for classification and regression, Neurocomputing 55 (2003) 643–663.10.1016/S0925-2312(02)00632-X
  • M. Berg, C. Stengel, P.T.K. Trang, P. Hung Viet, M.L. Sampson, M. Leng, S. Samreth, D. Fredericks, Magnitude of arsenic pollution in the Mekong and Red River Deltas—Cambodia and Vietnam, Sci. Total Environ. 372 (2007) 413–425.10.1016/j.scitotenv.2006.09.010
  • J. Buschmann, M. Berg, C. Stengel, M.L. Sampson, Arsenic and manganese contamination of drinking water resources in Cambodia: Coincidence of risk areas with low relief topography, Environ. Sci. Technol. 41 (2007) 2146–2152.10.1021/es062056k
  • R.M. Lewis, T.A. Virginia, Globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds, SIAM J. Optimiz. 12 (2002) 1075–1089.10.1137/S1052623498339727
  • J.E. Nash, J.V. Sutcliffe, River flow forecasting through conceptual models part I—A discussion of principles, J. Hydrol. 10 (1970) 282–290.10.1016/0022-1694(70)90255-6
  • D.N. Moriasi, J.G. Arnold, M.W. Van Liew, R.L. Bingner, R.D. Harmel, T.L. Veith, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, T. ASABE 50 (2007) 885–900.10.13031/2013.23153
  • H.A.L. Rowland, A.G. Gault, P. Lythgoe, D.A. Polya, Geochemistry of aquifersediments and arsenic-rich groundwaters from Kandal Province, Cambodia, Appl. Geochem. 23 (2008) 3029–3046.10.1016/j.apgeochem.2008.06.011
  • M.L. Polizzotto, B.D. Kocar, S.G. Benner, M. Sampson, S. Fendorf, Near-surface wetland sediments as a source of arsenic release to ground water in Asia, Nature 454 (2008) 505–508.10.1038/nature07093
  • P. Chanpiwat, S. Sthiannopkao, K.H. Cho, K.W. Kim, V. San, B. Suvanthong, C. Vongthavady, Contamination by arsenic and other trace elements of tube-well water along the Mekong River in Lao RDR, Environ. Pollut. 159 (2011) 567–576.10.1016/j.envpol.2010.10.007
  • L. Winkel, M. Berg, M. Amini, S.J. Hug, C.A. Johnson, Predicting groundwater arsenic contamination in Southeast Asia from surface parameters, Nat. Geosci. 1 (2008) 536–542.10.1038/ngeo254

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.