Prediction of Blue Water Footprint Accounting for Water Treatment Plants in Kuantan River Basin

  • Authors

    • Syazwan N. Moni
    • Edriyana A. Aziz
    • M. A. Malek
    • Nadiah Mokhtar
    • Amirul A. Borhan
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.35.22748
  • Water Supply, Water Resources Management, Water Footprint, Backpropagation Method

  • Abstract

    Water treatment plants (WTPs) in Kuantan river basin abstracts water from the blue water source, which is the Kuantan river. Therefore, by accounting the blue water footprint (WFb), the overall water consumption for all five WTPs  namely; Sungai Lembing, Bukit Sagu, Panching, Semambu, and Bukit Ubi can be obtained. In order to predict the value, Backpropagation method is the best method to be used due to the historical data obtained from the WFb accounting for all five WTPs above. The objective of this study is to predict the overall blue water consumption for water treatment plants located along Kuantan river basin using Backpropagation method in artificial neural network. In this study, WFb has been accounted throughout all water treatment plants by using reference from water footprint manual. Then, the WFb will undergo a series of testing using application in MATLAB software in order to predict the future value based on historical data from 2015 until 2016. As a result, the total WFb accounting obtained was 190,543,378.2 m3/day, while the total maximum capacity of the WTPs was 189,654,000 m3/day. Hence, the prediction value that kept increasing will not be able to cater the future demand due to unstoppable urbanization.

  • References

    1. [1] H. Yuk Feng, A. Shin Ying, L. Khia Min, and L. Teang Shui, “Quality of Water Resources in Malaysia,†in Research and Practices in Water Quality, 2015, pp. 65–94.

      [2] M. Arroita et al., “Water abstraction impacts stream ecosystem functioning via wetted-channel contraction,†Freshw. Biol., Nov. 2016.

      [3] A. Y. Hoekstra, A. K. Chapagain, and G. Zhang, “Water Footprints and Sustainable Water Allocation,†no. December 2015, pp. 1–6, 2016.

      [4] J. Morelli, “Environmental Sustainability: A Definition for Environmental Professionals,†J. Environ. Sustain., vol. 1, no. 1, pp. 1–27, 2011.

      [5] I. Haddeland et al., “Global water resources affected by human interventions and climate change.,†Proc. Natl. Acad. Sci. U. S. A., vol. 111, no. 9, pp. 3251–6, 2014.

      [6] Y. Zhu, Z. Lin, J. Wang, Y. Zhao, and F. He, “Impacts of Climate Changes on Water Resources in Yellow River Basin, China,†Procedia Eng., vol. 154, pp. 687–695, 2016.

      [7] Y. Cai, W. Yue, L. Xu, Z. Yang, and Q. Rong, “Sustainable urban water resources management considering life-cycle environmental impacts of water utilization under uncertainty,†Resour. Conserv. Recycl., vol. 108, pp. 21–40, 2016.

      [8] H. Biemans et al., “Future water resources for food production in five South Asian river basins and potential for adaptation - A modeling study,†Sci. Total Environ., vol. 468–469, pp. S117–S131, 2013.

      [9] F. Ahmed, C. Siwar, and R. A. Begum, “Water resources in Malaysia : Issues and challenges,†vol. 12, no. April, 2014.

      [10] A. Y. Hoekstra, A. K. Chapagain, M. M. Aldaya, and M. M. Mekonnen, The Water Footprint Assessment Manual. 2011.

      [11] S. Morera, L. Corominas, M. Poch, M. M. Aldaya, and J. Comas, “Water footprint assessment in wastewater treatment plants,†J. Clean. Prod., vol. 112, pp. 4741–4748, 2016.

      [12] S. A. Munro, G. C. G. Fraser, J. D. Snowball, and M. Pahlow, “Water footprint assessment of citrus production in South Africa : A case study of the Lower Sundays River Valley,†J. Clean. Prod., vol. 135, pp. 668–678, 2016.

      [13] G. Pellegrini, C. Ingrao, S. Camposeo, C. Tricase, and F. Cont, “Application of water footprint to olive growing systems in the Apulia region : a comparative assessment,†vol. 112, 2016.

      [14] Y. Gu et al., “Calculation of water footprint of the iron and steel industry : a case study in Eastern China,†vol. 92, pp. 274–281, 2015.

      [15] C. I. Rodriguez, V. A. R. De Galarreta, and E. E. Kruse, “Analysis of water footprint of potato production in the pampean region of Argentina,†J. Clean. Prod., vol. 90, pp. 91–96, 2015.

      [16] T. Okadera, J. Chontanawat, and S. H. Gheewala, “Water footprint for energy production and supply in Thailand,†Energy, vol. 77, pp. 49–56, 2014.

      [17] S. A. Ene, C. Teodosiu, B. Robu, and I. Volf, “Water footprint assessment in the winemaking industry: A case study for a Romanian medium size production plant,†J. Clean. Prod., vol. 43, pp. 122–135, 2013.

      [18] A. Ayres, “Germany ’ s water footprint of transport fuels,†vol. 113, pp. 1746–1751, 2014.

      [19] D. Chico, M. M. Aldaya, and A. Garrido, “A water footprint assessment of a pair of jeans : the in fl uence of agricultural policies on the sustainability of consumer products,†J. Clean. Prod., vol. 57, pp. 238–248, 2013.

      [20] P. Chooyok, N. Pumijumnog, and A. Ussawarujikulchai, “The Water Footprint Assessment of Ethanol Production from Molasses in Kanchanaburi and Supanburi Province of Thailand .,†APCBEE Procedia, vol. 5, pp. 283–287, 2013.

      [21] R. P. Marano and R. A. Filippi, “Water Footprint in paddy rice systems . Its determination in the provinces of Santa Fe and Entre Ríos , Argentina,†Ecol. Indic., vol. 56, pp. 229–236, 2015.

      [22] A. K. Chapagain and A. Y. Hoekstra, “The water footprint of coffee and tea consumption in the Netherlands,†vol. 4, 2007.

      [23] M. M. Mekonnen and A. Y. Hoekstra, “A Global Assessment of the Water Footprint of Farm Animal Products,†Ecosystems, vol. 15, no. 3, pp. 401–415, 2012.

      [24] A. Y. Hoekstra and M. M. Mekonnen, “The water footprint of humanity,†vol. 109, no. 9, 2012.

      [25] I. Cazcarro, A. Y. Hoekstra, and J. Sánchez Chóliz, “The water footprint of tourism in Spain,†Tour. Manag., vol. 40, pp. 90–101, 2014.

      [26] B. G. Ridoutt, P. Juliano, P. Sanguansri, and J. Sellahewa, “The water footprint of food waste : case study of fresh mango in Australia,†J. Clean. Prod., vol. 18, no. 16–17, pp. 1714–1721, 2010.

      [27] W. Gerbens-leenes, A. Y. Hoekstra, and T. H. Van Der Meer, “The water footprint of bioenergy,†vol. 2009, no. 23, 2009.

      [28] M. M. Mekonnen and A. Y. Hoekstra, “The green , blue and grey water footprint of crops and derived crop products,†pp. 1577–1600, 2011.

      [29] J. S. Li and G. Q. Chen, “Water footprint assessment for service sector: A case study of gaming industry in water scarce Macao,†Ecol. Indic., vol. 47, pp. 164–170, 2014.

      [30] D. Silverman and J. A. Dracup, “Long Range Precipitation Prediction in California: A Look Inside The ‘Black Box’ Of A Trained Network,†in Artificial Neural Networks in Hydrology, 2000, p. 311.

      [31] A. Najah, A. El-Shafie, O. A. Karim, and A. H. El-Shafie, “Application of artificial neural networks for water quality prediction,†Neural Comput. Appl., vol. 22, no. SUPPL.1, pp. 187–201, 2013.

      [32] M. Heydari, E. Olyaie, and H. Mohebzadeh, “Development of a Neural Network Technique for Prediction of Water Quality Parameters in the Delaware River , Pennsylvania,†Middle-East J. Sci. Res., vol. 13, no. 10, pp. 1367–1376, 2013.

      [33] A. K. Yadav and S. S. Chandel, “Solar radiation prediction using Artificial Neural Network techniques: A review,†Renew. Sustain. Energy Rev., vol. 33, pp. 772–781, 2014.

      [34] J. Veintimilla-Reyes, F. Cisneros, and P. Vanegas, “Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River,†Procedia Eng., vol. 162, pp. 153–161, 2016.

      [35] M. J. Alizadeh and M. R. Kavianpour, “Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean,†Mar. Pollut. Bull., vol. 98, no. 1–2, pp. 171–178, 2015.

      [36] F. Gümrah, B. Öz, B. Güler, and S. Evin, “The Application of Artificial Neural Networks for the Prediction of Water Quality of Polluted Aquifer,†Water Air Soil Pollut., vol. 119, no. 1, pp. 275–294, 2000.

      [37] J. Ma, F. Xu, K. Huang, and R. Huang, “Improvement on the linear and nonlinear auto-regressive model for predicting the NOx emission of diesel engine,†Neurocomputing, vol. 207, pp. 150–164, 2016.

      [38] M. Krishnan, B. Bhowmik, B. Hazra, and V. Pakrashi, “Real time damage detection using recursive principal components and time varying auto-regressive modeling,†Mech. Syst. Signal Process., vol. 101, pp. 549–574, 2018.

      [39] E. Vamsidhar, K. Varma, P. Rao, and R. Satapati, “Prediction of rainfall using backpropagation neural network model,†Int. J. Comput. Sci. Eng., vol. 2, no. 4, pp. 1119–1121, 2010.

      [40] M. Pahlow, J. Snowball, and G. Fraser, “Water footprint assessment to inform water management and policy making in South Africa,†vol. 41, no. 3, pp. 300–313, 2015.

      [41] A. Y. Hoekstra, “A critique on the water-scarcity weighted water footprint in LCA,†Ecol. Indic., vol. 66, pp. 564–573, Jul. 2016.

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  • How to Cite

    N. Moni, S., A. Aziz, E., A. Malek, M., Mokhtar, N., & Borhan, A. A. (2018). Prediction of Blue Water Footprint Accounting for Water Treatment Plants in Kuantan River Basin. International Journal of Engineering & Technology, 7(4.35), 286-293. https://doi.org/10.14419/ijet.v7i4.35.22748

    Received date: 2018-12-01

    Accepted date: 2018-12-01

    Published date: 2018-11-30