Modeling the Efficacy of Geopolymer Mosquito Repellent Strips Leachate Distribution Using Meta-heuristic Optimization

  • D. K. D. B. RupiniEmail author
  • T. Vamsi Nagaraju
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)


Many mosquito repellents were available in the markets in various forms such as coils, plug-in repellents, papers, creams, and other repellent imparted synthetics and fibers are in vogue. Moreover, all the aforementioned repellents were used in in-doors, applying for human bodies and some of them are imparted in clothes and accessories. However, one should give prime importance to control mosquitoes at their breeding stage itself in stagnant waters or drain waters. In this context, geopolymer soils imparted with mosquito repellent was developed to eradicate mosquitoes at their breeding stage itself. This paper presents the leachate distribution assessment of VR-geo mosquito repellent strip using swarm-assisted multi-linear regression. A model equation has been developed for the prediction of leachate distribution in terms of pH using input parameters like volume of the geopolymer repellent strip, molarity of NaOH, Na2SiO3/NaOH ratio, and alkali-activator content.


Geopolymerization Leachate assessment VR-geo mosquito repellent PSO 


  1. 1.
    Angelina, M.L., Melissa, A.P., Thomas, S., Nakul, C.: Mathematical modeling of mosquito dispersal in a heterogeneous environment. Math. Biosci. 241, 198–216 (2012)Google Scholar
  2. 2.
    Khare, A., Rangnekar, S.: A review of particle swarm optimization and its applications in solar photovoltaic system. Appl. Soft Comput. 13, 2997–3006 (2013)CrossRefGoogle Scholar
  3. 3.
    Davidovits, J.: Properties of geopolymer cements. In: First international conference on alkaline cements and concretes (1994)Google Scholar
  4. 4.
    Dmitri, Y.B., Leonid, L.M., Paul, J.L., James, R.T., Peter, J.S.S., William, R.H.: Insitu analysis of pH gradients in mosquito larvae using non-invasive, self-referencing, pH sensitive microelectrodes. J. Exp. Biol. 204, 691–699 (2001)Google Scholar
  5. 5.
  6. 6.
    Irish, S.: Effects of different pH levels on the viability, metamorphosis rate and morphology of aedes mosquitoes. In: Central visayas health research and innovation conference, Talamban, Cebu City (2016)Google Scholar
  7. 7.
    James, K., Russell, E.: Particle swarm optimization. IEEE 0-7803-2768-3/95, pp. 1942–1948 (1995)Google Scholar
  8. 8.
    Lee, K.Y., Chung, N., Hwang, S.: Application of an artificial neural networks (ANN) model for predicting mosquito abundances in urban areas. Ecol. Inform. 36, 172–180 (2015)CrossRefGoogle Scholar
  9. 9.
    Linus, F., Helge, K., Antje, K., Gunter, A.S., Doreen, W., Ralf, W.: Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations. Ecol. Model. 388, 136–144 (2018)CrossRefGoogle Scholar
  10. 10.
    Pelizza, S.A., Lopez, L.C.C., Becnel, J.J., Bisaro, V., Garcia, J.J.: Effects of temperature, pH and salinity on the infection of leptolegnia chapmanii Seymour (Peronosporomycetes) in mosquito larvae. J. Invertebr. Pathol. 96(2), 133–137 (2007)CrossRefGoogle Scholar
  11. 11.
    USDA/Agricultural Research Service.: Computer model for finding mosquito repellent compounds. ScienceDaily, 12 June 2008Google Scholar
  12. 12.
    Nagaraju, V.T.: Potential of geopolymer technology towards ground improvement. In: 2nd International conference on Advances in concrete, structural and geotechnical engineering. BITS Pilani, Rajasthan (2018)Google Scholar
  13. 13.
    Jain, V.K., Kumar, S.: Effective surveillance and predictive mapping of mosquito-borne diseases using social media. J. Comput. Sci. 25, 406–415 (2017)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.S. R. K. R. Engineering CollegeBhimavaramIndia

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