Water Quality Prediction and Detection of the Vibrio Cholerae Bacteria

Camilo Enrique Rocha Calderón, Octavio José Salcedo Parra, Sebastián Camilo Vanegas Ayala

Abstract


This document shows the results for two water quality-related trials based on the Physico-chemical characteristics given by the used dataset; both trials were carried out based on the same dataset from which the membership sets, and functions were defined the most relevant features. The first trial was a neural network method aimed to predict water quality through attributes as the pH, temperature, turbidity, salinity, among others; the second trial was a fuzzy logic system method for the detection of the Vibrio Cholerae in the water through the usual variables associated to its presence: temperature, salinity, phosphates, and nitrites' levels. The method for this research is divided into two phases. The first phase is developing suitable software using an iterative and incremental process model based on prototypes. The second phase or operative phase has an experimental characterization that allows for an adequation of the environment to establish the main features and properties that are relevant to the study object. The results showed effectiveness values of 99.99% (highest obtained value) for trial one and 70.23% for trial two; such values depict an accurate prediction on the quality of water and a valuable detection for Cholera related bacteria in water supplies. This research developed two highly interpretable and transparent systems to people through the graphic of the correspondences between the rules established and the membership functions in the input and output sets.

Keywords


Fuzzy systems; neural networks; quality; Vibrio Cholerae; water.

Full Text:

PDF

References


J. Juntunen, P. Meriläinen, and A. Simola, “Public health and economic risk assessment of waterborne contaminants and pathogens in Finland,” Sci. Total Environ., vol. 599–600, pp. 873–882, Dec. 2017, doi: 10.1016/j.scitotenv.2017.05.007.

L. E. Armstrong and E. C. Johnson, “Water intake, water balance, and the elusive daily water requirement,” Nutrients, vol. 10, no. 12, Dec. 2018, doi: 10.3390/nu10121928.

C. Troeger et al., “Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016,” Lancet Infect. Dis., vol. 18, no. 11, pp. 1211–1228, Nov. 2018, doi: 10.1016/S1473-3099(18)30362-1.

M. Ali, A. R. Nelson, A. L. Lopez, and D. A. Sack, “Updated Global Burden of Cholera in Endemic Countries,” PLoS Negl. Trop. Dis., vol. 9, no. 6, p. e0003832, Jun. 2015, doi: 10.1371/journal.pntd.0003832.

A. Richterman, M. F. Franke, G. Constant, G. Jerome, R. Ternier, and L. C. Ivers, “Food insecurity and self-reported cholera in Haitian households: An analysis of the 2012 Demographic and Health Survey,” PLoS Negl. Trop. Dis., vol. 13, no. 1, p. e0007134, Jan. 2019, doi: 10.1371/journal.pntd.0007134.

S. Lonappan, R. Golecha, and G. Balakrish Nair, “Contrasts, contradictions and control of cholera,” Vaccine, vol. 38, pp. A4–A6, Feb. 2020, doi: 10.1016/j.vaccine.2019.08.022.

M. M. Foley, A. Ritchie, P. B. Shafroth, J. J. Duda, M. M. Beirne, and R. Paradis, “Water quality in the Elwha River estuary, Washington, from 2006 to 2014,” U.S. Geol. Surv. data release, vol. 8, no. 2, pp. 552–577, Aug. 2017, doi: 10.1002/ecm.1268.

U.S. Government Works, “Water quality in the Elwha River,” Feb. 2018. .

L. Li, S. Rong, R. Wang, and S. Yu, “Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review,” Chem. Eng. J., vol. 405, p. 126673, Feb. 2021, doi: 10.1016/j.cej.2020.126673.

J. G. Nayak, L. G. Patil, and V. K. Patki, “Development of water quality index for Godavari River (India) based on fuzzy inference system,” Groundw. Sustain. Dev., vol. 10, p. 100350, Apr. 2020, doi: 10.1016/j.gsd.2020.100350.

Y. Zhao, T. Li, X. Zhang, and C. Zhang, “Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future,” Renew. Sustain. Energy Rev., vol. 109, pp. 85–101, Jul. 2019, doi: 10.1016/j.rser.2019.04.021.

Y. Deng, H. Xiao, J. Xu, and H. Wang, “Prediction model of PSO-BP neural network on coliform amount in special food,” Saudi J. Biol. Sci., vol. 26, no. 6, pp. 1154–1160, Sep. 2019, doi: 10.1016/j.sjbs.2019.06.016.

I. Škrjanc, J. Iglesias, A. Sanchis, D. Leite, E. Lughofer, and F. Gomide, “Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey,” Inf. Sci. (Ny)., vol. 490, pp. 344–368, Jul. 2019, doi: 10.1016/j.ins.2019.03.060.

H. Yan, Y. Liu, X. Han, and Y. Shi, “An evaluation model of water quality based on DSA-ELM method,” in ICOCN 2017 - 16th International Conference on Optical Communications and Networks, Nov. 2017, vol. 2017-January, pp. 1–3, doi: 10.1109/ICOCN.2017.8121280.

K. Joslyn and J. Lipor, “A Supervised Learning Approach to Water Quality Parameter Prediction and Fault Detection,” in Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Jan. 2019, pp. 2511–2514, doi: 10.1109/BigData.2018.8622628.

P. Huang et al., “An integrated modelling system for water quality forecasting in an urban eutrophic estuary: The swan-canning estuary virtual observatory,” J. Mar. Syst., vol. 199, p. 103218, Nov. 2019, doi: 10.1016/j.jmarsys.2019.103218.

A. Delgado, A. Aguirre, E. Palomino, and G. Salazar, “Applying triangular whitenization weight functions to assess water quality of main affluents of Rimac river,” in Proceedings of the 2017 Electronic Congress, E-CON UNI 2017, Jun. 2017, vol. 2018-January, pp. 1–4, doi: 10.1109/ECON.2017.8247308.

N. S. K. Gunda, S. H. Gautam, and S. K. Mitra, “Artificial Intelligence Based Mobile Application for Water Quality Monitoring,” J. Electrochem. Soc., vol. 166, no. 9, pp. B3031–B3035, Mar. 2019, doi: 10.1149/2.0081909jes.

P. Wang et al., “Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants,” Sci. Total Environ., vol. 693, p. 133440, Nov. 2019, doi: 10.1016/j.scitotenv.2019.07.246.

J. Leo, E. Luhanga, and K. Michael, “Machine Learning Model for Imbalanced Cholera Dataset in Tanzania,” Sci. World J., vol. 2019, 2019, doi: 10.1155/2019/9397578.

A. S. Khalid Waleed, P. D. Kusuma, and C. Setianingsih, “Monitoring and classification system of river water pollution conditions with fuzzy logic,” in Proceedings - 2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2019, Jul. 2019, pp. 112–117, doi: 10.1109/ICIAICT.2019.8784857.

U. Shafi, R. Mumtaz, H. Anwar, A. M. Qamar, and H. Khurshid, “Surface Water Pollution Detection using Internet of Things,” in 2018 15th International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT, HONET-ICT 2018, Nov. 2018, pp. 92–96, doi: 10.1109/HONET.2018.8551341.

Y. Wang, J. Zhou, K. Chen, Y. Wang, and L. Liu, “Water quality prediction method based on LSTM neural network,” in Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, Jul. 2017, vol. 2018-January, pp. 1–5, doi: 10.1109/ISKE.2017.8258814.

R. S. Pressman, Ingenieria del Software. Un Enfoque Practico. 2010.

N. Gilbert and A. Rusli, “Single object detection to support requirements modeling using faster R-CNN,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 2, pp. 830–838, Apr. 2020, doi: 10.12928/TELKOMNIKA.V18I2.14838.

H. Espitia, J. Soriano, I. Machón, and H. López, “Design Methodology for the Implementation of Fuzzy Inference Systems Based on Boolean Relations,” Electronics, vol. 8, no. 11, p. 1243, Oct. 2019, doi: 10.3390/electronics8111243.




DOI: http://dx.doi.org/10.18517/ijaseit.11.6.13598

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development