• Maureen Nettie Linan
  • Bobby Gerardo
  • Ruji Medina


Clustering algorithm, DRASTIC, Groundwater assessment, Self-organizing map, Small island.


Assessment of groundwater vulnerability to contamination plays a vital role in the utilization and protection of groundwater resource. In this study, a vulnerability map for Boracay Island, Philippines was developed using a modified self-organizing map algorithm to determine groundwater vulnerability in light of massive tourism developments in the island. Self-organizing map using the Nguyen-Widrow initialization algorithm was used to cluster DRASTIC data which were pre-processed using data cleaning normalization schemes. The vulnerability map developed showed that groundwater resource in the island is susceptible to contamination as confirmed by groundwater quality analysis. The result of the study demonstrates the effectiveness of the improved SOM algorithm as a tool for assessment of groundwater vulnerability and is comparable with the traditional DRASTIC method. The developed methodology allows grouping of datasets into clusters that represent the level of vulnerability to contamination of the groundwater. Further, this approach can be applied to other islands to ensure the balance between tourism developments and ecological integrity of the scarce groundwater resource.


S. Stevenazzi, M. Masetti, and G. P. Beretta, “Groundwater vulnerability assessment: from overlay methods to statistical methods in the Lombardy Plain area,” Acque Sotterranee – Italian Journal of Groundwater, vol. 6, no. 2, pp. 17-27, Jun. 2017.

A.V. Deshpande and S.N. Patil, “Assessment of groundwater quality by using statistical analysis from kopargaon taluka, Ahmednagar, India,” International Journal of Advanced Geosciences, vol. 4, no. 2, pp. 15–20, Jun. 2016.

A. Z. Abiy, A. M. Melesse, Y. M. Behabtu, B. Abebe, Groundwater Vulnerability Analysis of the Tana Subasin: An application of DRASTIC Index Method, in: A. Melesse, W. Abtew (Eds.), Landscape Dynamics, Soils and Hydrological Processes in Varied Climates, Springer Geography, Springer, Cham, 2016, pp. 435-461.

C. Jaseela, K. Prabhakar, and P. S. P. Harikumar, “Application of GIS and DRASTIC Modeling for Evaluation of Groundwater Vulnerability near a Solid Waste Disposal Site,” International Journal of Geosciences, vol. 7, no. 4, pp. 558–571, 2016.

T. O. Abdullah, S. S. Ali, N. A. Al-Ansari, and S. Knutsson, “Groundwater Vulnerability Using DRASTIC and COP Models: Case Study of Halabja Saidsadiq Basin, Iraq,” Engineering, vol. 8, no. 11, p. 741-760, Jan. 2016.

S. Javadi, N. Kavehkar, K. Mohammadi, A. Khodadadi, and R. Kahawita, “Calibrating DRASTIC using field measurements, sensitivity analysis and statistical methods to assess groundwater vulnerability,” Water International, vol. 36, no. 6, pp. 719–732, Oct. 2011.

S. Saidi, S. Bouri, H. Ben Dhia, and B. Anselme, “Assessment of groundwater risk using intrinsic vulnerability and hazard mapping: Application to Souassi aquifer, Tunisian Sahel,” Agricultural Water Management, vol. 98, no. 10, pp. 1671–1682, Aug. 2011.

S. F. Tavassol and G. S. Gopalakrishna, “Pb contamination and analysis of Aquifer in Karaj Plain, Alborz Province, Iran using GIS-based DRASTIC Model,” Bulletin of Environment, Phramacology and Life Sciences, vol. 3 (Spl issue II), pp. 263-271, 2014.

R. Barzegar, A. A. Moghaddam, and H. Baghban, “A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran,” Stochastic Environmental Research and Risk Assessment, vol. 30, no. 3, pp. 883–899, Mar. 2016.

M. A. Baghapour et al., “Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran,” Journal of Environmental Health Science and Engineering, vol. 14, no. 13, pp. 1-16, Aug. 2016.

S. Javadi, S. M. Hashemy, K. Mohammadi, K. W. F. Howard, and A. Neshat, “Classification of aquifer vulnerability using K-means cluster analysis,” Journal of Hydrology, vol. 549, pp. 27–37, Jun. 2017.

A. A. Nadiri, M. Gharekhani, R. Khatibi, and A. A. Moghaddam, “Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models,” Environmental Science and Pollution Research, vol. 24, no. 9, pp. 8562–8577, Mar. 2017.

H. Zou, Z. Zou, and X. Wang, “An Enhanced K-Means Algorithm for Water Quality Analysis of the Haihe River in China,” International Journal of Environmental Research and Public Health, vol. 12, pp. 14400–14413, Nov. 2015.

T. Kohonen, “Essentials of the self-organizing map,” Neural Networks, vol. 37, pp. 52–65, Jan. 2013.

T. T. Nguyen, A. Kawamura, T. N. Tong, N. Nakagawa, H. Amaguchi, and R. Gilbuena, “Clustering spatio–seasonal hydrogeochemical data using self-organizing maps for groundwater quality assessment in the Red River Delta, Vietnam,” Journal of Hydrology, vol. 522, pp. 661–673, Mar. 2015.

K. Nakagawa, H. Amano, A. Kawamura, and R. Berndtsson, “Classification of groundwater chemistry in Shimabara, using self-organizing maps,” Hydrology Research, vol. 48, no. 3, pp. 840–850, Jun. 2017.

L. Belkhiri, L. Mouni, A. Tiri, T. S. Narany, and R. Nouibet, “Spatial analysis of groundwater quality using self-organizing maps,” Groundwater for Sustainable Development, vol. 7, pp. 121–132, Sep. 2018.

F. Rezaei, M. R. Ahmadzadeh, and H. R. Safavi, “SOM-DRASTIC: using self-organizing map for evaluating groundwater potential to pollution,” Stochastic Environmental Research and Risk Assessment, vol. 31, no. 8, pp. 1941–1956, Oct. 2017.

E. Z. Moattar, M.-R. Feizi-Derakhshi, and M. Bakhshi, “Improvement of Self-Organizing Maps Algorithm with Weighting Optimization,” International Journal of Advances in Electronics and Computer Science, vol. 3, no. 4, p. 74-76, Apr. 2016.

A. A. Akinduko, E. M. Mirkes, and A. N. Gorban, “SOM: Stochastic initialization versus principal components,” Information Sciences, vol. 364–365, pp. 213–221, Oct. 2016.

D. Jude Hemanth, J. Anitha, Computational Intelligence Techniques for Pattern Recognition in Biomedical Image Processing Applications, in: S. Kulkarni (Ed.), Machine Learning for Problem Solving in Computational Applications: Intelligent Techniques, Information Science Reference (imprint of IGI Global), USA, 2012, pp. 195-209

S. Aisyah, M. Harahap, A. M. Husein Siregar, and M. Turnip, “Optimization of training backpropagation algorithm using nguyen widrow for angina ludwig diagnosis,” Journal of Physics: Conference Series, vol. 1007, 012050, 2018. [Online]. Available:

E. L. Linan, V. B. Ella, and L. M. Florece, “GIS-Based Assessment of Groundwater Vulnerability to Contamination in Boracay Island Using DRASTIC Model,” Journal of Environmental Science and Management, vol. 16, no. 2, pp. 19-27, Dec. 2013.

“Population of Region VI - Western Visayas (Based on the 2015 Census of Population) | Philippine Statistics Authority.” [Online]. Available at: [Accessed: 24-Apr-2018].

M. N. N. Linan, B. D. Gerardo, and R. P. Medina, “Improving self-organizing map with nguyen-widrow initialization algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 1, pp. 535–542, Jul. 2019.

A. Nawafleh, M. Awawdeh, and E. Salameh, “Assessment of Groundwater Vulnerability to Contamination in Irbid Governorate, North Jordan,” DIRASAT: Pure Sciences, vol. 38 no. 2, pp. 122-133, Jul. 2011.




How to Cite

Linan, M. N., Gerardo, B., & Medina, R. (2020). SELF-ORGANIZING MAP WITH NGUYEN-WIDROW INITIALIZATION ALGORITHM FOR GROUNDWATER VULNERABILITY ASSESSMENT. International Journal of Computing, 19(1), 63-69. Retrieved from