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<span lang="EN-US">The quality of cluster result and the learning speed of Self-organizing map (SOM) are dependent on the initialization of weights since the initial values for weight vectors affect the performance of SOM training when applied to clustering. In this paper, improvement of SOM was achieved with the application of the Nguyen-Widrow algorithm to initialize weights. Nguyen-Widrow initialization algorithm is a method for initialization of the weights of neural networks to speed up the training process. Performance of the modified SOM was determined in terms of cluster error rate and the number of iterations to achieve convergence using different datasets and results show that the modified SOM algorithm produces better cluster results and improved training speed compared to traditional SOM.</span>

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.

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