Deep Learning Models for Water Potability Classification in Rural Areas in the Philippines

Abstract

According to the World Bank, one out of five Filipinos do not get water from formal sources. Only 77% of the rural population and 90% of those in urban areas have access to an improved water source and only 44% have direct house connections. Surveillance of water quality is mandatory thus many research studies have been presented to different communities that showed effective results. In rural areas, there is already a classification model for water potability using traditional machine learning techniques. However, there currently no deep learning-based model for water potability classification. Thus, this work aims to create a deep learning water potability classification model for rural water sources in the Philippines. It starts from importing the water potability dataset of water monitoring sources from rural areas then pre-processing of the data, evaluation of the performance of the learning models through accuracy, precision, recall and f-measure metrics. Out of all the three, MLP had provided the greatest accuracy of 99.80%. LSTM performed better in accuracy and recall in comparison to GRU, but GRU had provided better precision than LSTM. LSTM has been considered to greatly classify the most common classifications in the dataset, while GRU has been observed to accurately classify the infrequent classifications in the dataset.