JETI

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Abstract

Clean water detection is the process of detecting the presence of clean water in an environment. Conventional approach to clean water detection is time consuming and prone to errors, this led to the development of automatic clean water detection systems. This research employed convolutional neural network for clean water detection. The dataset for training the model was obtained from Kaggle and pre-processed by removing duplicate rows and filling missing data. The developed model was evaluated using accuracy, precision, recall and F1-score and results show that the model performed well with an accuracy of 95%. The result obtained was compared to Logistic regression and results show that the developed CNN model performed better with up to 0.15 accuracy. This research therefore concluded that CNN performed better than logistic regression for clean water detection using Kaggle dataset. However, future research should acquire local dataset to make the work indigenous.

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