Enhancing Water Quality Detection for Drinking and Irrigation Using Convolutional Neural Networks

Abstract Water quality is essential for maintaining the health of ecosystems and the overall quality of life. It is crucial to monitor water quality for effective water resource management, as high-quality water suitable for domestic, drinking, irrigation, and industrial use is not always readily available. Polluted water has serious repercussions, leading to harmful environmental, human health, and infrastructure conditions. According to a United Nations (UN) report, 1.5 million people die each year due to diseases caused by polluted water. A recent study introduced a one-dimensional convolutional neural network (1D-CNN) approach for predicting water quality specifically for irrigation and drinking purposes. The prediction of water quality relies on physicochemical parameters. This study used two datasets—one for drinking water and the other for irrigation water—each with distinct features. The results show that the proposed model achieved 97.19% accuracy for irrigation and 100% accuracy for drinking water, demonstrating the model's effectiveness in accurately categorizing water samples suitable for drinking and providing robust support for decision-making processes related to potable water and irrigation.