An IoT-based Water Quality Monitoring System using a Hybrid 1D-CNN–BiLSTM Model with Attention Mechanism

Abstract

Water quality monitoring is very important in ensuring public safety and sustainability of aquatic ecosystems. However, traditional approaches used in monitoring water quality are not suitable for monitoring purposes due to lack of capability of continuously assessing water quality and rapidly detecting pollution. To solve this problem, this research presents an intelligent IoT-based framework for monitoring the water quality that is integrated with a hybrid deep learning model with 1D Convolutional Neural Networks, BiLSTM, and an attention mechanism. This framework uses parameters such as pH, turbidity, temperature, DO, BOD, and heavy metals for the classification of the degree of pollution of water. In order to overcome class imbalance problem, the SMOTE approach was applied, resulting in a balanced dataset consisting of 2,745 samples. Modern developments in sensing technology, wireless communication, and embedded computing have contributed to an enhanced adoption of IoT applications in the environmental sphere. IoT-based environmental monitoring solutions combine several sensors to gather the physicochemical parameters including pH, turbidity, temperature, dissolved oxygen (DO), biological oxygen demand (BOD), and heavy metals concentration