Smart Water Quality Prediction Using Atom Search Optimization with Fuzzy Deep Convolutional Network

Abstract

Smart solutions for monitoring water pollution are becoming increasingly prominent nowadays with the advance in the Internet of Things (IoT), sensors, and communication technologies. IoT enables connections among different devices with the capability to gather and exchange information. Additionally, IoT extends its ability to address environmental issues along with the automation industry. As water is essential for human survival, it is necessary to integrate some mechanisms for monitoring water quality. Water quality monitoring (WQM) is an efficient and cost-effective system intended to monitor the quality of drinking water that exploits IoT techniques. Therefore, this study developed a new smart water quality prediction using atom search optimization with the fuzzy deep convolution network (WQP-ASOFDCN) technique in the IoT environment. The WQP-ASOFDCN technique seamlessly monitors the water quality parameters using IoT devices for data collection purposes. Data pre-processing is carried out at the initial stage to make the input data compatible for further processing. For water quality prediction, the F-DCN model was utilized in this study. Furthermore, the prediction performance of the F-DCN approach was improved by using the ASO algorithm for the optimal hyperparameter tuning process. A sequence of simulations was applied to validate the enhanced water quality prediction outcomes of the WQP-ASOFDCN method. The experimental values denote the better performance of the WQP-ASOFDCN approach over other approaches in terms of different measures.