An IoT based smart water quality assessment framework for aqua-ponds management using Dilated Spatial-temporal Convolution Neural Network (DSTCNN)

Abstract

Assuring the quality of water is crucial for the growth and survival of fish in aquaculture ponds. Traditional methods of water quality monitoring can be inefficient which makes real-time monitoring and decision is a challenging one. Some deep learning techniques have shown apparent in improving water quality monitoring and assessment process, but encounter some limitations like data-overfitting, interpretability, and finds difficulties in capturing complex spatial and temporal dynamics that have hindered their effectiveness. To overcome these challenges, we propose an enhanced Dilated Spatial-temporal Convolution Neural Network (DSTCNN) for water quality monitoring in aquaculture, which uses an IoT system setup for capturing real-time data inputs from aqua ponds. The water quality data captured through the IoT sensors is labeled as per the water quality index (WQI) standards for analysis. This labeled data is effectively classified into two categories by the proposed DSTCNN model based on their suitability for fish growth or potential to cause fish mortality. By the leveraging power of dilated convolutions, the DSTCNN architecture accurately handles the intricacies of both spatial and temporal data, enabling it to capture essential features and patterns across multiple snapshots. This capability empowers the model to truly comprehend the complex relationships inherent in spatiotemporal data. Furthermore, to address the concerns like overfitting due to complexity of data and enhance generalization, the proposed model employs a hybrid activation function that synergistically combines ReLU and sigmoid during the activation process. The proposed DSTCNN model has been implemented on real-time and public datasets and obtained 99.28% and 99.02% accuracy respectively, whereas the state-of-the-art PCR-GB model obtains 96.97% and 97.11% accuracy on real-time and public datasets respectively.