IoT-Based Monitoring and Prediction of Water Quality Using Swin-Transformer and Depthwise Separable Convolutional Neural Network Optimized by Circulatory System-Based Optimization.

Real-time water quality monitoring is essential for environmental preservation and public health. Traditional laboratory-based testing methods are often slow and lack the capacity for continuous temporal prediction. To address these limitations, this project outlines an integrated framework combining Internet of Things (IoT) hardware with advanced deep learning architectures for real-time monitoring and predictive analysis of water quality parameters. The system utilizes physical sensors to collect critical indicators such as pH, turbidity, temperature, and dissolved oxygen. These data streams are processed using a hybrid model consisting of a Swin-Transformer and a Depthwise Separable Convolutional Neural Network (DS-CNN). The Swin-Transformer captures long-range temporal dependencies in the sensor time-series data, while the DS-CNN

extracts local spatial-temporal features with reduced computational overhead, making it suitable for edge-compatible deployments. To enhance prediction accuracy and model convergence, a Circulatory System-Based Optimization (CSBO) algorithm is employed to optimize the hyperparameters of the neural network. Implementation support focuses on sensor calibration, data acquisition pipelines, and model deployment strategies. This structured approach provides a robust methodology for continuous water quality assessment, enabling proactive environmental management through precise predictive modeling.

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