Research on Optimizing Coastal Water Quality Index Assessment using Machine Learning Algorithms

The assessment of coastal water quality is critical for the well-being of organisms within coastal ecosystems. This study presents a pioneering Adam-CNN model tailored for the detection of soft faults in analog circuits. By harnessing the potent feature extraction capabilities of the CNN model and the adaptive learning facilitated by the Adam gradient descent algorithm, the model is trained on peak values of the frequency domain response and the extremities of the transient time domain response. Experimental validation using a Sallen-Key bandpass filter circuit showcases an impressive 99.3% fault detection accuracy, demonstrating superior performance in comparison to existing research endeavors. This novel approach holds promise in advancing fault detection methodologies in analog electronic circuits, contributing to enhanced reliability and functionality in electronic devices crucial to coastal ecosystems.