Integration of Information Technology in a Compact Neural Network Model for Real-Time Monitoring of Seagrass

Atyaf Sami Noori^1,*

¹University of Information Technology and Communications, Baghdad, Iraq

Abstract

Monitoring seagrass ecosystems offers critical insights into water quality, which is essential for maintaining aquatic biodiversity. Real-time monitoring, however, is hindered by various challenges, including coral reef degradation, habitat deterioration, fishing impacts, seagrass dredging risks, and complex coastal management issues. To overcome these barriers, this study presents an improved neural network model enhanced by Information Technology (IT) and Artificial Intelligence Neural Networks (AINN). Specifically, a recurrent neural network (RNN) has been utilized to address fishing pressures and habitat issues by evaluating sediment stability within seagrass areas. Additionally, a modular neural network (MNN), leveraging IT support, effectively analyzed coral reef deterioration to promote ecological sustainability. A convolutional neural network (CNN) was further implemented to enhance risk assessment and facilitate optimal seagrass growth conditions, thus improving real-time monitoring accuracy. Results indicated that this integrated IT-based neural network significantly surpassed traditional CNN methods, achieving superior performance in seagrass monitoring and coastal ecosystem management.

Keywords: Seagrass; Ocean; Neural network; AI; Information technology