###### Abstract  
This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering-learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring.  
adaptive integrated filters; deep learning; noise cancellation; UWSNs; monitor aquatic life 20242461022024610224610224610224610224610224610224610224610224610224610224610224610224610224610224610224610224610224610224610246102461024610246102461024  
required. Because of the rapid growth of communication and information equipment and technologies in recent years, data traffic movement over networks has increased. Current simple network control systems, however, are considered to be incapable of dealing with such a massive rise in traffic. Deep learning, which has made significant advances in research, is a technology that enables network managers to achieve new intelligent traffic control. Deep learning has shown effectiveness in several fields of information science, including image recognition, speech recognition, robotics, self-driving, and natural language processing. Additionally, extensive training in network traffic monitoring has begun, and additional progress is predicted [2].  
Underwater wireless sensor networks (UWSNs) are gaining popularity as a means of monitoring and managing aquatic life. They are, nevertheless, among the most failure-prone branches of Wireless Sensor Networks. UWSNs confront numerous obstacles, including limited memory and bandwidth, high propagation, route loss at higher levels, and high noise volume. Another component influencing acoustic channel performance is underwater noise. As a result, deep learning technology is utilized in conjunction with computational intelligence (CI) techniques to identify undiscovered resources in the water. The capacity of a system to accomplish a certain task, such as data integrity or pure/free-noise and intact experimental monitoring below the sea, is referred to as CI. Currently, data are seen as the essence of all entities in nature, encompassing humans, robots, and devices, such as those inside the Internet of Things (IoT). As a result, the obtained data must be correct, complete, and meet the needs of a certain assignment [3].