HE-DFNETS: A Novel Hybrid Deep Learning Architecture for the Prediction of Potential Fishing Zone Areas in Indian Ocean Using Remote Sensing Images

Abstract

The Indian subcontinent is known for its larger coastline spanning, over 8100 km and is considered the habitat for many millions of people. The livelihood of their habitat is purely dependent upon the fishing activities. Often, the search for fish requires more time for catching and more resources, thus increasing the operational cost leading to low profitability. With the advent of artificial intelligence algorithms, designing intelligent algorithms for an effective prediction of fishing areas has reached new heights in terms of high accuracy (Acy) and less time. But still, predicting the location of potential fishing zones (PFZs) is always a daunting task. To reduce these issues, this work presented the novel hybrid prediction architecture of PFZs using remote sensing images. The proposed architecture integrates the deep convolutional layers and flitter bat optimized long short-term memory (FB-LSTM)-based recurrent neural networks (RNN). These convolutional layers are utilized to remove the various color features such as chlorophyll, sea surface temperature (SST), and GPS location from the satellite images, and FB-LTSM is utilized to predict the potential locations for fishing. The extensive experimentations are carried out utilizing the satellite data from Indian National Centre for Ocean Information Services (INCOIS) and implemented using TensorFlow 1.18 with Keras API. The performance metrics such as prediction Acy, precision (Pscn), recall (Rcl) or sensitivity (Sty), specificity (Sfy), and F1-score and compared with other existing intelligent learning models. From our observations, the proposed architecture (99% prediction Acy) has outperformed the other existing algorithms and finds its best place in designing an intelligent system for better predicting of PFZs.

A review on features and methods of potential fishing zone

“…By using the root-mean-square error and mean absolute error values, the suggested model's performance is then compared to that of a traditional artificial neural network, showing that it can outperform the previous other [72]. Sivasankari et al [73] realized the importance of having a better fishing community by developing a prediction model of the potential fishing zone using remote sensing images of chlorophyll, SST, and GPS location to predict the potential locations for fishing. Long short-term memory (FB-LSTM)-based recurrent neural networks and deep convolutional layers (RNN) are what the suggested architecture is made up of [73].…”

“…Sivasankari et al [73] realized the importance of having a better fishing community by developing a prediction model of the potential fishing zone using remote sensing images of chlorophyll, SST, and GPS location to predict the potential locations for fishing. Long short-term memory (FB-LSTM)-based recurrent neural networks and deep convolutional layers (RNN) are what the suggested architecture is made up of [73].…”

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

“…At the same time, a set of shrimp farm distribution management system based on the BP algorithm was established, which can monitor the trajectory of fishing vessels and the distribution of shrimp population in real time. Sivasankari et al [73] proposed a hybrid prediction architecture for potential fishing zones based on remote sensing images, which integrated deep convolution layer and RNN based on flitter bat-optimized long short-term memory (FB-LSTM). These convolutional layers were used to remove various color features such as chlorophyll, sea surface temperature (SST) and GPS location from satellite images, and FB-LTSM was used to predict potential fishing locations.…”