Deep learning and machine learning classification technique for integrated forecasting

Abstract

Smart fisheries are increasingly using artificial intelligence (AI) technologies to increase their sustainability. The potential fishing zone (PFZ) forecasts several fish aggregation zones throughout the duration of the prediction in any sea. The autoregressive integrated moving average (ARIMA) and random forest model are used in the current study to provide a technique for locating viable fishing zones in deep marine seas. A significant amount of data was gathered for the database's creation, including monitoring information for Indian fishing fleets from 2017 to 2019. Using expert label datasets for validation, it was discovered that the model's detection accuracy was 98%. Our method uses salinity and dissolved oxygen, two crucial markers of water quality, to identify suitable fishing zones for the first time. In the current research, a system was created to identify and map the quantity of fishing activity. The tests use a number of parameter measurements to evaluate the contrast-enhanced computed tomography (CECT) approach to machine learning (ML) and deep learning (DL) methodologies. The findings showed that the CECT had a 94% accuracy rate compared to a convolutional neural network's 92% accuracy rate for the 80% training data and 20% testing data.