Financial derivative features based integrated potential fishing zone (IPFZ) Future forecast

In India, around 7 million people depend on fishing for their livelihoods. They are assisted with a reliable and fast brief forecast the areas of fish aggregations. Habitat mapping is critical in supporting strategic choices usage protection. conjunction techniques machine learning, remote control has made comprehensive relevant scales possible. supervised algorithms utilized to make forecasts from datasets, when data accessible without relating output factors. this research work, Ocean Surface Temperature (OST) Satellite derived Chlorophyl material basic inputs generating information Potential Fishing Zone (PFZ). The 16 features additional financial derivative used accurate future prediction PFZ. unwanted missing removed using effective pre-processing techniques. Among various methods available forecasting nonlinear phenomena, Neural Network best efficient method get forecast. Therefore, Function Fitting (FFNN) technique mainly predicting Integrated (IPFZ). practical analyses performed by analysing 80% -20%, 60% -40% terms parameters. From results, it proved that suggested FFNN achieved 90% accuracy, where existing neural network 86% accuracy implementing -20% dataset.