A Machine Learning Model for Estimating Shipping noise in India Ocean Region

Commercial Ships are the major source of underwater radiated noise which is generated because of the interaction between the hull and water and propeller cavitation which lies in the low frequency range [1]. Underwater radiated management is an interesting research area. This URN management is important due to some of these reasons, the first is the ship design and manufacturing for efficient operation & maintenance can be perform, the second is related to requirement of acoustic stealth for naval platforms in order to avoid detection by enemy sonars and mines [2], the third is related to degradation of ‘acoustic vision’ of underwater species like marine mammals [10]. Many Underwater species are known to use sound waves for multiple biologically critical functions such as navigation, communication, survival (through the avoidance of predators) [3]. Their perception of the underwater environment is through acoustic signals, it called acoustic vision that is seriously degrades because of URN and ambient noise [9].

This issue is now getting recognized by the authorities like the International Whaling Commission (IWC), International Union for Conservation of Nature (IUCN), International Maritime Organization (IMO) for establishing and monitoring rules and regulations [1][4]. Some Mathematical models are present by D. Ross, RANDI, Wales-Heitmeyer, SONIC and Wittekind for estimating shipping radiated noise. But all these models have some drawbacks [2]. Because of that we need an ML based approach to estimating the shipping noise. URN management study has broadly covered three main aspects, the first is the measurement & analysis that needs some effective and efficient hardware and software [3], the second is the prediction of URN based on available inputs for varied design and operational conditions [5], the third is the deception where we fake the actual signature of platform [2].

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    The conventional approach of each of the stakeholders pursuing their own UDA* efforts has serious limitations given the highly resource intensive field experimental research initiative required for a long period. Click here for more