Sediment recognition by warp tension monitoring of bottom otter trawling and applying the self-organizing map algorithm

Abstract Model towing experiments of a bottom trawl net with hyper-lift trawl door were conducted to investigate the effect of the bottom sediment (concrete, sand, gravel, and rock) on the warp tension of the overall trawl system. The towing speed was from 50 cm/s to 70 cm/s and the ratio of warp length relative to the water depth was within the range of 4–6. Through the signal analysis of time-series warp tension, results reveal that there is a significant dependence of the warp tension on the type of bottom sediment, and the oscillation of warp tension in a frequency range of 1–10 Hz increases in the order of concrete, sand, gravel, and rock. Based on these characterizations, the time-series warp tension is thus represented by the feature vector for the input data of the self-organizing map (SOM) and learning vector quantization (LVQ) neural networks. A clustering method with an unsupervised SOM neural network acting as an updating tool for the bottom sediment database was successfully built using the validation of the prepared sediments. In combination with the output vector of labeled bottom sediment, the supervised LVQ neural network for sediment recognition performed excellently with a high classification accuracy of over 80%.