Underwater Noise Estimation with General Regression Neural Network
Résumé
With growing exploration and utilization of the ocean by human beings, sound pollution increases accordingly and significantly. Hence, the impact of noise on marine organisms has become one of the most important research topics. In order to assess the impact of these activities on marine fauna, and especially on marine mammals, underwater noise propagation should be estimated. It is in this context that the proposed work is situated. In fact, the aim of this project is to study how neural network techniques can speed up these computations for estimating underwater noise propagation. For this, experimental data from practical investigations/experiments were used to train the GRNN for estimating noise level caused by each boat. The predicted values using GRNN closely followed the experimental ones with a root mean square error scores (RMSE) that is equal to 0.7. Results showed that the GRNN model had good prediction results during the testing process in terms of both RMSE scores and training times. To evaluate the model accuracy, the propagation distance from the source in the horizontal plane was set at 150 km. At this distance, the predicted loss of acoustic energy resulted in noise levels which were comparable to the reference values. Several parameters was used in order to build an accurate model. These parameters include: the propagation distance, the depth, the transmission frequency, the kinematic bathymetry, and the sediment nature. The proposed model therefore estimated the noise level until 150 km from the noise source with high accuracy and high speed computation without complex procedures. Moreover, the use of GRNN made it possible to avoid remaking the expensive computations for each sub-zone: it estimated the noise levels with a very reduced time compared to the state-of-the art methods such as RAM. Underwater noise prediction GRNN neural network function approximation
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