Open AccessPrediction and classification of suspended sediment and zooplankton signals from acoustic Doppler current profiler backscatter data using artificial neural networks
Author(s) -
Angga Dwinovantyo,
Steven Solikin,
Henry M. Manik,
Tri Prartono,
Susilohadi
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/944/1/012014
Subject(s) - acoustic doppler current profiler , zooplankton , artificial neural network , sediment , feature selection , doppler effect , backpropagation , sonar , classifier (uml) , geology , pattern recognition (psychology) , computer science , artificial intelligence , current (fluid) , oceanography , paleontology , physics , astronomy
Characterization of each underwater object has its challenges, especially for small objects. The process of quantifying acoustic signals for these small objects can be done using high-frequency hydroacoustic instruments such as an acoustic Doppler current profiler (ADCP) combined with the artificial intelligence (AI) technique. This paper presents an artificial neural network (ANN) methodology for classifying an object from acoustic and environmental data in the water column. In particular, the methodology was tuned for the recognition of suspended sediments and zooplankton. Suspended sediment concentration and zooplankton abundance, which extracted from ADCP acoustic data, were used as input in the backpropagation method along with other environmental data such as effects of tides, currents, and vertical velocity. The classifier used an optimal number of neurons in the hidden layer and a feature selection based on a genetic algorithm. The ANN method was also used to estimate the suspended sediment concentration in the future. This study provided new implications for predicting and classifying suspended sediment and zooplankton using the ADCP instrument. The proposed methodology allowed us to identify the objects with an accuracy of more than 95%.