Estimation of mean grain size of seafloor sediments using neural network

De, Chanchal; Chakraborty, Bhaskar

doi:10.1007/s11001-011-9145-x

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…On the other hand, for the other sediment group (50-100 µm), ABS values were used as a single input, and the highest R 2 (0.917) and lowest RMSE (0.521) values were obtained for the MLP among all ANN models ( Table 2). De and Chakraborty (2012) concluded that estimating the mean grain size using an acoustic inversion algorithm is computing-intensive, but this value could be estimated using an ANN-based approach in a much shorter computing Meral et …”

Section: Ann Modelsmentioning

confidence: 99%

Analyses of Turbidity and Acoustic Backscatter Signal With Artificial Neural Network for Estimation of Suspended Sediment Concentration

Meral¹

2018

Appl. Ecol. Env. Res.

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Abstract. The commonly used sampling method is restrictive for the spatial and temporal measurement of suspended sediment and requires intensive labor. These limitations and technological advances have led to methods based on sound or light scattering in water. In this study, the turbidity and acoustic backscattering signal (ABS) values were used with the aim of improving these methods with different artificial neural network (ANN) models; Multilayer Perceptron (MLP), Radial Basis Neural networks (RBNN) and General Regression Neural Network (GRNN). Measurements were taken in a vertical sediment tower for two different sediment sizes (< 50 µm and 50-100 µm) and concentrations (0.0-6.0 g L -1 ). In the results of the regression analyses, turbidity values had strong relationships with sediment concentration for both sediment size groups (R 2 = 0.937 and 0.967). Although the ABS values had a reasonable R 2 value (0.873) for the 50-100 µm group, the < 50 µm group did not produce a significant R 2 value with regression analyses. The remarkable differences were not observed among MLP, RBNN and GRNN model for this sediment size group, and the reasonable R 2 and RMSE results were not produced with any ANN model that had a single ABS input for the < 50 µm sediment group. On the other hand, for the other sediment group (50-100 µm), ABS values were used as a single input, and the highest R 2 (0.917) value was obtained with MLP model and it was improved with the turbidity input (up to R 2 = 0.999). The results show that the ANN model could be considered as an alternative method because it was applied successfully to estimate suspended sediment concentration using with turbidity and ABS under different particle size conditions.

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Section: Ann Modelsmentioning

confidence: 99%

Analyses of Turbidity and Acoustic Backscatter Signal With Artificial Neural Network for Estimation of Suspended Sediment Concentration

Meral¹

2018

Appl. Ecol. Env. Res.

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“…In their recent research, De and Chakraborty (2010, 2011, 2012 discussed the estimation of mean grain sizes of mixed seabed sediments using dual-frequency single beam data and neural network, but they did not use multi-beam echo sounder BS data to do seabed mixed sediment classification.…”

Section: Introductionmentioning

confidence: 99%

Seabed Mixed Sediment Classification with Multi-beam Echo Sounder Backscatter Data in Jiaozhou Bay

Tang

Lei

et al. 2014

Marine Georesources & Geotechnology

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The multi-beam echo sounder system can not only obtain high-precision seabed bathymetry data, but also obtain high-resolution seabed backscatter strength data. A number of studies have applied acoustic remote sensing method to classify seabed sediment type with multi-beam backscatter strength data, and obtained better classification results than the traditional sediment sampling method. However, these studies mainly focus on the single type sediment classification or seabed mixed sediment classification using single beam data, not multi-beam echo sounder data. Based on backscatter strength data by a high-frequency (300 kHz) Simrad EM3000 multi-beam echo sounder and seabed sediment sampling data of Jiaozhou Bay in Qingdao, China, we establish the relation model between seabed backscatter strength and sediment type characteristics after data processing and corrections. The purpose of data processing is to diminish or weaken the influence of local bottom slope and near nadir reflection on backscatter strength data. Processed backscatter strength data through corrections and compensations can better characterize the features of the seabed sediments. Applying the back propagation neural network method based on genetic algorithm, we present a fast and accurate seabed classification method in this paper which could identify not just a single type of sediment-like rock, sand but also mixed types of sediment like sand gravel, clayey silt and sand-silt-clay.

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