Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning
2018
Abstract. In this study, high-resolution bathymetric multibeam and optical image data,
both obtained within the Belgian manganese (Mn) nodule mining license area by
the autonomous underwater vehicle (AUV) Abyss , were combined in
order to create a predictive random
forests (RF) machine learning model. AUV bathymetry reveals small-scale
terrain variations, allowing slope estimations and calculation of bathymetric
derivatives such as slope, curvature, and ruggedness. Optical AUV imagery
provides quantitative information regarding the distribution (number and
median size) of Mn nodules. Within the area considered in this study, Mn
nodules show a heterogeneous and spatially clustered pattern, and their
number per square meter is negatively correlated with their median size. A
prediction of the number of Mn nodules was achieved by combining information
derived from the acoustic and optical data using a RF model. This model was
tuned by examining the influence of the training set size, the number of
growing trees ( ntree ), and the number of predictor variables to be
randomly selected at each node ( mtry ) on the RF prediction accuracy. The use of larger training
data sets with higher ntree and mtry values increases the
accuracy. To estimate the Mn-nodule abundance, these predictions were linked
to ground-truth data acquired by box coring. Linking optical and
hydroacoustic data revealed a nonlinear relationship between the Mn-nodule
distribution and topographic characteristics. This highlights the importance
of a detailed terrain reconstruction for a predictive modeling of Mn-nodule
abundance. In addition, this study underlines the necessity of a sufficient
spatial distribution of the optical data to provide reliable modeling input
for the RF.
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