I. Gazis

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Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

I. Gazis, T. Schoening, Evangelos Alevizos, 2018, Biogeosciences.

Interactive comment on “Quantitative mapping and predictive modelling of Mn-nodules’ distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning” by Iason-Zois Gazis et al

I. Gazis, 2018 .

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

I. Gazis, T. Schoening, Evangelos Alevizos, 2018 .

Searching for potential marine sand resources to mitigate beach erosion in island settings

I. Gazis, M. Stamatakis, A. Velegrakis, 2020 .

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean

R. Koch, I. Gazis, T. Schoening, 2022, Scientific Reports.

Impact of a long-lived anticyclonic mesoscale eddy on seawater anomalies in the northeastern tropical Pacific Ocean: a composite analysis from hydrographic measurements, sea level altimetry data, and reanalysis model products

M. Walter, I. Gazis, M. Haeckel, 2022, Ocean Science.

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean

I. Gazis, T. Schoening, J. Greinert, 2022, Scientific Reports.

Monitoring of Anthropogenic Sediment Plumes in the Clarion-Clipperton Zone, NE Equatorial Pacific Ocean

M. Baeye, L. Thomsen, I. Gazis, 2022, Frontiers in Marine Science.

Importance of Spatial Autocorrelation in Machine Learning Modeling of Polymetallic Nodules, Model Uncertainty and Transferability at Local Scale

I. Gazis, J. Greinert, 2021, Minerals.

An Interpretable Multi-Model Machine Learning Approach for Spatial Mapping of Deep-Sea Polymetallic Nodule Occurrences

I. Gazis, Francois Charlet, Jens Greinert, 2024, Natural Resources Research.