Conference proceedings

Displaying 1 - 3 of 3 in Environmental data analysis and modelling (remove filter), machine (remove filter)

CEST Proceedings are published under the ISSN 2944-9820.

A data-driven approach to predict phytoplankton blooms using satellite-derived water quality and hydrometeorological drivers

(Corresponding) Kandris K., Romas E., Tzimas A., Bresciani M., Giardino C., Bauer P., Pechlivanidis I., Dessena M.

Topic:

Environmental data analysis and modelling

The present work leverages simulated hydrometeorological factors and satellite-derived chlorophyll-a to predict phytoplankton dynamics for Mulargia reservoir (Sardinia, Italy). A Random Forest (RF) model was (a) calibrated to minimize out-of-bag errors of chlorophyll-a predictions for a 5-year-long...Read more

Keywords:

Machine learning; forecasting; phytoplankton blooms; remote sensing; hydrometeorological predictions

DOI:

https://doi.org/10.30955/gnc2021.00420

Conference:

CEST2021

Paper ID:

cest2021_00420

Nonlinear Autoregressive Neural Networks for Air Temperature forecasting

Philippopoulos K., (Corresponding) Tzanis C., Deligiorgi D., Alimissis A.

Topic:

Environmental data analysis and modelling

In the field of climatic conditions forecasting, the linear classical time series models are inadequate for modelling and predicting accurately the air temperature variability. This work presents the novel application of nonlinear autoregressive neural networks (NAR) in air temperature forecasting...Read more

Keywords:

Air temperature, Machine Learning, Artificial Neural Networks, Dynamic Neural Networks, Forecasting

DOI:

https://doi.org/10.30955/gnc2021.00485

Conference:

CEST2021

Paper ID:

cest2021_00485

A Machine Learning Approach for the prediction of solid fuels consumption in Turkey

Celik N., (Corresponding) Konyalioglu A.

Topic:

Environmental data analysis and modelling

Solid fuels are very crucial energy sources as most of industries use them for obtaining heat, electricity and light. Furthermore, since solid fuels are scarce sources in Turkey, it is very important to forecast the consumption in order to effectively manage the energy policies and to conduct an...Read more

Keywords:

Solid Fuels, Environmental Data Analysis, Machine Learning

DOI:

https://doi.org/10.30955/gnc2023.00118

Conference:

CEST2023

Paper ID:

cest2023_00118