Conference proceedings

Displaying 1 - 5 of 5 in Environmental data analysis and modelling (remove filter), learning (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

A Deep Learning Model, interpreted with an XAI technique, to simulate and optimize the remediation of oil-drilling cuttings in bubble flow reactors

Kalari K., Christodoulis K., Bali N., Theodoropoulou M., (Corresponding) Tsakiroglou C.

Topic:

Environmental data analysis and modelling

A multitask deep neural network (DNN) is developed to simulate the ozonation of oil-drilling cuttings (ODC) and is interpreted through a technique of explainable artificial intelligence (XAI) to provide knowledge about the experimental conditions that will maximize the decontamination of ODC. On a...Read more

Keywords:

Ozonation, Soil Remediation, Bubble Column Reactor, Deep Neural Networks, Multitask Learning, Explainable Artificial Intelligence.

DOI:

https://doi.org/10.30955/gnc2023.00166

Conference:

CEST2023

Paper ID:

cest2023_00166

Predicting Land Cover Map Changes in the Philippines for use in LULC-based Carbon Capture Monitoring using Deep Learning

Camaclang R., Ballesteros F., (Corresponding) Labao A.

Topic:

Environmental data analysis and modelling

In the area of Carbon Capture and Storage (CSS), monitoring plays an important role not only to determine if there is any anomalies in the release of CO2 in the atmosphere, but also to prepare for disasters and to plan better future developments in the industrial sector, transportation sector, real...Read more

Keywords:

Land Use and Land Cover (LULC), Land Cover Prediction, Carbon Capture and Storage, Deep Learning

DOI:

https://doi.org/10.30955/gnc2023.00171

Conference:

CEST2023

Paper ID:

cest2023_00171