CEST Proceedings are published under the ISSN 2944-9820.
A comparison between the past and future expected wind conditions in the European coastal environment of the Mediterranean Sea
(Corresponding) RUSU E.
Topic:
Enviornmental data analysis and modelling
In the last years, exploitation of the wind power has been constantly increasing together with the size of the turbines. Furthermore, by 2030 wind energy is expected to supply around 30% of EU’s power demand. Offshore wind represents a significant future opportunity, since resources are abundant...Read more
Keywords:
Mediterranean Sea, wind power, 2050, RCP4.5, historical data, average and extreme wind conditions
The integration of three field survey datasets in Athens, Greece: transformation of five-point to seven-point thermal sensation scale
(Corresponding) Pantavou K., Lykoudis S., Delibasis K., Tseliou A., Koletsis I., Nikolopoulou M., Tsiros I.
Topic:
Enviornmental data analysis and modelling
The integration of the datasets from three different field surveys on thermal sensation conducted at eight different sites of the area of Athens, Greece was examined. All three surveys were carried out with similar methodologies so data integration can be considered meaningful. The surveys included...Read more
Keywords:
field surveys, thermal sensation, data integration, PET
Multivariate Analysis of Large µ-FTIR Data Sets in Search of Microplastics
(Corresponding) Wander L., Vianello A., Vollertsen J., Braun U., Paul A.
Topic:
Microplastics in the marine environment
µ-FTIR spectroscopy is a widely used technique in microplastics research. It allows to simultaneously characterize the material of the small particles, fibers or fragments, and to specify their size distribution and shape. Modern detectors offer the possibility to perform two-dimensional imaging of...Read more
Quantification of Soil Properties from Hyperspectral Data for Sustainable Agriculture Using Deep Learning
(Corresponding) Singh S., Kasana S.
Topic:
Agroforestry, forest and agricultural sustainability
The characterization of soil properties is critical for optimizing farming for sustainable agriculture. All the existing techniques for soil quantification do not take advantage of the sequential nature of Hyperspectral Data. This work focuses on proposing a Hybrid Framework that can quantitatively...Read more
Estimation of Soil Organic Carbon for Sustainable Agriculture using Deep Learning
(Corresponding) Singh S., Kasana S.
Topic:
Agroforestry, forest and agricultural sustainability
The organic carbon percentage is concomitant indicating the mineralization of nutrients and the ability of the soil to hold nutrients cations, structural stability, and water holding capacity. It is necessary to know the quantity of carbon for healthy soil and avoid the production related problems...Read more
Keywords:
Deep Learning, Long Short-Term Networks, Silica, Organic Carbon, Hyperspectral Data
Meteorological Data Science: exploiting causality discovery in time-series for knowledge discovery and improved forecasting
Gkikas A., (Corresponding) Maragoudakis M.
Topic:
Enviornmental data analysis and modelling
Climate change and its impact on everyday life still remains one of the greatest challenge of our era. The complex nature of climate data addresses the use of data science techniques to provide predictive analytics to the task at hand. While most existing approaches exploit correlation between...Read more
Keywords:
Data Science, Causal Inference, Time-Series Analysis, Graph Analysis, Feature Selection
Aloe Vera waste for Methylene Blue (MB), Rhodamine B (RB) and Methyl Orange (MO) adsorption.
(Corresponding) Mazzeo L., Bavasso I., Di Palma L.
Topic:
Wastewater treatment
Aloe Vera (Aloe barbadensis miller) waste obtained from a local cosmetic production factory was used as bio-adsorbent for the removal of three different dyes: Methylene Blue (MB), Rhodamine B (RB) and Methyl Orange (MO). The material was preliminary washed with water at room temperature. Batch...Read more
Keywords:
Adsorption; Aloe Vera; Data fitting; wastewater treatment.
Correction of the predicted wave characteristics using regression methods – a case study for the Iberian coastal environment
(Corresponding) Rusu L.
Topic:
Marine environment and coastal management
The west Iberian coast is affected by various storms developed in the North Atlantic Ocean. For this reason, an accurate prediction of the sea state conditions is very important to manage the protection of the harbours and population living in the coastal cities. In recent years computing power has...Read more
Keywords:
SWAN, West Iberian coast, hindcast wave data, correction, regression methods.
What is the impact of earth observation and in-situ data assimilation on seasonal hydrological predictions?
(Corresponding) Pechlivanidis I., Musuuza J.
Topic:
Process understanding through innovative sensors and remote sensing
Earth Observations (EO) have become popular in hydrology because they provide information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EOs into hydrological models to improve the...Read more
Keywords:
earth observations, hydrology, forecasting, data assimilation
The research present a novel dynamic and embedded multi criteria methodology for the environmental monitoring and control of large-scale civil engineering works subjected to environmental impact assessment procedure. The principal phases are discussed and highlighted, along with the identification...Read more
Keywords:
environmental monitoring, port, mitigation actions, civil works, real-time data
