Article
Geochemistry & Geophysics
William O. Taylor, Marios N. Anagnostou, Diego Cerrai, Emmanouil N. Anagnostou
Summary: This study investigates the effectiveness of machine learning techniques in measuring wind speed and precipitation rate using underwater ambient ocean noises recorded by a passive aquatic listener in the Mediterranean. The results show strong correlation between the acoustic data and wind speed, as well as a significant reduction in unexplained variance. Machine learning models such as CatBoost and random forest demonstrate promising performance in measuring precipitation amount.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Risma Joseph, P. P. Mujumdar, Rajarshi Das Bhowmik
Summary: The impact of climate change on urban flooding has increased in recent decades, and historical design storms need to be revised based on robust intensity-duration-frequency relationships. This study reconstructs sub-daily rainfall time series over an urban rain-gauge network and investigates the spatiotemporal variation in extreme rainfall distribution within Bangalore, India. The study finds significant spatial variations in extreme rainfall distribution and observes a significant reduction in the volume of design floods with increasing area.
Article
Energy & Fuels
Florin Onea, Eugen Rusu
Summary: The objective of this study is to provide a comprehensive understanding of the benefits that can be gained from implementing offshore wind projects in island environments in the Mediterranean Sea. By analyzing wind speed data, potential areas with high wind energy capacity, such as those near Sardinia or Cyprus, are identified. The results of the study indicate that the north-western part of the region is the most suitable for wind turbine operation, with a capacity factor of at least 6%.
Article
Environmental Sciences
Eva Turicchia, Massimo Ponti, Gianfranco Rossi, Martina Milanese, Cristina Gioia Di Camillo, Carlo Cerrano
Summary: Since 2001, trained EcoDivers have been using the RCMed U-CEM protocol to record data on key marine species along the Mediterranean Sea coasts. The dataset collected has proven useful for monitoring ecological status, assessing human impacts, and complementing scientific papers.
FRONTIERS IN MARINE SCIENCE
(2021)
Article
Chemistry, Analytical
Shu Liu, Qi Li, Dajing Shang, Rui Tang, Qingming Zhang
Summary: The study focuses on underwater noise produced by raindrops hitting the water surface, proposing a method to calculate the acoustic energy generated and developing a model to predict the average underwater sound energy radiated by single raindrops through experiments.
Article
Engineering, Marine
Ahmed I. Elshinnawy, Jose A. A. Antolinez
Summary: This study examines 58 years (1961-2018) of wind-waves in the Mediterranean Sea. A wave dataset was created using high-resolution downscaled wind fields, which provided a more accurate representation of the local geomorphology. The results showed that the western Mediterranean Sea experiences the most frequent storms, with an average of three storms per year. Overall, there were mild long-term trends in the mean and extreme wave conditions and in storminess.
Article
Geosciences, Multidisciplinary
Dong-Gyun Han, Jongmin Joo, Wuju Son, Kyoung Ho Cho, Jee Woong Choi, Eun Jin Yang, Jeong-Hoon Kim, Sung-Ho Kang, Hyoung Sul La
Summary: The underwater acoustic environment in the Arctic Ocean is rapidly changing due to Arctic warming. Results from passive acoustic monitoring in the East Siberian Sea's marginal ice zone show a strong negative correlation between ambient sound levels and sea ice concentration (SIC). The sound level in September was significantly higher than other months, indicating a potential increase in ambient sound level in the Arctic Ocean as sea ice melting accelerates.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Plant Sciences
Francesco Rendina, Annalisa Falace, Giuseppina Alongi, Maria Cristina Buia, Joao Neiva, Luca Appolloni, Giuliana Marletta, Giovanni Fulvio Russo
Summary: This study reports the discovery of healthy and dense marine forests formed by Fucales in the Santa Maria di Castellabate Marine Protected Area in Cilento, Italy. The presence of 10 Cystoseira taxa and the high ecological value of this area highlight the importance of marine protected area management and regional monitoring programs for the conservation of these valuable yet fragile coastal ecosystems.
Article
Green & Sustainable Science & Technology
Nan Xiao, Zhibao Dong, Shun Xiao, Jiaqi Wang, Zhengyao Liu, Sarina, Yu Tuo, Chunming Zhu, Miaoyan Feng
Summary: Wind resource assessment is important for the economy and ecology, especially in arid areas. Little research has been done on the estimation of sand-driving winds. A function was constructed to improve the estimation of sand-driving winds, which has implications for wind energy development and understanding of aeolian activities.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Engineering, Civil
Andrea Trucco, Annalisa Barla, Roberto Bozzano, Sara Pensieri, Alessandro Verri, David Solarna
Summary: In the past, wind and rainfall predictions were made using empirical equations with limited spectral data. Recent studies show that supervised machine learning techniques using all spectral data can improve accuracy at the cost of complexity. This article proposes using temporal correlation in meteorological data to predict wind and rainfall, and demonstrates that using the long short-term memory recurrent neural network along with a dataset of underwater noise measurements significantly improves accuracy compared to random forest regression.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2023)
Article
Environmental Sciences
Diego Macias, Adolf Stips, Georg Hanke
Summary: Plastic litter pollution is a major concern for marine ecosystems worldwide, and EU Member States have agreed on a maximum threshold of litter items per coast length. Research shows that the amount of transboundary litter in Mediterranean countries can be significant, with regional and seasonal differences.
MARINE POLLUTION BULLETIN
(2022)
Article
Thermodynamics
Tahsin Gormus, Burak Aydogan, Berna Ayat
Summary: We evaluated the spatiotemporal characteristics of wind power density (WPD) in the Mediterranean and Black Sea regions. Using hourly wind speed data from ERA5 of ECMWF spanning over 62 years, we revealed the spatial distributions of WPD at different time scales. The study identified the Gulf of Lion and Aegean Sea as the most prominent regions in terms of WPD. Additionally, the diurnal variation, variability indicators, and the performance of five commercial turbines in the study area were investigated.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Engineering, Environmental
Jose M. Perez-Bella, Javier Dominguez-Hernandez, Juan E. Martinez-Martinez, Mar Alonso-Martinez, Juan J. del Coz-Diaz
Summary: This study assesses the reliability and functionality of generic equations used to estimate extreme values of climatic variables, finding that these estimations have limitations and errors when characterizing both variables for subdaily intervals and extreme conditions. An alternative approach is proposed to accurately extrapolate extreme values of both variables related to any subdaily recording interval in a functional manner and from any available records.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Acoustics
Emmanuelle D. Cook, David R. Barclay, Clark G. Richards
Summary: The main sources of noise in the Arctic Ocean are naturally occurring, and sustained acoustic monitoring at high latitudes provides quantitative measures of changes in the sound field. The study presents a 12-month time series of ambient sound levels recorded near Gascoyne Inlet, Nunavut, showing a dependence on seasonal ice variations and higher frequencies varying more strongly. The analysis suggests that tidal-driven surface currents and ice block collisions contribute to the periodic trend in noise power.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2022)
Article
Environmental Sciences
Young Geul Yoon, Dong-Gyun Han, Jee Woong Choi
Summary: As interest in renewable energy development grows, numerous offshore wind farms are being constructed worldwide. Consequently, the potential effects of underwater operational noise on marine ecosystems have become a concern, necessitating an understanding of the mechanisms and acoustic characteristics of such noise for environmental impact assessments.
FRONTIERS IN MARINE SCIENCE
(2023)
Article
Geochemistry & Geophysics
William O. Taylor, Marios N. Anagnostou, Diego Cerrai, Emmanouil N. Anagnostou
Summary: This study investigates the effectiveness of machine learning techniques in measuring wind speed and precipitation rate using underwater ambient ocean noises recorded by a passive aquatic listener in the Mediterranean. The results show strong correlation between the acoustic data and wind speed, as well as a significant reduction in unexplained variance. Machine learning models such as CatBoost and random forest demonstrate promising performance in measuring precipitation amount.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Engineering, Marine
Andrea Bordone, Tiziana Ciuffardi, Giancarlo Raiteri, Antonio Schirone, Roberto Bozzano, Sara Pensieri, Francesca Pennecchi, Paola Picco
Summary: This study focuses on the influence and uncertainty analysis of ADCP systems mounted on spar buoys for sea current measurements, proposing a new methodology for correction and estimation. Through comparison with marine current numerical models and historical data, the proposed method has been well validated, ensuring reliable measurements for oceanographic studies and 3D hydrodynamic model validation.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Marine
Dionisis L. Patiris, Sara Pensieri, Christos Tsabaris, Roberto Bozzano, Effrossyni G. Androulakaki, Marios N. Anagnostou, Stylianos Alexakis
Summary: Marine in situ gamma-ray spectrometry was used to study rainfall in the Ligurian Sea, Italy, revealing TCR as a good rainfall indicator and K-40 as an additional radio-tracer. Results showed that TCR and radon progenies concentrations exhibited a non-linear increasing trend with rainfall height and intensity.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Civil
Andrea Trucco, Roberto Bozzano, Emanuele Fava, Sara Pensieri, Alessandro Verri, Annalisa Barla
Summary: Underwater noise analysis can help estimate meteorological parameters in harsh environments such as polar waters. Machine learning models, specifically random forest classifiers, have shown promising performance in detecting precipitation events using hourly averaged spectra data.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2022)
Article
Geochemistry & Geophysics
Yagmur Derin, Md Abul Ehsan Bhuiyan, Emmanouil Anagnostou, John Kalogiros, Marios N. Anagnostou
Summary: This study examines the representation of precipitation variability over mountainous regions using satellite-based precipitation products and an extended network of ground-based X-band radar deployments. The error model developed in this study can significantly reduce errors in PMW products and is transferable among complex terrain regions, which may be useful for algorithm developers to integrate in Level 3 products.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Engineering, Marine
Sara Pensieri, Federica Viti, Gabriele Moser, Sebastiano Bruno Serpico, Luca Maggiolo, Martina Pastorino, David Solarna, Andrea Cambiaso, Carlo Carraro, Cristiana Degano, Ilaria Mainenti, Silvia Seghezza, Roberto Bozzano
Summary: The study introduces a low-cost marine machine-to-machine communication system, utilizing LoRa technology for experimental evaluation in a marine environment. Through field tests, the reliability of LoRaWAN transmission was demonstrated to be over 110 km in a free space scenario and over 20 km in a coastal urban environment.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Environmental Sciences
Nikolaos Gourgouletis, Georgios Bariamis, Marios N. Anagnostou, Evangelos Baltas
Summary: This research aims to demonstrate the efficiency of combining remotely sensed water level and water area estimations in estimating the water storage variation of a reservoir. The results show that remote sensing is highly effective in producing empirical level-area-storage curves, with a high correlation to in situ observations.
Article
Engineering, Civil
Andrea Trucco, Annalisa Barla, Roberto Bozzano, Emanuele Fava, Sara Pensieri, Alessandro Verri, David Solarna
Summary: This article demonstrates the superiority of regression models based on supervised learning over empirical equations using underwater noise and anemometer measurements. It also shows that different tradeoffs between accuracy and temporal resolution can be achieved depending on the type of compounding implemented.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2022)
Article
Engineering, Civil
Andrea Trucco, Annalisa Barla, Roberto Bozzano, Sara Pensieri, Alessandro Verri, David Solarna
Summary: In the past, wind and rainfall predictions were made using empirical equations with limited spectral data. Recent studies show that supervised machine learning techniques using all spectral data can improve accuracy at the cost of complexity. This article proposes using temporal correlation in meteorological data to predict wind and rainfall, and demonstrates that using the long short-term memory recurrent neural network along with a dataset of underwater noise measurements significantly improves accuracy compared to random forest regression.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2023)
Article
Engineering, Marine
Roberto Nardini, Paola Picco, Tiziana Ciuffardi, Roberto Bozzano, Maurizio Demarte, Giancarlo Raiteri, Andrea Bordone, Sara Pensieri
Summary: Echo-sounders and Vessel-Mounted Acoustic Doppler Current Profilers (VM-ADCP) are used on research vessels to provide real-time backscatter and ocean current profiles during vessel movement. However, mapping zooplankton from a moving vessel is challenging due to their daily vertical migration and space-time variability. This study aims to describe a GIS application developed for managing and analyzing these data, with a test-case in the Ligurian Sea using VM-ADCP backscatter data. The GIS system allows for the selection and visualization of data sorted according to various layers, and the identification of migration phases through false color scale representation of backscatter profiles.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
