Wave height predictions in complex sea flows through soft-computing models: Case study of Persian Gulf

Lama, Giuseppe Francesco Cesare; Sihag, Parveen; Bayram, Atilla; Kişi, Özgür

doi:10.1016/j.oceaneng.2021.110467

Cited by 71 publications

(20 citation statements)

References 78 publications

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“…For this purpose, the correlation of surface temperature with water and air temperature was first investigated through field and satellite measurements. To observe the scattering of field and satellite data, fitting lines and corresponding equations were obtained and the error rates were calculated [17]. The R 2 value was estimated as 0.9 which shows that there is a strong correlation between these two data.…”

Section: Resultsmentioning

confidence: 99%

“…The effects of wind and other meteorological factors such as wind speed, precipitation, evaporation and pollutants on parameters such as surface water temperature, salinity, sea currents, sea surface instability, etc. in the southern and southwestern parts of Iran have rarely been studied [16,17]. This study tries to investigate the effect of climate change (dust phenomenon) on the physical parameters of seawater in the western basin of the Persian Gulf and also to provide the necessary predictions in this regard and analyze the results in detail.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Dust Storm on Sea Surface Temperature in the Western Basin of Persian Gulf

et al. 2022

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A dust storm is one of the costliest and most destructive events in many desert regions. This research investigates the effect of dust storm on sea surface temperature (SST) in the western zone of the Persian Gulf, especially Bushehr Province and its beaches in the years 2008 and 2009. Therefore, some climate and sea parameters such as SST, salinity, air temperature, wind velocity and direction, evaporation, horizontal visibility, sunshine hours and radiation, simultaneously measured in a specific period of time, were analyzed by comparing each of them with satellite data. Sea surface temperature analysis in summer shows that the maximum SST in Persian Gulf along neighbor waters to Bushehr County and central regions in northern section of Persian Gulf is about 34–36 °C. The SST amplitude variation in these places in summer ranges from 28 to 34 °C and when there are dust phenomena, it is from 29.5 to 31 °C. The outcome of this study shows that the SST increases during dusting phenomena and this causes an increase in vapor and as a result a decrease in temperature occurs. On the other hand, vapor increase leads to a growth in the amount and layer of earth’s cloud cover and finally it causes an effective decrease in short-wave sunshine and the temperature and the vapor on surface decrease. As a result, the decrease in sea surface temperature terminates.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of Dust Storm on Sea Surface Temperature in the Western Basin of Persian Gulf

et al. 2022

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“…Although the empirical thresholds of test-based methods such as Fmask [11], ATCOR [12] and Sen2Cor [13] can provide rough snow prediction in most scenarios, studies that focused on improving snow coverage segmentation performance by making use of machine (deep)-learning algorithms are still missing. It should be highlighted that conventional machine-learning methods and recent deep-learning methods (in particular) have made significant breakthroughs with appreciable performance in various applications including agriculture, and these are therefore worth investigation for snow coverage mapping as well [22,23].…”

Section: Introductionmentioning

confidence: 99%

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

Wang

Zhai

et al. 2022

Remote Sensing

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Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping.

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“…LiDAR point clouds have been widely used in many emerging applications [ 1 ], such as the preservation of historical relics, mobile robots, and remote sensing [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. LiDAR sensors are essential for autonomous vehicles, and dense LiDAR point cloud maps play an indispensable role in unmanned driving, such as obstacle detection [ 10 ], localization [ 11 ], and navigation [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…LiDAR sensors are essential for autonomous vehicles, and dense LiDAR point cloud maps play an indispensable role in unmanned driving, such as obstacle detection [ 10 ], localization [ 11 ], and navigation [ 12 ]. In wide geographic areas, a LiDAR point cloud map consists of a vast number of points and requires a large bandwidth and storage space to transmit and store [ 4 , 13 , 14 , 15 ]. Therefore, developing compression algorithms for the dense LiDAR point cloud maps is an urgent task.…”

Section: Introductionmentioning

confidence: 99%

An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network

Wang

Jiang

Sun

et al. 2022

Sensors

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In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the point cloud map compression into the point cloud sequence compression problem. The coding architecture includes two techniques: intra-coding and inter-coding. For intra-frames, a segmentation-based intra-prediction technique is developed. For inter-frames, an interpolation-based inter-frame coding network is explored to remove temporal redundancy by generating virtual point clouds based on the decoded frames. We only need to code the difference between the original LiDAR data and the intra/inter-predicted point cloud data. The point cloud map can be reconstructed according to the decoded point cloud sequence and quaternion matrices. Experiments on the KITTI dataset show that the proposed coding scheme can largely eliminate the temporal and spatial redundancies. The point cloud map can be encoded to 1/24 of its original size with 2 mm-level precision. Our algorithm also obtains better coding performance compared with the octree and Google Draco algorithms.

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Wave height predictions in complex sea flows through soft-computing models: Case study of Persian Gulf

Cited by 71 publications

References 78 publications

The Effect of Dust Storm on Sea Surface Temperature in the Western Basin of Persian Gulf

The Effect of Dust Storm on Sea Surface Temperature in the Western Basin of Persian Gulf

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network

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