Modelling and Remote Sensing of Land Surface Temperature in Turkey

Şahin, Mehmet; Yıldız, B. Yiğit; Şenkal, Ozan; Peştemalci, V.

doi:10.1007/s12524-011-0158-3

Cited by 21 publications

(3 citation statements)

References 31 publications

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“…Deep learning methods have been used in Arctic climate studies, such as sea ice forecasting [26][27][28][29] . Recent studies have demonstrated that deep learnings are valuable methods for patching missing data, with partial convolution method outperforming other image inpainting technologies 18,[30][31][32][33] . Compared to conventional interpolation methods such as kriging and principal component analysis-based infilling, deep learning approaches with partial convolution can produce geographically more realistic temperatures 18 .…”

Section: Background and Summarymentioning

confidence: 99%

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

Huang

Zhang

et al. 2023

Sci Data

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A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications.

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Section: Background and Summarymentioning

confidence: 99%

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

Huang

Zhang

et al. 2023

Sci Data

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“…In this study, satellite‐based land surface temperature (LST) obtained from the National Oceanic and Atmospheric Administration advanced very high‐resolution radiometer (NOAA‐AVHRR) was considered as a function of SR and used as a part of the inputs for training ELM. Because it has an extremely large influence upon SR, it has been frequently preferred as an independent variable in many previous studies to estimate SR . However, to obtain the LST value, images of NOAA‐AVHRR channels must be transformed by being subjected to the following three basic processes: estimation of normalized difference vegetation index (NDVI), calculation of surface emissivity, and application of LST algorithm‐based split‐window technique.…”

Section: Study Area and Data Descriptionmentioning

confidence: 99%

Application of extreme learning machine for estimating solar radiation from satellite data

Şahin

Kaya

Uyar

et al. 2013

Int. J. Energy Res.

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SUMMARYIn this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell.

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“…Corrosion is one of the foremost functional issues involved with the heat exchangers and pipelines system of cooling water. [ 3–5 ] These problems impose a monstrous economic effect due to their association with the metal substrate's degradation, resulting in losing their thermal exchange capacity. Moreover, these complications are a probable hazard to high asset speculations as well as investment damage at the cost of the industry's recurrent shutdown.…”

Section: Introductionmentioning

confidence: 99%

Acridine as Bioinspired Corrosion Inhibitors

Bhati

Tripathy

Gupta

2023

Macromolecular Symposia

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Mild steel has been considerably exploited in various manufacturing and commercial fields due to its tremendous mechanical performance. However, mild steel's high susceptibility to corrosion under acidic conditions leads to vast economic losses and environmental consequences. Inhibitors used for steel corrosion in the acidic medium are ordinarily polar heteroaromatic molecules with electronegative atoms. Acridines are one type of inhibitor with several advantages, such as heteroatoms, functional groups and their polarity, double bond conjugation, and importantly efficient and non‐hazardous synthetic routes. The polar moieties of such inhibitors are considered to augment their adsorption on steel surface, thus creating a shielding to inhibit steel corrosion. Acridine analogs are N‐heterocyclic organic compounds that can be easily produced owing to impeccable inhibition capacity. The current review discusses the chemistry, synthesis, corrosion inhibition mechanism, and properties of acridines and their derivatives.

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Modelling and Remote Sensing of Land Surface Temperature in Turkey

Cited by 21 publications

References 31 publications

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

Application of extreme learning machine for estimating solar radiation from satellite data

Acridine as Bioinspired Corrosion Inhibitors

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