Design of Fan Beam Gamma Ray Tomography Scanning System

Dewi, Fitria Yunita; Wibisono,; Prawito,

doi:10.1109/iceeie47180.2019.8981460

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…), deep learning (convolutional neural networks (CNNs)), and fuzzy logic practices, which all belong to artificial intelligence (AI), have been used to improve fog forecasting. The CNN was first implemented in 1995 [101], and then case studies were performed by many researchers [102][103][104][105][106]. Fuzzy logic was first tested [107,108] and then improved [109][110][111].…”

Section: Fog Forecastingmentioning

confidence: 99%

Fog Water: A General Review of Its Physical and Chemical Aspects

Khoury,

Millet,

Jabali

et al. 2023

Environments

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Studies concerning fog water have been rapidly increasing due to its negative impacts on different environmental processes. However, fog water harvesting has become beneficial in various countries to overcome water scarcity. Accurate fog forecasting remains a challenging issue due to its spatio-temporal variability and uncertainties despite the development and efforts made to understand its chemistry and microphysics. The literature proved that the decrease in fog frequency over time in most countries is mainly attributed to the improvement in air quality or the change in regional climatic conditions. The current fog review summarizes its different types and collectors, life cycle, and impacts, the effects of aerosols, and the latest results concerning its forecast challenges and frequency. It also highlights the major chemical processes along with the main field studies performed on fog water. The aim of this work is not to provide a criticism about fog but to present a general comprehensive review of its physical and chemical aspects covering up to 330 research and review papers aimed to serve as a basis for new challenges and findings about fog water.

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Section: Fog Forecastingmentioning

confidence: 99%

Fog Water: A General Review of Its Physical and Chemical Aspects

Khoury,

Millet,

Jabali

et al. 2023

Environments

View full text Add to dashboard Cite

show abstract

“…Through state-of-the-art numerical models and statistical approaches such as a decision tree [5][6][7], neural network [8,9], fuzzy logic [10,11], and artificial intelligence/machine learning [12][13][14][15], the predictability of fog could be enhanced by reproducing the fogrelevant thermodynamical state of the atmosphere. Although the nowcasting (6-12 h) skill of statistical approaches for site-specific fog is reasonably satisfactory, these approaches have limited spatial resolution and depend on long-term fog field observations.…”

Section: Introductionmentioning

confidence: 99%

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

et al. 2022

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One of the well-known challenges of fog forecasting is the high spatio-temporal variability of fog. An ensemble forecast aims to capture this variability by representing the uncertainty in the initial/lateral boundary conditions (ICs/BCs) and model physics. The present study highlights a new operational Ensemble Forecast System (EFS) developed by the Indian Institute of Tropical Meteorology (IITM), Pune, to predict the fog over the Indo-Gangetic Plain (IGP) region using the visibility (Vis) diagnostic algorithm. The EFS framework comprises the WRF model with a 4 km horizontal resolution, initialized by 21 ICs/BCs. The advantages of probabilistic fog forecasting have been demonstrated by comparing control (CNTL) and ensemble-based fog forecasts. The forecast is verified using fog observations from the Indira Gandhi International (IGI) airport during the winter months of 2020–2021 and 2021–2022. The results show that with a probability threshold of 50%, the ensemble forecasts perform better than the CNTL forecasts. The skill scores of EFS are relatively promising, with a Hit Rate of 0.95 and a Critical Success Index of 0.55; additionally, the False Alarm Rate and Missing Rate are low, with values of 0.43 and 0.04, respectively. The EFS could correctly predict more fog events (37 out of 39) compared with the CNTL forecast (31 out of 39) and shows the potential skill. Furthermore, EFS has a substantially reduced error in predicting fog onset and dissipation (mean onset and dissipation error of 1 h each) compared to the CNTL forecasts.

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Design of Fan Beam Gamma Ray Tomography Scanning System

Cited by 2 publications

References 12 publications

Fog Water: A General Review of Its Physical and Chemical Aspects

Fog Water: A General Review of Its Physical and Chemical Aspects

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

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