摘要 :
Nowcasting represents a short-term weather forecast of how the atmospheric state will evolve during the next time period, typically less than two hours. It is vital for generating society-level emergency alerts in order to take ti...
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Nowcasting represents a short-term weather forecast of how the atmospheric state will evolve during the next time period, typically less than two hours. It is vital for generating society-level emergency alerts in order to take timely actions and responses to potential disasters. The objective of the paper is to improve upon current nowcasting methods by applying a Deep Learning model that uses Convolutional Long-Short Term Memory Networks on a combination of satellite data. It is proposed a model ConvS Now for short-term prediction of satellite images that would be useful for precipitation nowcasting. The proposed model was trained and evaluated on satellite imagery collected by EUMESAT's Meteosat-11 satellite utilizing the Severe Storms RGB product. The experimental results performed a subset of the Meteosat-11 data spanning Europe demonstrate that this model can enhance weather short-term forecasting, reduce costs and time, and improve the general quality of predictions, as a normalized mean of absolute errors of 1.6% was attained, outperforming every other baseline approaches considered for comparison. A relative improvement of more than 30% has been achieved by the ConvS Now compared to the baselines, our proposed model being able to capture the spatio-temporal features of the weather evolution.
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摘要 :
Due to the increasing number of severe phenomena in many regions of the world, weather nowcasting, which is the weather forecast for a short time period, is one of the most challenging topics in meteorology. The weather radar and ...
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Due to the increasing number of severe phenomena in many regions of the world, weather nowcasting, which is the weather forecast for a short time period, is one of the most challenging topics in meteorology. The weather radar and satellite are essential tools currently used by operational meteorologists for nowcasting. Issuing nowcasting warnings based on radar and satellite data is a complex task, due to the large volume of data that should be analyzed by meteorologists in order to make decisions. We are introducing in this paper DeePS at, a convolutional neural network architecture for short-term satellite images prediction that would be useful for weather nowcasting. The experimental evaluation is conducted on satellite data collected by EUMETSAT’s Meteosat-11 satellite, using five satellite products. The obtained results are analyzed and compared to the results of similar approaches. An average normalized mean of absolute errors of 3.84% was obtained for all satellite products, highlighting this way the effectiveness of DeePS at model.
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Online data miningtechniques are used to uncover relevant patterns in complex data which are dynamic by nature and thus continuously extended with real-time arriving data streams.Relational association rules(RARs), a data analysis...
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Online data miningtechniques are used to uncover relevant patterns in complex data which are dynamic by nature and thus continuously extended with real-time arriving data streams.Relational association rules(RARs), a data analysis and mining concept, extend the classical association rules so as to capture different relations between the attributes characterizing the data. This paper introduces a newIncremental Relational Association Rule Mining(IRARM) approach with the aim of progressively adapting the interestingrelational association rulesidentified in a data set, when it is enlarged with new instances. We have experimentally evaluatedIRARMon publicly available data sets. The reduction in mining time when usingIRARMagainst mining from scratch emphasizes its efficiency in adapting the rules to real-time data extension.
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This paper analyses the problem of predicting students’ academic performance, a subject that is increasingly investigated within the Educational Data Mining literature. For a better understanding of the educational related phenom...
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This paper analyses the problem of predicting students’ academic performance, a subject that is increasingly investigated within the Educational Data Mining literature. For a better understanding of the educational related phenomena, there is a continuous interest in applyingsupervisedandunsupervisedlearning methods for obtaining additional insights into the students’ learning process. The problem of predicting if a student will pass or fail at a certain academic discipline based on the students’ grades received during the semester is a difficult one, highly dependent on various conditions such as the course, the number of examinations during the semester, the instructors and their exigences. We propose a new classification model,S PRAR (Students Performance prediction using Relational Association Rules)for predicting the final result of a student at a certain academic discipline usingrelational association rules(RARs). RARs extend the classical association rules for expressing various relationships between data attributes. Experiments are performed on three real academic data sets collected from Babe?-Bolyai University from Romania. The performance of theS PRARclassifier on the considered case studies is compared against existing related work, being superior to previously proposed students’ performance predictors.
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The COSPAS-SARSAT system locates distress beacons via Doppler analysis of beacon transmissions as observed by low Earth orbiting (LEO) satellites. By contrast, the 406 MHz search and rescue transponder on the geostationary satelli...
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The COSPAS-SARSAT system locates distress beacons via Doppler analysis of beacon transmissions as observed by low Earth orbiting (LEO) satellites. By contrast, the 406 MHz search and rescue transponder on the geostationary satellites can measure only the transmitted frequency of the beacon, which is not currently being used in location determination. This report shows that a combined data approach can significantly improve the locating success rate, the time delay to the first solution, and the accuracy of location estimates
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This work presents an analysis of passive inverse synthetic aperture radar images obtained exploiting simultaneously digital video broadcasting-terrestrial (DVB-T) and digital video broadcasting-satellite (DVB-S) as illuminators o...
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This work presents an analysis of passive inverse synthetic aperture radar images obtained exploiting simultaneously digital video broadcasting-terrestrial (DVB-T) and digital video broadcasting-satellite (DVB-S) as illuminators of opportunity (IOs) over a cooperative maritime target with known motion. The combined exploitation of these two IOs is extremely appealing for passive imaging purposes, given their complementary characteristics. The analysis is first conducted in a simulated environment, to show the different expected outcomes from the two considered bistatic geometries and operating bandwidths. Subsequently, the same analysis is repeated over real data acquired during a field trial, by exploiting experimental setups developed at Fraunhofer FHR. In particular, DVB-T and DVB-S data are focused by means of back projection, which enables an easier comparison of the different ISAR products. Real data results show good match with simulated ones. Target size can be estimated with good accuracy in both DVB-T and DVB-S cases, and dominant scatterers can also be identified. DVB-S also enables target-shape recognition, given its higher signal bandwidth.
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