Short-Term Flood Prediction System

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Konrad Nering
Keyword(s):  
Flash Floods ◽  
Prediction System ◽  
Short Term ◽  
Data Set ◽  
Flood Prediction ◽  
Full Operation

AbstractThis paper describes a fully functional short-term flood prediction system. Its effect has been tested on watershed of Lubieńka river in Małopolska. To use this system it must have a data set also described in this paper. A modification of the system to adopt for predicting flash floods was described. Full operation of the system is shown on example of real flood on Lubieńka river in June 2011.

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

scholarly journals Investigating the Broken-Heart Effect: a Model for Short-Term Dependence between the Remaining Lifetimes of Joint Lives

2012 ◽  
Vol 7 (2) ◽  
pp. 236-257 ◽  
Author(s):  
Jaap Spreeuw ◽  
Iqbal Owadally
Keyword(s):  
Mortality Rates ◽  
Short Term ◽  
Numerical Work ◽  
Data Set ◽  
Multiple State ◽  
Multiple State Model ◽  
Insurance Data ◽  
Over Time ◽  
Assurance Contracts

AbstractWe analyze the mortality of couples by fitting a multiple state model to a large insurance data set. We find evidence that mortality rates increase after the death of a partner and, in addition, that this phenomenon diminishes over time. This is popularly known as a “broken-heart” effect and we find that it affects widowers more than widows. Remaining lifetimes of joint lives therefore exhibit short-term dependence. We carry out numerical work involving the pricing and valuation of typical contingent assurance contracts and of a joint life and survivor annuity. If insurers ignore dependence, or mis-specify it as long-term dependence, then significant mis-pricing and inappropriate provisioning can result. Detailed numerical results are presented.

Design and FPGA Prototyping of a Flood Prediction System

2008 ◽  
Author(s):  
Sandeep Gandla ◽  
Waleed K. Al-Assadi ◽  
Sahra Sedigh ◽  
Raghu A. R. Rao
Keyword(s):  
Prediction System ◽  
Flood Prediction

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

2021 ◽  
Author(s):  
Eva van der Kooij ◽  
Marc Schleiss ◽  
Riccardo Taormina ◽  
Francesco Fioranelli ◽  
Dorien Lugt ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
Heavy Rainfall ◽  
Predictive Performance ◽  
Heavy Precipitation ◽  
Early Warning Systems ◽  
Training Data ◽  
Short Term ◽  
Data Set ◽  
Radar Images

<p>Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.</p><p>We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.</p><p>We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.</p><p>To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.</p>

scholarly journals Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Ocean Science ◽  
2017 ◽  
Vol 13 (6) ◽  
pp. 925-945 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Observational Data ◽  
Skill Score ◽  
Forecast Skill ◽  
Objective Analysis ◽  
Prediction System ◽  
Regional Ocean Modeling System ◽  
Data Set ◽  
Vast Number ◽  
Ocean Prediction ◽  
Forecast Skill Score

Abstract. A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

scholarly journals Interactive effects of ambient ozone and climate measured on growth of mature loblolly pine trees

1996 ◽  
Vol 26 (4) ◽  
pp. 670-681 ◽  
Author(s):  
S.B. McLaughlin ◽  
D.J. Downing
Keyword(s):  
Soil Moisture ◽  
Loblolly Pine ◽  
Moisture Stress ◽  
Growth Patterns ◽  
Forest Growth ◽  
Interactive Effects ◽  
Forest Trees ◽  
Short Term ◽  
Data Set ◽  
Ambient Ozone

Seasonal growth patterns of mature loblolly pine (Pinustaeda L.) trees over the interval 1988–1993 have been analyzed to evaluate the effects of ambient ozone on growth of large forest trees. Patterns of stem expansion and contraction of 34 trees were examined using serial measurements with sensitive dendrometer band systems. Study sites, located in eastern Tennessee, varied significantly in soil moisture, soil fertility, and stand density. Levels of ozone, rainfall, and temperature varied widely over the 6-year study interval. Regression analysis identified statistically significant influences of ozone on stem growth patterns, with responses differing widely among trees and across years. Ozone interacted with both soil moisture stress and high temperatures, explaining 63% of the high frequency, climatic variance in stem expansion identified by stepwise regression of the 5-year data set. Observed responses to ozone were rapid, typically occurring within 1–3 days of exposure to ozone at ≥40 ppb and were significantly amplified by low soil moisture and high air temperatures. Both short-term responses, apparently tied to ozone-induced increases in whole-tree water stress, and longer term cumulative responses were identified. These data indicate that relatively low levels of ambient ozone can significantly reduce growth of mature forest trees and that interactions between ambient ozone and climate are likely to be important modifiers of future forest growth and function. Additional studies of mechanisms of short-term response and interspecies comparisons are clearly needed.

scholarly journals A Dense Long Short-Term Memory Model for Enhancing the Imagery-Based Brain-Computer Interface

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaofei Zhang ◽  
Tao Wang ◽  
Qi Xiong ◽  
Yina Guo
Keyword(s):  
Short Term Memory ◽  
Recognition Algorithm ◽  
Training Process ◽  
Short Term ◽  
Data Set ◽  
Static State ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Model Training ◽  
The One

Imagery-based brain-computer interfaces (BCIs) aim to decode different neural activities into control signals by identifying and classifying various natural commands from electroencephalogram (EEG) patterns and then control corresponding equipment. However, several traditional BCI recognition algorithms have the “one person, one model” issue, where the convergence of the recognition model’s training process is complicated. In this study, a new BCI model with a Dense long short-term memory (Dense-LSTM) algorithm is proposed, which combines the event-related desynchronization (ERD) and the event-related synchronization (ERS) of the imagery-based BCI; model training and testing were conducted with its own data set. Furthermore, a new experimental platform was built to decode the neural activity of different subjects in a static state. Experimental evaluation of the proposed recognition algorithm presents an accuracy of 91.56%, which resolves the “one person one model” issue along with the difficulty of convergence in the training process.

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