Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil

2019 ◽  
Vol 99 (1) ◽  
pp. 12-24 ◽  
Author(s):  
Rezvan Taki ◽  
Claudia Wagner-Riddle ◽  
Gary Parkin ◽  
Rob Gordon ◽  
Andrew VanderZaag
Keyword(s):  
Missing Values ◽  
Interpolation Method ◽  
Weather Conditions ◽  
Linear Interpolation ◽  
Bias Error ◽  
Research Station ◽  
Ann Model ◽  
Gap Filling ◽  
Adverse Weather ◽  
The Impact

Micrometeorological methods are ideally suited for continuous measurements of N2O fluxes, but gaps in the time series occur due to low-turbulence conditions, power failures, and adverse weather conditions. Two gap-filling methods including linear interpolation and artificial neural networks (ANN) were utilized to reconstruct missing N2O flux data from a corn–soybean–wheat rotation and evaluate the impact on annual N2O emissions from 2001 to 2006 at the Elora Research Station, ON, Canada. The single-year ANN method is recommended because this method captured flux variability better than the linear interpolation method (average R2 of 0.41 vs. 0.34). Annual N2O emission and annual bias resulting from linear and single-year ANN were compatible with each other when there were few and short gaps (i.e., percentage of missing values <30%). However, with longer gaps (>20 d), the bias error in annual fluxes varied between 0.082 and 0.344 kg N2O-N ha−1 for linear and 0.069 and 0.109 kg N2O-N ha−1 for single-year ANN. Hence, the single-year ANN with lower annual bias and stable approach over various years is recommended, if the appropriate driving inputs (i.e., soil temperature, soil water content, precipitation, N mineral content, and snow depth) needed for the ANN model are available.

Modelling of 10 Gbps Free Space Optics Communication Link Using Array of Receivers in Moderate and Harsh Weather Conditions

2017 ◽  
Vol 38 (3) ◽  
Author(s):  
Amit Gupta ◽  
Nagpal Shaina
Keyword(s):  
Free Space ◽  
Weather Conditions ◽  
Quality Data ◽  
Laser Source ◽  
Communication Link ◽  
Free Space Optical ◽  
Space Optics ◽  
Received Power ◽  
Adverse Weather ◽  
The Impact

AbstractIntersymbol interference and attenuation of signal are two major parameters affecting the quality of transmission in Free Space Optical (FSO) Communication link. In this paper, the impact of these parameters on FSO communication link is analysed for delivering high-quality data transmission. The performance of the link is investigated under the influence of amplifier in the link. The performance parameters of the link like minimum bit error rate, received signal power and Quality factor are examined by employing erbium-doped fibre amplifier in the link. The effects of amplifier are visualized with the amount of received power. Further, the link is simulated for moderate weather conditions at various attenuation levels on transmitted signal. Finally, the designed link is analysed in adverse weather conditions by using high-power laser source for optimum performance.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Investigating the Impact of Rainfall on Travel Speed

2014 ◽  
Vol 71 (3) ◽  
Author(s):  
Nordiana Mashros ◽  
Johnnie Ben-Edigbe ◽  
Hashim Mohammed Alhassan ◽  
Sitti Asmah Hassan
Keyword(s):  
Empirical Studies ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Travel Speed ◽  
Operating Conditions ◽  
Speed Reduction ◽  
Light Rain ◽  
Rainfall Condition ◽  
Adverse Weather ◽  
The Impact

The road network is particularly susceptible to adverse weather with a range of impacts when different weather conditions are experienced. Adverse weather often leads to decreases in traffic speed and subsequently affects the service levels. The paper is aimed at investigating the impact of rainfall on travel speed and quantifying the extent to which travel speed reduction occurs. Empirical studies were conducted on principle road in Terengganu and Johor, respectively for three months. Traffic data were collected by way of automatic traffic counter and rainfall data from the nearest raingauge station were supplied by the Department of Irrigation and Drainage supplemented by local survey data. These data were filtered to obtain traffic flow information for both dry and wet operating conditions and then were analyzed to see the effect of rainfall on percentile speeds. The results indicated that travel speed at 15th, 50th and 85th percentiles decrease with increasing rainfall intensities. It was observed that allpercentile speeds decreased from a minimum of 1% during light rain to a maximum of 14% during heavy rain. Based on the hypothesis that travel speed differ significantly between dry and rainfall condition; the study found substantial changes in percentile speeds and concluded that rainfalls irrespective of their intensities have significant impact on the travel speed.

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

2017 ◽  
Vol 2667 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Zihan Hong ◽  
Hani S. Mahmassani ◽  
Xiang Xu ◽  
Archak Mittal ◽  
Ying Chen ◽  
...  
Keyword(s):  
Traffic Management ◽  
Demand Management ◽  
Management Strategies ◽  
Weather Conditions ◽  
Active Transportation ◽  
Operational Conditions ◽  
Traffic Conditions ◽  
Adverse Weather ◽  
Traffic Performance ◽  
The Impact

This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

scholarly journals Assessing Machine Learning Models for Gap Filling Daily Rainfall Series in a Semiarid Region of Spain

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1158
Author(s):  
Juan Antonio Bellido-Jiménez ◽  
Javier Estévez Gualda ◽  
Amanda Penélope García-Marín
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Mean Bias Error ◽  
Semiarid Region ◽  
Bias Error ◽  
Spatial And Temporal Variability ◽  
Rainfall Time Series ◽  
Learning Models ◽  
Gap Filling ◽  
Machine Learning Models

The presence of missing data in hydrometeorological datasets is a common problem, usually due to sensor malfunction, deficiencies in records storage and transmission, or other recovery procedures issues. These missing values are the primary source of problems when analyzing and modeling their spatial and temporal variability. Thus, accurate gap-filling techniques for rainfall time series are necessary to have complete datasets, which is crucial in studying climate change evolution. In this work, several machine learning models have been assessed to gap-fill rainfall data, using different approaches and locations in the semiarid region of Andalusia (Southern Spain). Based on the obtained results, the use of neighbor data, located within a 50 km radius, highly outperformed the rest of the assessed approaches, with RMSE (root mean squared error) values up to 1.246 mm/day, MBE (mean bias error) values up to −0.001 mm/day, and R2 values up to 0.898. Besides, inland area results outperformed coastal area in most locations, arising the efficiency effects based on the distance to the sea (up to an improvement of 63.89% in terms of RMSE). Finally, machine learning (ML) models (especially MLP (multilayer perceptron)) notably outperformed simple linear regression estimations in the coastal sites, whereas in inland locations, the improvements were not such significant.

scholarly journals Cardiovascular risk assessment of the population under adverse weather conditions

2020 ◽  
Vol 99 (5) ◽  
pp. 488-492
Author(s):  
R. B. Tsallagova ◽  
O. I. Kopytenkova ◽  
Fatima K. Makoeva ◽  
A. R. Nanieva
Keyword(s):  
Negative Impact ◽  
Adult Population ◽  
Medical Science ◽  
Weather Conditions ◽  
Air Pressure ◽  
Medical Documentation ◽  
Atmospheric Air ◽  
Adverse Weather ◽  
The Impact ◽  
The City

Introduction. Climate change around the world determines the relevance of the study of its effects on the human health. Nowadays, due to the development of modern medical science, the methods of evidence-based analysis of negative impact of the environmental factors on the public health are being widely implemented into preventive medicine. Their use should make it possible to quantify the various risks with high confidence and to manage them effectively. Material and methods. The weather conditions of the territory of Vladikavkaz for a 15-years period (2001-2015) have been studied. On the basis of the data from the primary medical documentation of emergency medical care (EMC) for the same period, the health status of the adult population in the city has been studied. The impact of the meteorological elements under the study on the frequency of seeking EMC was estimated using the relative (RR) and population risks (Rpop) values. Results. For the city of Vladikavkaz (according to the medical classification of weather conditions), high air humidity is typical for 65% of the days in a year, low air velocity (less than 3 m/s) - 77% of the days in a year. Inter-day fluctuations in temperature and atmospheric air pressure, exceeding the optimal levels for the human body, are recorded more than in 30% of the days in a year. Discussion. The city of Vladikavkaz is characterized by windless, wet weather, with frequent inter-day fluctuations in temperature and atmospheric air pressure, which corresponds to the clinically irritating and acute types of weather. Conclusion. The calculations of the relative and the population risks of impact of meteorological changes on the developing of urgent cardiovascular conditions in the population of Vladikavkaz showed RR of combining two unfavourable weather factors to be of 1.081 (p <0.0001), and the Rpop increases by more than 3600 additional EMC calls due to cardiovascular pathology.

scholarly journals MATHEMATICAL MODEL OF IMPLEMENTATION OF FIRE TASKS BY TANK CREW ON THE BATTLEFIELD

2020 ◽  
Vol 2 (36) ◽  
pp. 63-69
Author(s):  
D. Khaustov ◽  
◽  
Ya. Khaustov ◽  
V. Sokolovskij ◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Markov Chain ◽  
Full Range ◽  
Weather Conditions ◽  
Observation System ◽  
Extended Model ◽  
Sequence Of Events ◽  
Functional Scheme ◽  
Adverse Weather ◽  
The Impact

Reconnaissance and defeat of enemy targets on the battlefield depends on the technical characteristics of surveillance and sighting devices, and it is important to ensure their effective operation, during both the day and night, especially in adverse weather conditions. Therefore, in the field of modernization and development of surveillance and sighting devices, by both domestic and foreign manufacturers, in recent years there has been a major trend towards the development of multi-channel sighting systems on armored vehicles. To determine the optimal parameters of the sighting systems of armored vehicle samples, it is necessary to develop a mathematical model that would take into account the entire process of target reconnaissance, including the impact of full range of natural and man-made obstacles, to determine the ways for the improvement of combat effectiveness of weapons samples. In this work, a mathematical model is developed for description of fire tasks execution on the battlefield by a tank crew, which is equipped with a multi-channel sight-monitoring system, to assess the work of the sighting system as whole as well as of each of its channels. The analysis of the functional scheme of solving the combat task shows that the stage of data acquisition should be divided into, at least three, separate states, namely: detection, recognition and identification such that the transitions between these states will be described by the corresponding probabilities. The developed model describes the sequence of events that in terms of probability theory form a Markov chain. The proposed analytical Markov model allows for modeling the solution of fire tasks by the tank crew equipped with a multi-channel sighting and observation system with separate consideration of the stages of target reconnaissance, its recognition and identification, which in turn are considered as separate states of the Markov chain. The model opens a possibility for the development of an extended model to determine a quantitative indicator to assess the effectiveness of a given sighting system in comparison with its analogs.

scholarly journals Influence of Overheating Phenomenon on Bitumen and Asphalt Mixture Properties

Materials ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 610 ◽  
Author(s):  
Michał Sarnowski ◽  
Karol Kowalski ◽  
Jan Król ◽  
Piotr Radziszewski
Keyword(s):  
Asphalt Mixture ◽  
Fatigue Cracking ◽  
Weather Conditions ◽  
Asphalt Mixtures ◽  
Heating Process ◽  
Test Results ◽  
Adverse Weather Conditions ◽  
Loss Of Resistance ◽  
Adverse Weather ◽  
The Impact

In the course of manufacturing, transport and installation, road bitumens and asphalt mixtures can be exposed to the impact of elevated process temperatures exceeding 240 °C. This mainly applies to the mixtures used for road pavements and bridge deck insulation during adverse weather conditions. The heating process should not change the basic and rheological properties of binders and the asphalt mixtures that to a degree cause the degradation of asphalt pavement durability. The work involved analyzing the properties of non-modified bitumens and SBS polymer modified bitumens, heated at temperatures of 200 °C, 250 °C and 300 °C for 1 h. Next, the asphalt mixtures were heated in the same temperatures. Based on the developed Overheating Degradation Index (ODI) it was demonstrated that polymer-modified bitumens were characterized by higher overheating sensitivity A(ODI) than non-modified bitumens, which was confirmed by mixture test results. Overheating limit temperatures T(ODI) were determined, which in the case of polymer-modified bitumens are up to 20 °C lower than for non-modified bitumens. When the temperature increases above T(ODI), loss of viscoelastic properties occurs in the material which causes, among other effects, a loss of resistance to fatigue cracking.

scholarly journals Cross-Comparison and Methodological Improvement in GPS Tomography

2019 ◽  
Vol 12 (1) ◽  
pp. 30 ◽  
Author(s):  
Hugues Brenot ◽  
Witold Rohm ◽  
Michal Kačmařík ◽  
Gregor Möller ◽  
André Sá ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Critical Role ◽  
Weather Conditions ◽  
Severe Weather ◽  
Research Station ◽  
Observational Technique ◽  
Independent Observations ◽  
Methodological Improvement ◽  
The Impact

GPS tomography has been investigated since 2000 as an attractive tool for retrieving the 3D field of water vapour and wet refractivity. However, this observational technique still remains a challenging task that requires improvement of its methodology. This was the purpose of this study, and for this, GPS data from the Australian Continuously Operating Research Station (CORS) network during a severe weather event were used. Sensitivity tests and statistical cross-comparisons of tomography retrievals with independent observations from radiosonde and radio-occultation profiles showed improved results using the presented methodology. The initial conditions, which were associated with different time-convergence of tomography inversion, play a critical role in GPS tomography. The best strategy can reduce the normalised root mean square (RMS) of the tomography solution by more than 3 with respect to radiosonde estimates. Data stacking and pseudo-slant observations can also significantly improve tomography retrievals with respect to non-stacked solutions. A normalised RMS improvement up to 17% in the 0–8 km layer was found by using 30 min data stacking, and RMS values were divided by 5 for all the layers by using pseudo-observations. This result was due to a better geometrical distribution of mid- and low-tropospheric parts (a 30% coverage improvement). Our study of the impact of the uncertainty of GPS observations shows that there is an interest in evaluating tomography retrievals in comparison to independent external measurements and in estimating simultaneously the quality of weather forecasts. Finally, a comparison of multi-model tomography with numerical weather prediction shows the relevant use of tomography retrievals to improving the understanding of such severe weather conditions.

scholarly journals Urban Road Crashes and Weather Conditions: Untangling the Effects

2019 ◽  
Vol 11 (11) ◽  
pp. 3176 ◽  
Author(s):  
António Lobo ◽  
Sara Ferreira ◽  
Isabel Iglesias ◽  
António Couto
Keyword(s):  
Negative Binomial ◽  
Weather Conditions ◽  
Crash Risk ◽  
Traffic Crash ◽  
Road Crashes ◽  
Road Users ◽  
Poisson Models ◽  
Adverse Weather ◽  
Single Vehicle ◽  
The Impact

Most previous studies show that inclement weather increases the risk of road users being involved in a traffic crash. However, some authors have demonstrated a little or even an opposite effect, observed both on crash frequency and severity. In urban roads, where a greater number of conflict points and heavier traffic represent a higher exposure to risk, the potential increase of crash risk caused by adverse weather deserves a special attention. This study investigates the impact of meteorological conditions on the frequency of road crashes in urban environment, using the city of Porto, Portugal as a case study. The weather effects were analyzed for different types of crashes: single-vehicle, multi-vehicle, property-damage-only, and injury crashes. The methodology is based on negative binomial and Poisson models with random parameters, considering the influence of daily precipitation and mean temperature, as well as the lagged effects of the precipitation accumulated during the previous month. The results show that rainy days are more prone to the occurrence of road crashes, although the past precipitation may attenuate such effect. Temperatures below 10 °C are associated with higher crash frequencies, complying with the impacts of precipitation in the context of the Portuguese climate characteristics.

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