scholarly journals On selection of the optimal data time interval for real-time hydrological forecasting

2013 ◽  
Vol 17 (9) ◽  
pp. 3639-3659 ◽  
Author(s):  
J. Liu ◽  
D. Han
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Time Interval ◽  
Rainfall Runoff ◽  
Time Intervals ◽  
Hydrological Forecasting ◽  
Forecast Lead Time ◽  
The Future ◽  
The Impact ◽  
Selection Of

Abstract. With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is which suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall–runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet transform tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall–runoff modelling due to the catchment low-pass-filtering effect. To further investigate the impact of a data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the probability distributed model (PDM) with a real-time updating scheme, the autoregressive moving-average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15, 30, 45, 60, 90 and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications of the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the future is that higher sampling rates will become more widespread, and there is an urgent need for hydrologists both in academia and in the field to realise the significance of the data time interval issue. It is important that more case studies in different catchments with various hydrological forecasting systems are explored in the future to further verify and improve the proposed hypothetical pattern.

scholarly journals On selection of the optimal data time interval for real-time hydrological forecasting

2012 ◽  
Vol 9 (9) ◽  
pp. 10829-10875
Author(s):  
J. Liu ◽  
D. Han
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Time Interval ◽  
Rainfall Runoff ◽  
Time Intervals ◽  
Hydrological Forecasting ◽  
Forecast Lead Time ◽  
The Future ◽  
The Impact ◽  
Selection Of

Abstract. With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is what suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall-runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet decomposition tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall-runoff modelling due to the catchment low-pass filtering effect. To further investigate the impact of data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the Probability Distributed Model (PDM) with a real-time updating scheme, the autoregressive-moving average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15 min, 30 min, 45 min, 60 min, 90 min and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications on the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the trend in the future is that higher sampling rates will become widespread and there is an urgent need for both academic and practising hydrologists to realise the significance of the data time interval issue. It is important that more case studies in different catchments with various hydrological forecasting models should be explored in the future to further verify and improve the proposed hypothetical pattern.

A Pandemic of Violence? The Impact of COVID-19 on Conflict

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Sara M.T. Polo
Keyword(s):  
Spatial Distribution ◽  
Real Time ◽  
Case Studies ◽  
Armed Conflict ◽  
Event Data ◽  
Social Unrest ◽  
Johns Hopkins University ◽  
The World ◽  
Political Advantage ◽  
The Impact

AbstractThis article examines the impact and repercussions of the COVID-19 pandemic on patterns of armed conflict around the world. It argues that there are two main ways in which the pandemic is likely to fuel, rather than mitigate, conflict and engender further violence in conflict-prone countries: (1) the exacerbating effect of COVID-19 on the underlying root causes of conflict and (2) the exploitation of the crisis by governments and non-state actors who have used the coronavirus to gain political advantage and territorial control. The article uses data collected in real-time by the Armed Conflict Location & Event Data Project (ACLED) and the Johns Hopkins University to illustrate the unfolding and spatial distribution of conflict events before and during the pandemic and combine this with three brief case studies of Afghanistan, Nigeria, and Libya. Descriptive evidence shows how levels of violence have remained unabated or even escalated during the first five months of the pandemic and how COVID-19-related social unrest has spread beyond conflict-affected countries.

Abstract 13248: The Impact of Instructions for Covering the Mouth and the Nose on Dispatch-Assisted Cardiopulmonary Resuscitation

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Hidetada Fukushima ◽  
Hideki Asai ◽  
Koji Yamamoto ◽  
Yasuyuki Kawai
Keyword(s):  
Cardiopulmonary Resuscitation ◽  
Median Time ◽  
Chest Compression ◽  
Fire Department ◽  
Emergency Call ◽  
Time Interval ◽  
Time Intervals ◽  
Median Time Interval ◽  
Study Results ◽  
The Impact

Introduction: Under the SARS-CoV-2 pandemic, rescuers are recommended to cover their mouth and nose with a facemask or a cloth as well as victim’s mouth and nose when performing cardiopulmonary resuscitation (CPR). However, its impact on dispatch-assisted CPR (DACPR) has not been investigated well. Hypothesis: DACPR including the instruction for covering the rescuer’s and the victim’s mouth and nose can significantly delay the start of the first chest compression. Methods: We retrospectively analyzed DACPR records of the Nara Wide Area Fire Department, covering population of 853,000/3361km 2 , in Japan. We investigated the key time intervals of 505 DACPR records between May 2020 and March 2021. We also compared the results to that of the same period in 2019 (535 records). Results: Dispatchers failed to provide mask instruction in 322 cases (63.8%). The median time interval from the emergency call and the start of CPR instruction was longer in 2020 (197 seconds vs 190 seconds, p=0.641). The time to the first chest compression was also delayed in 2020 (264 seconds vs 246 seconds, p=0.015). Among the cases that dispatchers successfully provided mask instruction (183 cases, 36.2%), median time intervals to the start of instruction and the first chest compression were relatively faster than cases without mask instruction (177 seconds vs 211 seconds and 254 seconds vs 269.5 seconds, respectively). Conclusions: Dispatchers failed to provide mask instruction in the majority of CA cases. However, our study results indicate that the impact of mask instruction on DACPR can be minor in terms of immediate CPR provision.

IIOT and Real Time Data Analytics - Maximizing the Impact on Safety and Productivity

2021 ◽  
Author(s):  
Ryan Daher ◽  
Nesma Aldash
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Data Analytics ◽  
Contact Tracing ◽  
Time Data ◽  
Seamless Integration ◽  
Location Services ◽  
Industrial Internet ◽  
Potential Applications ◽  
The Impact

Abstract With the global push towards Industry 4.0, a number of leading companies and organizations have invested heavily in Industrial Internet of Things (IIOT's) and acquired a massive amount of data. But data without proper analysis that converts it into actionable insights is just more information. With the advancement of Data analytics, machine learning, artificial intelligence, numerous methods can be used to better extract value out of the amassed data from various IIOTs and leverage the analysis to better make decisions impacting efficiency, productivity, optimization and safety. This paper focuses on two case studies- one from upstream and one from downstream using RTLS (Real Time Location Services). Two types of challenges were present: the first one being the identification of the location of all personnel on site in case of emergency and ensuring that all have mustered in a timely fashion hence reducing the time to muster and lessening the risks of Leaving someone behind. The second challenge being the identification of personnel and various contractors, the time they entered in productive or nonproductive areas and time it took to complete various tasks within their crafts while on the job hence accounting for efficiency, productivity and cost reduction. In both case studies, advanced analytics were used, and data collection issues were encountered highlighting the need for further and seamless integration between data, analytics and intelligence is needed. Achievements from both cases were visible increase in productivity and efficiency along with the heightened safety awareness hence lowering the overall risk and liability of the operation. Novel/Additive Information: The results presented from both studies have highlighted other potential applications of the IIOT and its related analytics. Pertinent to COVID-19, new application of such approach was tested in contact tracing identifying workers who could have tested positive and tracing back to personnel that have been in close proximity and contact therefore reducing the spread of COVID. Other application of the IIOT and its related analytics has also been tested in crane, forklift and heavy machinery proximity alert reducing the risk of accidents.

Case Studies for the Successful Deployment of Wells Augmented Stuck Pipe Indicator in Wells Real Time Centre

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Tengku Ezharuddin Tengku Bidin ◽  
...  
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Drilling Fluid ◽  
Drill String ◽  
Drilling Operation ◽  
Cost Impact ◽  
Drilling Parameters ◽  
The Impact ◽  
Productive Time ◽  
Specific Challenge

Abstract The restriction or inability of the drill string to reciprocate or rotate while in the borehole is commonly known as a stuck pipe. This event is typically accompanied by constraints in drilling fluid flow, except for differential sticking. The stuck pipe can manifest based on three different mechanisms, i.e. pack-off, differential sticking, and wellbore geometry. Despite its infrequent occurrence, non-productive time (NPT) events have a massive cost impact. Nevertheless, stuck pipe incidents can be evaded with proper identification of its unique symptoms which allows an early intervention and remediation action. Over the decades, multiple analytical studies have been attempted to predict stuck pipe occurrences. The latest venture into this drilling operational challenge now utilizes Machine Learning (ML) algorithms in forecasting stuck pipe risk. An ML solution namely, Wells Augmented Stuck Pipe Indicator (WASP), is developed to tackle this specific challenge. The solution leverages on real-time drilling database and supplementary engineering design information to estimate proxy drilling parameters which provide active and impartial pattern recognition of prospective stuck pipe events. The solution is built to assist Wells Real Time Centre (WRTC) personnel in proactively providing a holistic perspective in anticipating potential anomalies and recommending remedial countermeasures before incidents happen. Several case studies are outlined to exhibit the impact of WASP in real-time drilling operation monitoring and intervention where WASP is capable to identify stuck pipe symptoms a few hours earlier and provide warnings for stuck pipe avoidance. The presented case studies were run on various live wells where restrictions are predicted stands ahead of the incidents. Warnings and alarms were generated, allowing further analysis by the personnel to verify and assess the situation before delivering a precautionary procedure to the rig site. The implementation of the WASP will reduce analysis time and provide timely prescriptive action in the proactive real-time drilling operation monitoring and intervention hub, subsequently creating value through cost containment and operational efficiency.

scholarly journals Clinical Outcome of Spine Surgery Complicated by Accidental Dural Tears: Meta-Analysis of the Literature

2020 ◽  
pp. 219256822091487
Author(s):  
Zeiad A. F. Alshameeri ◽  
El-Nasri Ahmed ◽  
Vinay Jasani
Keyword(s):  
Back Pain ◽  
Clinical Outcome ◽  
Spinal Surgery ◽  
Short Form ◽  
Meta Analysis ◽  
Systemic Review ◽  
Time Interval ◽  
Time Intervals ◽  
Dural Tears ◽  
The Impact

Study Design: Systemic review and meta-analysis. Objectives: Several studies have reported the impact of accidental dural tears (DT) on the outcome of spinal surgery, some with conflicting results. Therefore, the aim of this study was to carry out a systemic review and meta-analysis of the literature to establish the overall clinical outcome of spinal surgery following accidental DT. Method: A systemic literature search was carried out. Postoperative improvement in Oswestry Disability Index (ODI), Short-Form 36 survey (SF36), leg pain visual analogue scale (VAS), and back pain VAS were compared between patients with and without DT at different time intervals. Results: Eleven studies were included in this meta-analysis. There was a slightly better improvement in ODI and leg VAS score (standardized mean difference of −0.06, 95% confidence interval [CI] −0.12 to −0.01, and −0.06, 95% CI −0.09 to −0.02, respectively) in patients without DT at 12 months postsurgery, but this effect was not demonstrated at any other time intervals up to 4 years. There were no differences in the overall SF36 (function) score at any time interval or back pain VAS at 12 months. Conclusion: Based on this study, accidental DT did not have an overall significant adverse impact on the short-term clinical outcome of spinal surgery. More studies are needed to address the long-term impact and other outcome measures including other immediate complications of DT.

scholarly journals Advancing from Convection-Allowing NWP to Warn-on-Forecast: Evidence of Progress

2018 ◽  
Vol 33 (2) ◽  
pp. 599-607 ◽  
Author(s):  
John R. Lawson ◽  
John S. Kain ◽  
Nusrat Yussouf ◽  
David C. Dowell ◽  
Dustan M. Wheatley ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Radar Data ◽  
Model Systems ◽  
Deterministic Systems ◽  
Forecast Lead Time ◽  
The Impact

Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.

Impact of Climate Change on River Flows in the Black Volta River

2014 ◽  
Vol 9 (4) ◽  
pp. 432-442 ◽  
Author(s):  
Nobuhiko Sawai ◽  
◽  
Kenichiro Kobayashi ◽  
Apip ◽  
Kaoru Takara ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Future Climate ◽  
Precipitation Pattern ◽  
Present Climate ◽  
Rainfall Runoff ◽  
Maximum Rainfall ◽  
Impact Of Climate Change ◽  
The Future ◽  
The Impact

This paper assesses the impact of climate change in the Black Volta River by using data output from the atmospheric general circulation model with a 20-km resolution (AGCM20) through the Japanese Meteorological Agency (JMA) and the Meteorological Research Institute (MRI). The Black Volta, which flows mainly in Burkina Faso and Ghana in West Africa, is a major tributary of the Volta River. The basin covers 142,056 km2 and has a semi-arid tropical climate. Before applying AGCM20 output to a rainfall–runoff model, the performance of the AGCM20 rainfall data is investigated by comparing it with the observed rainfall in the Black Volta Basin. To assess the possible impact of rainfall change on river flow, a kinematic wave model, which takes into consideration saturated and unsaturated subsurface soil zones, was performed. The rainfall analysis shows that, the correlation coefficient of the monthly rainfall between the observed rainfall and AGCM20 for the present climate (1979–2004) is 0.977. In addition, the analysis shows that AGCM20 overestimates precipitation during the rainy season and underestimates the dry season for the present climate. The analysis of the AGCM20 output shows the precipitation pattern change in the future (2075–2099). In the future, precipitation is expected to increase by 3%, whereas evaporation and transpiration are expected to increase by 5% and by 8%, respectively. Also, daily maximum rainfall is expected to be 20 mm, or 60%, higher. Thus, the future climate in this region is expected to be more severe. The rainfall–runoff simulation is successfully calibrated at the Bamboi discharge gauging station in the Black Volta fromJune 2000 to December 2000 with 0.72 of the Nash–Sutcliffe model efficiency index. The model is applied with AGCM20 outputs for the present climate (1979–2004) and future climate (2075–2099). The results indicate that future discharge will decrease from January to July at the rate of the maximum of 50% and increase fromAugust to December at the rate of the maximumof 20% in the future. Therefore, comprehensive planning for both floods and droughts are urgently needed in this region.

Competing Swarms of Autonomous Vehicles: Intruders Versus Guardians

2017 ◽  
Author(s):  
Daigo Shishika ◽  
Katarina Sherman ◽  
Derek A. Paley
Keyword(s):  
Protected Area ◽  
Autonomous Vehicles ◽  
Payoff Function ◽  
Time Interval ◽  
Design Strategies ◽  
Time Intervals ◽  
Aerial Vehicles ◽  
Zero Sum ◽  
The Impact ◽  
Probability Of Capture

We consider a competition between two swarms of aerial vehicles, where multiple intruder vehicles try to approach and then leave an area that multiple guardian vehicles are protecting. Pre-existing swarming strategies for the guardians to maximize the probability of capturing a single intruder are summarized. This work considers the case where multiple intruders approach the protected area sequentially with varied time intervals, to study the impact of intrusion frequency on the probability of capture. In addition, we formulate a payoff function treating the competition as a zero-sum game, and use this function to design strategies for the intruders, i.e., how to optimize the time interval between intrusions. We propose an intrusion strategy and demonstrate its performance with numerical simulations.

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