Characterizing Dam Induced Flood at Downstream of a Hydel Project 

2021 ◽  
Author(s):  
Dipsikha Devi ◽  
Anupal Baruah ◽  
Arup Kumar Sarma
Keyword(s):  
Water Level ◽  
Short Duration ◽  
Flash Flood ◽  
Daily Basis ◽  
Low Flow ◽  
Mitigation Measures ◽  
Hydropower Project ◽  
River Bank ◽  
Operating Policy ◽  
The Impact

<p>Flooding due to sudden release from a hydropower dam during monsoon is becoming a serious concern for downstream locality, especially when there is lack of coordination between the dam authority and the Disaster Management Authority (DMA) at downstream. For hilly river, a disastrous flash flood is generally caused by short duration high intensity precipitation and a pondage hydropower project cannot attenuate such flood. Generally, reservoir simulation/optimization for a hydropower project is carried out on monthly, ten-daily or at best on daily basis to determine the best operating policy and to analyze impact of such operation on the flow scenario and therefore, in conventional analysis such flash flood event goes un-noticed. A detailed investigation of the downstream flooding is required before the construction of any hydropower project with at least on hourly basis to get insight into the impact of such inflow at downstream. Non-availability of short duration precipitation/flow data in interior project area, particularly in developing country hinder such analysis. Need and scope of such analysis is demonstrated by using a typical flow hydrograph of 48 hours, having two flood peaks, as inflow to the Lower Subansiri Hydroelectric Project (LSHP). The project is located in the Subansiri River, the largest tributary of the Brahmaputra River in India. Two operating policies; i) Standard Operating Policy (SOP) and ii) Dynamic Programming (DP) generated operating policy have been tested and both the polices have generated similar hourly flow time series of total reservoir outflow (spill + Release). These reservoir operation models have been coupled with the hydrodynamic model to route the hourly reservoir outflow from LSHP to a flood prone area located 13Km downstream of it. Post dam flood scenario thus generated is compared with the pre dam flood scenario by routing the same inflow hydrograph without considering the dam. As the river has an embankment, and flooding occurs only when the embankment fails, a specified water level at the downstream section has been considered as critical for flooding for the purpose of a comparative study.  For the considered inflow hydrograph, it is observed that the flood magnitude is not increased by the action of dam operation rather peaks get slightly attenuated. However, in natural condition without dam, flood rises gradually providing prior information to the locality and providing sufficient time for completing pre-disaster actions based on experience. With inclusion of dam, peak flow rises vary rapidly from a very low flow without showing any indication of flood beforehand and thus flood becomes more disastrous. Sudden fluctuation of water level can also cause failure of river bank and progressive bank failure can eventually cause the embankment to fail. The analysis has shown the possible impact of hydel project with more clarity to help disaster manager prepare mitigation measures in an informed way.</p>

Street Level Flood Monitoring and Warning System, a conceptual model using IoT: A Case of Surat City

2021 ◽  
Vol 14 (12) ◽  
pp. 55-65
Author(s):  
Anant Patel ◽  
Sanjay Yadav
Keyword(s):  
Developing Countries ◽  
Water Level ◽  
Warning System ◽  
Water Levels ◽  
Mitigation Measures ◽  
Developed Country ◽  
River Bank ◽  
Street Level ◽  
Flood Warning ◽  
Flood Monitoring

Most of the natural disasters are unpredictable, but the most frequent occurring catastrophic event over the globe is flood. Developing countries are severely affected by the floods because of the high frequencies of floods. The developing countries do not have good forecasting system compared to the developed country. The metro cities are also settled near the coast or river bank which are the most vulnerable places to floods. This study proposes plan for street level flood monitoring and warning system for the Surat city, India. Waterlogging happens in the low lying area of the Surat city due to heavy storm and heavy releases from the Ukai dam. The high releases from upstream Ukai dam and heavy rainfall resulted into flooding in the low lying area of the Surat city. This research proposed a wireless water level sensor network system for the street water level flood monitoring. The system is proposed to monitor the water levels of different areas of city through the wireless water level sensors as well as to capture live photos using CCTV camera. This will help authority not only to issue flood warning but also to plan flood mitigation measures and evacuation of people.

scholarly journals Numerical Investigation of a Flash Flood Process that Occurred in Zhongdu River, Sichuan, China

2021 ◽  
Vol 9 ◽  
Author(s):  
Qingyuan Yang ◽  
Tonghuan Liu ◽  
Jingjing Zhai ◽  
Xiekang Wang
Keyword(s):  
Sediment Transport ◽  
Water Level ◽  
Flash Flood ◽  
Peak Level ◽  
Field Studies ◽  
Bed Deformation ◽  
Bed Erosion ◽  
Set Up ◽  
Discharge Profile ◽  
The Impact

In 2018, a flash flood occurred in the Zhongdu river, which lies in Yibin, Sichuan province of China. The flood caused many casualties and significant damage to people living nearby. Due to the difficulty in predicting where and when flash floods will happen, it is nearly impossible to set up monitors in advance to detect the floods in detail. Field investigations are usually carried out to study the flood propagation and disaster-causing mechanism after the flood’s happening. The field studies take the relic left by the flash flood to deduce the peak level, peak discharge, bed erosion, etc. and further revel the mechanism between water and sediment transport during the flash flood This kind of relic-based study will generate bigger errors in regions with great bed deformation. In this study, we come up with numerical simulations to investigate the flash flood that happened in the Zhongdu river. The simulations are based on two-dimensional shallow water models coupled with sediment transport and bed deformation models. Based on the real water level and discharge profile measured by a hydrometric station nearby, the numerical simulation reproduced the flash flood in the valley. The results show the flood coverage, water level variation, and velocity distribution during the flood. The simulation offers great help in studying the damage-causing process. Furthermore, simulations without considering sediment transport are also carried out to study the impact of bed erosion and sedimentation. The study proved that, without considering bed deformation, the flood may be greatly underestimated, and the sediment lying in the valley has great impact on flood power.

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

2020 ◽  
Author(s):  
Lukman Olagoke ◽  
Ahmet E. Topcu
Keyword(s):  
Control Measure ◽  
Control Measures ◽  
World Health ◽  
Mitigation Measures ◽  
Effective Control ◽  
The World ◽  
Decision Making Processes ◽  
And Performance ◽  
Health Organization ◽  
The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

scholarly journals Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5213
Author(s):  
Chin-Shiuh Shieh ◽  
Wan-Wei Lin ◽  
Thanh-Tuan Nguyen ◽  
Chi-Hong Chen ◽  
Mong-Fong Horng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Denial Of Service ◽  
Gaussian Mixture ◽  
Mitigation Measures ◽  
Distribution Model ◽  
Ddos Attacks ◽  
Open Set ◽  
The Impact

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Assessing the Preparedness of Healthcare Facilities for Disasters and Emergencies in Damghan, Iran

2021 ◽  
pp. 1-7
Author(s):  
Behrad Pourmohammadi ◽  
Ahad Heydari ◽  
Farin Fatemi ◽  
Ali Modarresi
Keyword(s):  
Health Care ◽  
Cross Sectional Study ◽  
Health Care Facilities ◽  
Mitigation Measures ◽  
Health Staff ◽  
Cross Sectional ◽  
Healthcare Facilities ◽  
Wide Range ◽  
Care Facilities ◽  
The Impact

Abstract Objectives: Iran is exposed to a wide range of natural and man-made hazards. Health-care facilities can play a significant role in providing life-saving measures in the minutes and hours immediately following the impact or exposure. The aim of this study was to determine the preparedness of health-care facilities in disasters and emergencies. Methods: This cross-sectional study was conducted in Damghan, Semnan Province, in 2019. The samples consisted of all the 11 health-care facilities located in Damghan County. A developed checklist was used to collect the data, including 272 questions in 4 sections: understanding threatening hazards, functional, structural, and nonstructural vulnerability of health-care facilities. The data were analyzed using SPSS 21. Results: The results revealed that the health-care facilities were exposed to 22 different natural and man-made hazards throughout the county. The total level of preparedness of the health-care centers under assessment was 45.8%. The average functional, structural, and nonstructural vulnerability was assessed at 49.3%, 31.6%, and 56.4%, respectively. Conclusions: Conducting mitigation measures is necessary for promoting the functional and structural preparedness. Disaster educational programs and exercises are recommended among the health staff in health-care facilities.

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