scholarly journals A GIS-based monitoring and early warning system for cover-collapse sinkholes in karst terrane in Wuhan, China

2017 ◽  
Author(s):  
Li Xueping ◽  
Xiao Shangde ◽  
Tang Huiming ◽  
Peng Jinsheng
Keyword(s):  
Plastic Zone ◽  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Underground Water ◽  
Water Levels ◽  
Monitoring Data ◽  
Karst Collapse ◽  
Warning Model

Abstract. To reduce disastrous losses caused by karst collapse especially in urban areas, it is important to establish an early warning system utilizing monitoring data. Three major aspects have been monitored based upon engineering geological conditions and characteristics of karst collapse processes in Wuhan, China: changes in surface soil, soil deformation, and groundwater levels. Measurements have been recorded of: (1) soil pressure, (2) ground-penetrating radar images, (3) underground water levels, (4) ground water levels, (5) rainfall, (6) cracking, (7) ground deformation, and (8) water level in monitored wells. This paper has selected geological radar cross-sectional data and underground water level monitoring data to obtain criteria for hydraulic gradient warning, geological radar warning and plastic zone warning based upon these monitoring data and wider knowledge of karst collapse in Wuhan. A comprehensive warning system has been developed on a MAPGIS platform, employing monitoring data in Microsoft Excel format and Microsoft Visual C++ development tools. Three warning levels are adopted by the system: safe, becoming dangerous, and dangerous; indicated in green, yellow and red respectively on hazard maps. The system automatically undertakes processes of data management and model calculation leading to geo-hazard warning map generation. Using monitoring data collected in the first six months of 2011 at Wuhan, the system has established a hydraulic gradient model, plastic zone warning model, geological radar warning model, and a comprehensive early warning model; and has been shown to be an effective method of providing karst collapse warning.

scholarly journals Can an early-warning system help minimize the impacts of coastal storms? A case study of the 2012 Halloween storm, northern Italy

2016 ◽  
Vol 16 (1) ◽  
pp. 209-222 ◽  
Author(s):  
M. D. Harley ◽  
A. Valentini ◽  
C. Armaroli ◽  
L. Perini ◽  
L. Calabrese ◽  
...  
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Numerical Models ◽  
Warning System ◽  
Northern Italy ◽  
Water Levels ◽  
Storm Event ◽  
Forecast Performance ◽  
High Hazard

Abstract. The Emilia-Romagna early-warning system (ER-EWS) is a state-of-the-art coastal forecasting system that comprises a series of numerical models (COSMO, ROMS, SWAN and XBeach) to obtain a daily 3-day forecast of coastal storm hazard at eight key sites along the Emilia-Romagna coastline (northern Italy). On the night of 31 October 2012, a major storm event occurred that resulted in elevated water levels (equivalent to a 1-in-20- to 1-in-50-year event) and widespread erosion and flooding. Since this storm happened just 1 month prior to the roll-out of the ER-EWS, the forecast performance related to this event is unknown. The aim of this study was to therefore reanalyse the ER-EWS as if it had been operating a day before the event and determine to what extent the forecasts may have helped reduce storm impacts. Three different reanalysis modes were undertaken: (1) a default forecast (DF) mode based on 3-day wave and water-level forecasts and default XBeach parameters; (2) a measured offshore (MO) forecast mode using wave and water-level measurements and default XBeach parameters; and (3) a calibrated XBeach (CX) mode using measured boundary conditions and an optimized parameter set obtained through an extensive calibration process. The results indicate that, while a "code-red" alert would have been issued for the DF mode, an underprediction of the extreme water levels of this event limited high-hazard forecasts to only two of the eight ER-EWS sites. Forecasts based on measured offshore conditions (the MO mode) more-accurately indicate high-hazard conditions for all eight sites. Further considerable improvements are observed using an optimized XBeach parameter set (the CX mode) compared to default parameters. A series of what-if scenarios at one of the sites show that artificial dunes, which are a common management strategy along this coastline, could have hypothetically been constructed as an emergency procedure to potentially reduce storm impacts.

scholarly journals Threshold Definition for Monitoring Gapa Landslide under Large Variations in Reservoir Level Using GNSS

2021 ◽  
Vol 13 (24) ◽  
pp. 4977
Author(s):  
Shuangshuang Wu ◽  
Xinli Hu ◽  
Wenbo Zheng ◽  
Matteo Berti ◽  
Zhitian Qiao ◽  
...  
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Water Levels ◽  
Deformation Model ◽  
Warning Systems ◽  
Velocity Threshold ◽  
Landslide Deformation

The triggering threshold is one of the most important parameters for landslide early warning systems (EWSs) at the slope scale. In the present work, a velocity threshold is recommended for an early warning system of the Gapa landslide in Southwest China, which was reactivated by the impoundment of a large reservoir behind Jinping’s first dam. Based on GNSS monitoring data over the last five years, the velocity threshold is defined by a novel method, which is implemented by the forward and reverse double moving average of time series. As the landslide deformation is strongly related to the fluctuations in reservoir water levels, a crucial water level is also defined to reduce false warnings from the velocity threshold alone. In recognition of the importance of geological evolution, the evolution process of the Gapa landslide from topping to sliding is described in this study to help to understand its behavior and predict its potential trends. Moreover, based on the improved Saito’s three-stage deformation model, the warning level is set as “attention level”, because the current deformation stage of the landslide is considered to be between the initial and constant stages. At present, the early warning system mainly consists of six surface displacement monitoring sites and one water level observation site. If the daily recorded velocity in each monitoring site exceeds 4 mm/d and, meanwhile, the water level is below 1820 m above sea level (asl), a warning of likely landslide deformation accelerations will be released by relevant monitoring sites. The thresholds are always discretely exceeded on about 3% of annual monitoring days, and they are most frequently exceeded in June (especially in mid-June). The thresholds provide an efficient and effective way for judging accelerations of this landslide and are verified by the current application. The work presented provides critical insights into the development of early warning systems for reservoir-induced large-scale landslides.

Web-Based Surface Level Measuring System Employing Ultrasonic Sensors and GSM/GPRS-Based Communication

2015 ◽  
Vol 771 ◽  
pp. 92-95 ◽  
Author(s):  
Muhammad Miftahul Munir ◽  
Rahmat Awaludin Salam ◽  
Eko Widiatmoko ◽  
Yundi Supriadani ◽  
Andri Rahmadhani ◽  
...  
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Water Surface ◽  
Warning System ◽  
Water Levels ◽  
Measuring System ◽  
Web Based ◽  
Surface Level

Water surface level should get special attention as water can cause disasters such as flood when its surface exceeds a certain level. A real time early warning system to monitor water surface level is necessary for avoiding severe effects of flood to human life. A web-based water level measuring system using an ultrasonic sensor can be an alternative choice for developing the early warning system. It is known that the system has advantages in the installation and maintenance compared to other systems. This paper discusses the design of a water level measuring system integrated with an internet web server. Ultrasonic sensors are used to measure the water surface level. A GSM / GPRS-based communication system is applied for sending measured water levels to a web server. The results indicate that the measurement data are in accordance with the water levels manually obtained. The results also show that the system works real time.

scholarly journals The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

2021 ◽  
Vol 13 (2) ◽  
pp. 566
Author(s):  
Nelly Florida Riama ◽  
Riri Fitri Sari ◽  
Henita Rahmayanti ◽  
Widada Sulistya ◽  
Mohamad Husein Nurrahmat
Keyword(s):  
Path Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Coastal Flooding ◽  
Coastal Areas ◽  
Public Acceptance ◽  
Community Attitudes ◽  
Early Warning Model ◽  
Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

scholarly journals Internet-Based Flood Detection System (Iot) and Telegram Messenger Using Mcu Node and Water Level Sensor

2020 ◽  
Vol 4 (1) ◽  
pp. 230-235
Author(s):  
Novianda Nanda Nanda ◽  
Rizalul Akram ◽  
Liza Fitria
Keyword(s):  
Water Level ◽  
Early Warning ◽  
High Intensity ◽  
Rainy Season ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Level Sensor ◽  
Flood Detection ◽  
Internet Of Thing

During the rainy season, several regions in Indonesia experienced floods even to the capital of Indonesia also flooded. Some of the causes are the high intensity of continuous rain, clogged or non-smooth drainage, high tides to accommodate the flow of water from rivers, other causes such as forest destruction, shallow and full of garbage and other causes. Every flood disaster comes, often harming the residents who experience it. The late anticipation from the community and the absence of an early warning system or information that indicates that there will be a flood so that the community is not prepared to face floods that cause a lot of losses. Therefore it is necessary to have a detection system to provide early warning if floods will occur, this is very important to prevent material losses from flooded residents. From this problem the researchers designed an internet-based flood detection System of Things (IoT). This tool can later be controlled via a smartphone remotely and can send messages Telegram messenger to citizens if the detector detects a flood will occur.Keywords: Flooding, Smartphone, Telegram messenger, Internet of Thing (IoT).

scholarly journals GPS water level measurements for Indonesia's Tsunami Early Warning System

2011 ◽  
Vol 11 (3) ◽  
pp. 741-749 ◽  
Author(s):  
T. Schöne ◽  
W. Pandoe ◽  
I. Mudita ◽  
S. Roemer ◽  
J. Illigner ◽  
...  
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Deep Ocean ◽  
Ocean Bottom ◽  
Tsunami Early Warning ◽  
Tsunami Waves ◽  
Gps Technology ◽  
Water Level Measurements

Abstract. On Boxing Day 2004, a severe tsunami was generated by a strong earthquake in Northern Sumatra causing a large number of casualties. At this time, neither an offshore buoy network was in place to measure tsunami waves, nor a system to disseminate tsunami warnings to local governmental entities. Since then, buoys have been developed by Indonesia and Germany, complemented by NOAA's Deep-ocean Assessment and Reporting of Tsunamis (DART) buoys, and have been moored offshore Sumatra and Java. The suite of sensors for offshore tsunami detection in Indonesia has been advanced by adding GPS technology for water level measurements. The usage of GPS buoys in tsunami warning systems is a relatively new approach. The concept of the German Indonesian Tsunami Early Warning System (GITEWS) (Rudloff et al., 2009) combines GPS technology and ocean bottom pressure (OBP) measurements. Especially for near-field installations where the seismic noise may deteriorate the OBP data, GPS-derived sea level heights provide additional information. The GPS buoy technology is precise enough to detect medium to large tsunamis of amplitudes larger than 10 cm. The analysis presented here suggests that for about 68% of the time, tsunamis larger than 5 cm may be detectable.

Early-Warning Model for TECC Based on Gray Neural Network - Taking Laolongtou Scenic Area as an Example

2013 ◽  
Vol 397-400 ◽  
pp. 2435-2438
Author(s):  
Xiu Ping Yang ◽  
Er Chao Li
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Historical Data ◽  
Fuzzy Inference ◽  
Warning System ◽  
Forecasting Model ◽  
Error Signal ◽  
Single Feature ◽  
Warning Model

Based on fuzzy inference and gray neural network, indexes of early-warning system of carrying capacity in scenic spots is established and extract fuzzy rules based on historical data, simulate the early-warning system based on fuzzy inference, gray forecasting model is built for single feature index respectively, add a compensated error based on neural network. The prediction value equals to the output value of grey neural network model plus the compensated error signal. At last, takes Laolongtou scenic area as an example.

Development of early warning system for linear engineering - a case study on highway in Sichuan, China

2020 ◽  
Author(s):  
Ruihua Xiao
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Highway Safety ◽  
Warning Systems ◽  
Safety Control ◽  
Early Warning Model ◽  
Landslide Early Warning ◽  
Warning Model

<p>For the recent years, highway safety control under extreme natural hazards in China has been facing critical challenges because of the latest extreme climates. Highway is a typical linear project, and neither the traditional single landslide monitoring and early warning model entirely dependent on displacement data, nor the regional meteorological early warning model entirely dependent on rainfall intensity and duration are suitable for it. In order to develop an efficient early warning system for highway safety, the authors have developed an early warning method based on both monitoring data obtained by GNSS and Crack meter, and meteorological data obtained by Radar. This early-warning system is not each of the local landslide early warning systems (Lo-LEWSs) or the territorial landslide early warning systems (Te-LEWSs), but a new system combining both of them. In this system, the minimum warning element is defined as the slope unit which can connect a single slope to the regional ones. By mapping the regional meteorological warning results to each of the slope units, and extending the warning results of the single landslides to the similar slope units, we can realize the organic combination of the two warning methods. It is hopeful to improve the hazard prevention and safety control for highway facilities during critical natural hazards with the progress of this study.</p>

Design and Application of Risk Early-Warning System of Electric Power Engineering Projects - A Case Study of Combined Heat and Power Project in Western China

2013 ◽  
Vol 278-280 ◽  
pp. 2113-2117
Author(s):  
Qing Miao ◽  
Zhen Tao Xia
Keyword(s):  
Electric Power ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Power Engineering ◽  
Western China ◽  
Electric Power Engineering ◽  
Warning Model ◽  
Fuzzy Comprehensive Appraisal ◽  
Engineering Projects

Based on the theories of fuzzy set and fuzzy conversion, the method of fuzzy comprehensive appraisal is a decision-making process which combines qualitative analysis and quantitative analysis and can be used to forecast risk of electric power engineering projects. Using the method of AHP to establish risk early-warning indicators system and method of fuzzy comprehensive appraisal to establish risk early-warning model, the paper constitutes risk early-warning system of electric power engineering projects. A case from western China is applied to prove the validity of the risk early-warning system.

scholarly journals Sepsis prediction via the clinical data integration system in the ICU

2021 ◽  
Author(s):  
Qiyu Chen ◽  
Ranran Li ◽  
Zhizhe Lin ◽  
Zhiming Lai ◽  
Peijiao Xue ◽  
...  
Keyword(s):  
Data Integration ◽  
Medical Devices ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Integration System ◽  
Integration Platform ◽  
Early Warning Model ◽  
Data Integration System ◽  
Warning Model

Sepsis is an essential issue in critical care medicine, and early detection and intervention are key for survival. We established the sepsis early warning system based on a data integration platform that can be implemented in ICU. The sepsis early warning module can detect the onset of sepsis 5 hours proceeding, and the data integration platform integrates, standardizes, and stores information from different medical devices, making the inference of the early warning module possible. Our best early warning model got an AUC of 0.9833 in the task of detect sepsis in 4 hours proceeding on the open-source database. Our data integration platform has already been operational in a hospital for months.

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