scholarly journals The world should establish an early warning system for new viral infectious diseases by space‐weather monitoring

MedComm ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 423-426
Author(s):  
Jiangwen Qu ◽  
N.Chandra Wickramasinghe
Keyword(s):  
Infectious Diseases ◽  
Space Weather ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
The World

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

scholarly journals IMPROVING MODIFIED ICOLD METHOD WITH LOSS OF LIFE INDEX FOR DAM SAFETY RISK ASSESSMENT

2020 ◽  
Vol 11 (2) ◽  
pp. 79-93
Author(s):  
A. Henrianto ◽  
R.W. Triweko ◽  
D. Yudianto
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Action Plan ◽  
Risk Index ◽  
Warning System ◽  
Regression Equation ◽  
Dam Safety ◽  
Security Risks ◽  
The World ◽  
Level Of Risk

This research paper explains the results of the prediction analysis of the number of lives lost in the event of a catastrophic dam collapse in Indonesia as a further consideration in assessing the level of risk of dam safety. The proposed procedure is to make a new prediction index of the number of lives lost (LoL) as the development of a risk index of evacuation requirements from Risk Affected Populations (PENRIS), on the Modified ICOLD Method which is always used in Indonesia. This study, resulting in a regression equation as a correlation between PENRIS and LoL, takes its source from various catastrophic dam collapse events that have occurred in the world including Indonesia. Furthermore the regression equation is integrated with the standard determination of the level of risk of dam safety used in Indonesia and the world, for conditions with and without a disaster early warning system based on the Graham formula (2010). Further analysis of the Emergency Action Plan (EAP or RTD) of 16 dams in Indonesia as a sample, gives an indication that the implementation of an early warning system will reduce the amount of LoL by almost 100% if implemented according to design. This research, with its focus on developing a prediction index for the number of LoL, proves that in Indonesia, where there are still many dams eventhough they already have RTDs, and have not conducted a disaster-based space arrangement based on predicted LoL numbers,the reduction in the value of dam security risks can only be optimal in the range of 50 % of the total dam studied.

scholarly journals Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1616 ◽  
Author(s):  
Abhirup Dikshit ◽  
Raju Sarkar ◽  
Biswajeet Pradhan ◽  
Saroj Acharya ◽  
Kelzang Dorji
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Rainfall Duration ◽  
The World ◽  
Landslide Data

Consistently over the years, particularly during monsoon seasons, landslides and related geohazards in Bhutan are causing enormous damage to human lives, property, and road networks. The determination of thresholds for rainfall triggered landslides is one of the most effective methods to develop an early warning system. Such thresholds are determined using a variety of rainfall parameters and have been successfully calculated for various regions of the world at different scales. Such thresholds can be used to forecast landslide events which could help in issuing an alert to civic authorities. A comprehensive study on the determination of rainfall thresholds characterizing landslide events for Bhutan is lacking. This paper focuses on defining event rainfall–duration thresholds for Chukha Dzongkhag, situated in south-west Bhutan. The study area is chosen due to the increase in frequency of landslides during monsoon along Phuentsholing-Thimphu highway, which passes through it and this highway is a major trade route of the country with the rest of the world. The present threshold method revolves around the use of a power law equation to determine event rainfall–duration thresholds. The thresholds have been established using available rainfall and landslide data for 2004–2014. The calculated threshold relationship is fitted to the lower boundary of the rainfall conditions leading to landslides and plotted in logarithmic coordinates. The results show that a rainfall event of 24 h with a cumulated rainfall of 53 mm can cause landslides. Later on, the outcome of antecedent rainfall varying from 3–30 days was also analysed to understand its effect on landslide incidences based on cumulative event rainfall. It is also observed that a minimum 10-day antecedent rainfall of 88 mm and a 20-day antecedent rainfall of 142 mm is required for landslide occurrence in the area. The thresholds presented can be improved with the availability of hourly rainfall data and the addition of more landslide data. These can also be used as an early warning system especially along the Phuentsholing–Thimphu Highway to prevent any disruptions of trade.

scholarly journals Comparative Study on Early Warning Methods of Infectious Diseases

2021 ◽  
Vol 251 ◽  
pp. 03084
Author(s):  
Song-nian Hu ◽  
Xiao Cheng ◽  
Dan Chen
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Early Warning ◽  
Early Warning System ◽  
Disease Outbreaks ◽  
Warning System ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Social Unrest ◽  
Infectious Disease Outbreaks

Major epidemics of infectious diseases will not only endanger people’s lives and property, but also cause panic and social unrest. Therefore, it is particularly important to establish an infectious disease early warning system and take effective measures in time to prevent infectious disease outbreaks. The article summarizes the relevant definitions of infectious disease early warning system, domestic and foreign development status, infectious disease early warning models and methods, and aims to provide references for the establishment of infectious disease early warning systems.

Early warning system in Tunisia: evolution, challenges and role of laboratory, biosecurity, environment and modeling of infectious diseases

2018 ◽  
Vol 1 ◽  
Author(s):  
Nissaf Bouafif Ben Alaya ◽  
Hédia Bellali ◽  
Matthias Nachtnebel ◽  
Lilian Hollenweger ◽  
Dilan K�c�kali ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System
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