Coping Mechanism of Farmers at Catagbacan, Goa, Camarines Sur in Extreme Weather Condition

2017 ◽  
Keyword(s):  
Weather Condition ◽  
Extreme Weather ◽  
Coping Mechanism

scholarly journals Context-Based Visual Aids to Support the Situation Awareness of Field Engineers Conducting Windstorm Risk Surveys

2019 ◽  
Vol 63 (1) ◽  
pp. 1898-1902
Author(s):  
Sruthy Agnisarman ◽  
Kapil Chalil Madathil ◽  
Jeffery Bertrand
Keyword(s):  
Experimental Design ◽  
Situation Awareness ◽  
Decision Aids ◽  
Weather Condition ◽  
Extreme Weather ◽  
Visual Aids ◽  
Loss Prevention ◽  
Predictive Display ◽  
Independent Variable ◽  
And Performance

Insurance loss prevention survey, specifically windstorm risk inspection survey is the process of investigating potential damages associated with a building or structure in the event of an extreme weather condition such as a hurricane or tornado. This process is performed by a trained windstorm risk engineer who physically goes to a facility to assess the wind vulnerabilities associated with it. This process is highly subjective, and the accuracy of findings depends on the experience and skillsets of the engineer. Although using sensors and automation enabled systems help engineers gather data, their ability to make sense of this information is vital. Further, their Situation Awareness (SA) can be affected by the use of such systems. Using a between-subjects experimental design, this study explored the use of various context-based visualization strategies to support the SA requirements and performance of windstorm risk engineers. The independent variable included in this study is the type of context-based visualizations used (with 3 levels: no visual aids, checklist based and predictive display based visual aids). We measured SA using SAGAT and performance using a questionnaire. SA and performance were found to be higher for the predictive display and checklist based conditions. The findings from this study will inform the design of context-based decision aids to support the SA of risk engineers.

Payment of Ecosystem Service to Alleviate Poverty from Kyrgyz Republic in Central Asia Considering Climate Change and Extreme Weather Condition

2015 ◽  
Vol 1 (1,2) ◽  
pp. 119-128 ◽  
Author(s):  
Chitresh Saraswat ◽  
Pankaj Kumar ◽  
Dinara Kem ◽  
Ram Avtar ◽  
AL. Ramanathan
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
Ecosystem Service ◽  
Weather Condition ◽  
Extreme Weather ◽  
Kyrgyz Republic

scholarly journals Optimal operational scheduling and reconfiguration coordination in smart grids for extreme weather condition

2019 ◽  
Vol 13 (15) ◽  
pp. 3455-3463 ◽  
Author(s):  
Hesam Khazraj ◽  
Babak Yousefi Khanghah ◽  
Pramod Ghimire ◽  
Frank Martin ◽  
Mohammad Ghomi ◽  
...  
Keyword(s):  
Smart Grids ◽  
Weather Condition ◽  
Extreme Weather

Effects of cold and hot temperature on dehydration: a mechanism of cardiovascular burden from extreme weather condition

2013 ◽  
Vol 2013 (1) ◽  
pp. 4419
Author(s):  
Youn-Hee Lim ◽  
Min-Seon Park ◽  
Yoonhee Kim ◽  
Ho Kim ◽  
Keun-Young Yoo ◽  
...  
Keyword(s):  
Weather Condition ◽  
Extreme Weather ◽  
Hot Temperature

scholarly journals Empirical analysis of extreme weather conditions and aviation safety in Nigeria

2016 ◽  
Vol 9 (6) ◽  
pp. 680-690
Author(s):  
C.O. Akanni ◽  
A.M. Hassan ◽  
T.C. Osuji
Keyword(s):  
Traffic Accidents ◽  
Weather Condition ◽  
Secondary Data ◽  
Weather Conditions ◽  
Aviation Safety ◽  
Extreme Weather ◽  
Aviation Industry ◽  
A Value ◽  
Low Visibility ◽  
Safety Accident

The frequency of delay, diversion and outright cancellation occasioned by poor weather has affected the Nigerian aviation industry and serious safety implication.This study therefore examines the influence of weather conditions on aviation safety in Nigeria. Secondary data basically from Nigeria Meteorological Agency such as information on thunderstorm, fog occurrence and rainfall from 2004 to 2013 and data obtained from Federal Airport Authority of Nigeria on air accident induced by extreme weather within the same period were analysed using Multiple Regression Analysis. Results show that low visibility as a result of fog occurrence causes four (4) air traffic accidents more than other weather conditions and that Lagos experienced two (2) air accidents more than other airports during the period under study.  So also the value of R2 shows a value of 77.8% which implies that there is variation in the dependent variable (Airport Operation) which can be predicted by independent variables (Weather conditions). The F-statistic value of 62.892 is also found to be statistically significant at 5% (p<0.05) which shows that weather condition has significant influence on aviation safety. Baseline studies on flight operation, government intervention in aviation industry, maintenance culture were recommended.Keywords: Fog, Thunderstorm, Rainfall, Safety, Accident

Extreme weather condition for aviation over the Donets Ridge on August 22, 2006

2007 ◽  
Vol 32 (5) ◽  
pp. 307-312 ◽  
Author(s):  
B. E. Peskov ◽  
A. A. Alekseeva ◽  
S. E. Chernyi
Keyword(s):  
Weather Condition ◽  
Extreme Weather

scholarly journals Modelling and Analysis of IoT Technology Using Neural Networks In Agriculture Environment

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Emre Özbilge ◽  
Yönal Kırsal ◽  
Ersin Çaglar
Keyword(s):  
Neural Networks ◽  
Internet Of Things ◽  
Network Model ◽  
Rapid Development ◽  
Weather Condition ◽  
Weather Conditions ◽  
Research Area ◽  
Extreme Weather ◽  
Learning System ◽  
Agricultural Environment

The rapid development of internet, cloud computing and sensor networks lead to develop and deploy the Internet of Things (IoT) which is a hot topic for the researchers. It has started to be used in various areas. Thus, agriculture is one of the most popular IoT research area. In agriculture environment, farming platform area is being a huge open structure and farmers must protect the crops from extreme weather conditions namely; wind speed/direction, precipitation, air temperature, solar radiations, and relative humidity etc. These extreme weather conditions effect crops and farms very significantly. But with the benefits of Internet of Things technologies, an agriculture business become more easy and efficient despite extreme weather conditions. This paper provides a model of smart agriculture environment using neural networks that helps the farmers to make more accurate predictions for the future according to weather conditions. This paper proposed a time-delay radial basis function (TDRBF) network approach to model temporal and sequential relationship between the various weather condition sensor readings from the agricultural environment. The performance of the acquired network model was analysed statistically and presented in this paper. As a result, the results of the neural network model show that it could be used to predict the desired weather condition sensor readings beforehand in order to increase the productivity in agricultural environment and also it is possible that by using such an intelligent learning system could provide a life-long learning for the changing weather conditions in the farming area over the years.

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