scholarly journals Weather Sensitivity of Downy Mildew and Alternaria Blight of Mustard in the Gangetic West Bengal, India

2013 ◽  
Vol 8 (1-2) ◽  
pp. 77-81 ◽  
Author(s):  
S Banerjee ◽  
I Bhattacharya ◽  
SA Khan ◽  
AKS Huda
Keyword(s):  
Downy Mildew ◽  
Weather Condition ◽  
Weather Data ◽  
Weather Factors ◽  
Protection Measures ◽  
Alternaria Blight ◽  
Weather Sensitivity ◽  
Percent Disease Index ◽  
Forewarning System ◽  
Average Minimum Temperature

The infestation of disease-pest poses a considerable threat to rapeseed-mustard production in India. Alternaria blight [A. brassicicola (Schw.)] and downy mildew [Peronospora parasitica (Pers.) Kuntze] are the major diseases in lower Gangetic plains of India. As the rate of disease increase are dependent on weather factors, weather-based forewarning system may enable to guide farmers to take protection measures timely. The paper aims at to find the effect of weather on alternaria blight and downy mildew infestations. The weather data were compared with Percent Disease Index and the prevailing weather condition during peak disease intensity has been sorted out. It was observed that an increasing trend of last seven days average minimum temperature and relative humidity indicates more Alternaria blight and downy mildew infestation. DOI: http://dx.doi.org/10.3329/jsf.v8i1-2.14629 J. Sci. Foundation, 8(1&2): 77-81, June-December 2010

MAIN DIRECTIONS OF PREVENTION OF INCREASED WEATHER SENSITIVITY AND TREATMENT OF WEATHER-DEPENDENT PATIENTS

2020 ◽  
pp. 45-48
Author(s):  
Kolyagina N.M. ◽  
Berezhnova T.A. ◽  
Klepikov O.V. ◽  
Kulintsova Ya.V.
Keyword(s):  
Chronic Diseases ◽  
Climatic Factors ◽  
Weather Factors ◽  
Educational Work ◽  
Pathological Effect ◽  
Term Care ◽  
Cardiovascular Pathology ◽  
Therapeutic Work ◽  
Weather Sensitivity ◽  
Voronezh Region

Currently, and over the past decade, intensive research is being conducted in the field of organizational, preventive and therapeutic work with weather-sensitive and weather-dependent patients suffering from cardiovascular pathology. One of the most discussed issues in practical medicine is the application of climate conditions, with a fixed frequency and time of exposure. Conditions of influence of climatic factors, in some cases, can exceed the limits of the norm and, accordingly, have a pathological effect on the functional state of a person. Thus, we used data from statistically reliable results of 928 questionnaires processed and analyzed, reflecting the medical and social characteristics of patients with cardiovascular pathology. Direct results were evaluated by analyzing changes in the dynamics of indicators. According to a questionnaire survey, 62% of patients who have chronic diseases and seek medical help for diseases of the cardiovascular system believe that weather factors have a significant impact on their health. of the patients who do not have chronic diseases, 38% gave positive answers to the question about the weather sensitivity of their health. As part of the pilot project to create a system of long-term care for elderly and disabled citizens in the Voronezh region, patients are informed about the sources of obtaining specialized medical weather forecasts in the region; interaction between the parties is organized to conduct sanitary and educational work; medical examinations are organized and conducted; training sessions are organized for relatives of citizens who have lost the ability to self-service. Further organizational, preventive and therapeutic work with weather-sensitive and weather-dependent patients in the Voronezh region is characterized by an increase in the quality of medical care for patients with cardiovascular diseases.

Exploring Weather Data to Predict Activity Attendance in Event-based Social Network

2021 ◽  
Vol 15 (2) ◽  
pp. 1-25
Author(s):  
Jifeng Zhang ◽  
Wenjun Jiang ◽  
Jinrui Zhang ◽  
Jie Wu ◽  
Guojun Wang
Keyword(s):  
Real Life ◽  
Prediction Performance ◽  
Weather Data ◽  
Gradient Boosting ◽  
User Interest ◽  
Weather Factors ◽  
Event Duration ◽  
Outdoor Activities ◽  
Weather Factor ◽  
Event Based

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

scholarly journals Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yajie Zou ◽  
Ting Zhu ◽  
Yifan Xie ◽  
Linbo Li ◽  
Ying Chen
Keyword(s):  
Travel Time ◽  
Weather Condition ◽  
Time Of Day ◽  
Weather Data ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Time Data ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
The Impact

Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

scholarly journals Association between weather factors and dengue in Dhaka: a time-series analysis

2021 ◽  
Vol 39 (1) ◽  
pp. 43-49
Author(s):  
Shafia Shaheen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Incidence Rate ◽  
Negative Binomial ◽  
Weather Data ◽  
Negative Binomial Regression Model ◽  
Weather Factors ◽  
Average Temperature ◽  
Series Analysis ◽  
Average Maximum

Background: There was an epidemic of dengue fever that happened  in Bangladesh  in the year of 2019. Temperature of this country has been raising which leads to changing in rainfall pattern. This study was aimed to investigate the relationship of weather factors and dengue incidence in Dhaka. Methods: A time series analysis was carried out by using 10 years weather data as average , maximum and minimum monthly temperature, average monthly humidity and average and cumulative monthly rainfall. Reported number of dengue cases was extracted from January 2009 to July 2019. Firstly, dengue incidence rate was  calculated. Correlation analysis and negative binomial regression model was developed. Results: Dengue incidence rate had sharp upward trend. Dengue incidence and mean, maximum and minimum average temperature showed statistically significant negative correlation at 3 months' lag. Highest incidence Rate Ratio (IRR) of dengue was found at minimum average temperature at 0 and I-month lag. Average humidity showed positive and significant correlation with dengue incidence at 0-month lag. Average and cumulative rainfall also showed negative and significant correlation only at 3-months lag period. Conclusion: Weather variability influences dengue incidence and the association between the weather factors are non­ linear and not consistent. So the study findings should be evaluated area basis with other local factors to develop early warning for dengue epidemic prediction. JOPSOM 2020; 39(1): 43-49

scholarly journals Weather Prediction using Advanced Machine Learning Techniques

2021 ◽  
Vol 2089 (1) ◽  
pp. 012059
Author(s):  
G. Hemalatha ◽  
K. Srinivasa Rao ◽  
D. Arun Kumar
Keyword(s):  
Linear Relationship ◽  
Weather Prediction ◽  
Weather Condition ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Learning Techniques ◽  
Proposed Model ◽  
Non Linear ◽  
The Relationship

Abstract Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..

Screening of Brassica rapa L. var. Yellow Sarson Genotypes Against Downy Mildew and Alternaria blight

2014 ◽  
pp. 149-157
Author(s):  
Kamlesh Kumar Prajapati ◽  
O. P. Verma ◽  
Prakash Singh ◽  
Sanjeev Singh ◽  
Dhirendra K. Singh
Keyword(s):  
Downy Mildew ◽  
Brassica Rapa ◽  
Alternaria Blight

Review of Passive Downdraught Evaporative Cooling Technique and its Application

2013 ◽  
Vol 390 ◽  
pp. 691-695 ◽  
Author(s):  
Jing Qiu ◽  
Bao Feng Li ◽  
Yu Qiu
Keyword(s):  
Global Climate ◽  
Low Cost ◽  
Evaporative Cooling ◽  
Weather Condition ◽  
Cost Effective ◽  
Weather Data ◽  
North West ◽  
Direct Evaporative Cooling ◽  
Key Factor ◽  
Cooling Technique

Direct evaporative cooling has long been demonstrated as an energy efficient ,cost effective and no CFCs emission means for space cooling in hot dry regions .With the aggravating of the global climate warming and energy crisis, using passive cooling technique will be a good solution . In this paper, the theory of passive downdraught evaporative cooling techniques is analyzed. It is an environmental friendly technique in that it can provide more fresh air than the conventional air-conditionings, and also low cost on operation and no CFCs emission compared with conventional air-conditionings. In this paper, some cases will be introduced .The successful PDEC cases in hot dry areas show weather condition is the key factor for the feasibility using PDEC technique. From the analysis on the weather data in Turpan, which presents a typical climatic character in North-west China , predicts a great feasibility of using PDEC technique in public architectures.

scholarly journals WEATHER SENSITIVITY OF RICE YIELD: EVIDENCE FROM INDIA

2014 ◽  
Vol 05 (04) ◽  
pp. 1450011 ◽  
Author(s):  
ANUBHAB PATTANAYAK ◽  
K. S. KAVI KUMAR
Keyword(s):  
Rice Yield ◽  
Negative Impact ◽  
Significant Loss ◽  
Climatic Conditions ◽  
Weather Data ◽  
Weather Sensitivity ◽  
Daily Weather Data ◽  
Level Information ◽  
Maximum Temperatures ◽  
The Impact

This study estimates the weather sensitivity of rice yield in India, using disaggregated (district) level information on rice and high resolution daily weather data over the period 1969–2007. Compared to existing India specific studies on rice which consider only the effects of nighttime (minimum) temperature, the present study takes into account the effects of both nighttime and daytime (maximum) temperatures along with other weather variables on rice yield. The results suggest that both nighttime and daytime temperatures adversely affect rice during different growth phases. The effect of higher nighttime temperature on rice yield was much lower than those estimated by previous studies. Further, the negative impact of higher daytime temperature on rice yield was much larger than the impact due to higher nighttime temperature. The study further estimates that average rice yield would have been 8.4% higher had the pre-1960 climatic conditions prevailed during the period of study. This translates into an annual average loss of 4.4 million tons/yr or a cumulative loss of 172 million tons over the 39 year period for India. The paper argues that such significant loss in rice production under climate change conditions in future will have strong implications for the region's food-security and poverty, given that a large number of producers and consumers depend on rice for their livelihood and sustenance.

Sign in / Sign up

Export Citation Format

Share Document