A Framework to Improve Reuse in Weather-Based Decision Support Systems
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- Mamatha A., Krishna Reddy P., Kumara Swamy M., Sreenivas G., Reddy D.R. (2014) A Framework to Improve Reuse in Weather-Based Decision Support Systems. In: Srinivasa S., Mehta S. (eds) Big Data Analytics. BDA 2014. Lecture Notes in Computer Science, vol 8883. Springer, Cham
The systems for weather observation and forecast are being operated to deal with adverse weather in general to mankind. Weather-based decision support systems (DSSs) are being build to improve the efficiency of the production systems in the domains of health, agriculture, livestock, transport, business, planing, governance and so on. The weather-based DSS provides appropriate suggestions based on the weather condition of the given period for the selected domain. In the literature, the notion of reuse is being employed in improving the efficiency of DSSs. In this paper, we have proposed a framework to identify similar weather conditions, which could help in improving the performance of weather-based DSSs with better reuse. In the proposed framework, the range of weather variable is divided into categories based on its influence on that domain. We form a weather condition for a period which is the combination of category values of weather variables. By comparing the daily/weekly weather conditions of a given year to weather conditions of subsequent years, the proposed framework identifies the extent of reuse. We have conducted the experiment by applying the proposed framework on 30 years of weather data of Rajendranagar, Hyderabad and using the categories employed by India Meteorological Department in Meteorology domain. The results show that there is a significant degree of similarity among daily and weekly weather conditions over the years. The results provide an opportunity to improve the efficiency of weather-based DSSs by improving the degree of reuse of the developed suggestions/knowledge for the corresponding weather conditions.
KeywordsReuse Decision Support Systems Similarity Weather Condition Data Analysis
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