Abstract
In weather-based decision support system (DSS), the domain experts provide suggestions to carry out appropriate measures to improve the efficiency of the respective domain by analyzing both the forecasted and observed weather values. In this paper, to provide suggestions for a given combination of forecasted and observed values, we have proposed a framework to exploit reuse of the suggestions which have been prepared for the past combinations of observed and forecasted values over the years. We define the notion of coupled weather condition (CWC) which represents the weather conditions of two consecutive durations for a given combination of weather variables. By employing the domain-specific categories, the proposed framework exploits the reuse of CWCs for the given domain. We have applied the proposed framework by considering the case study of agromet advisory service of India Meteorological Department (IMD). The extent of reuse has been computed by considering 30 years of weather data from Rajendranagar, Hyderabad, Telangana State, based on the weather categories data provided by IMD. The reuse over 30 years is computed by considering the period of year and crop seasons of a year. Period is defined as portion of time of the year(s) that is considered to analyze the similarity. The results are very positive. The results show that the percentage of reuse of CWCs with three weather variables for the period of year is about 77% after five years. The results provide the scope to develop automatic weather-based DSS in various domains with minimal human intervention and improve the utilization of the generated content.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
India meteorological department weather forecasters guide. http://imdpune.gov.in/Weather/Reports/forecaster_guide.pdf
Bajwa, S.: Investigating the impact of weather information on travel time prediction. In: OPTTIMUM International Symposium on Recent Advances in Transport Modelling (2013)
Balasubramanian, T., Jagannathan, R., Maragatham, N., Sathyamoorthi, K., Nagarajan, R.: Generation of weather windows to develop agro advisories for Tamil Nadu under automated weather forecast system. J. Agrometeorology 16(1), 60 (2014)
Filip, F.G.: Decision support and control for large scale complex systems. In: Large Scale Complex Systems Theory and Applications, vol. 11, pp. 2–12 (2007)
Johnson, M.P., Zheng, K., Padman, R.: Modeling the longitudinality of user acceptance of technology with an evidence-adaptive clinical decision support system. Decis. Support Syst. 57, 444–453 (2014)
Jones, J.W., Hansen, J.W., Royce, F.S., Messina, C.D.: Potential benefits of climate forecasting to agriculture. Agric. Ecosyst. Environ. 82(1), 169–184 (2000)
Maini, P., Rathore, L.: Economic impact assessment of the agrometeorological advisory service of India. Current Sci. 101(10), 1296–1310 (2011)
Mamatha, A., Krishna Reddy, P., Kumara Swamy, M., Sreenivas, G., Reddy, D.R.: A framework to improve reuse in weather-based decision support systems. In: Srinivasa, S., Mehta, S. (eds.) BDA 2014. LNCS, vol. 8883, pp. 1–13. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13820-6_1
Monkhouse, F.J.: A Dictionary of Geography. Transaction Publishers, New Brunswick (2007)
Rathore, L.: Weather information for sustainable agriculture in India. J. Agric. Phys. 13(2), 89–105 (2013)
Reddy, P.K., Trinath, A.V., Kumaraswamy, M., Reddy, B.B., Nagarani, K., Reddy, D.R., Sreenivas, G., Murthy, K.D., Rathore, L.S., Singh, K.K., Chattopadhyay, N.: Development of eagromet prototype to improve the performance of integrated agromet advisory service. In: Madaan, A., Kikuchi, S., Bhalla, S. (eds.) DNIS 2014. LNCS, vol. 8381, pp. 168–188. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05693-7_11
Krishna Reddy, P., Bhaskar Reddy, B., Rama Rao, D.: A model of virtual crop labs as a cloud computing application for enhancing practical agricultural education. In: Srinivasa, S., Bhatnagar, V. (eds.) BDA 2012. LNCS, vol. 7678, pp. 62–76. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35542-4_6
Schepers, H., Bouma, E., Frahm, J., Volk, T., Secher, B.: Control of fungal diseases in winter wheat with appropriate dose rates and weather-based decision support systems 1. EPPO Bull. 26(3–4), 623–630 (1996)
Selby, R.W.: Enabling reuse-based software development of large-scale systems. IEEE Trans. Softw. Eng. 31(6), 495–510 (2005)
Srivastava, B.: A decision-support framework for component reuse and maintenance in software project management, pp. 125–134. IEEE (2004)
Stanescu, I.A., Filip, F.G.: Capture and reuse of knowledge in ICT-based decisional environments. Informatica Economica 13(4), 11 (2009)
Strahler, A.N.: Introduction to physical geography (1965)
Acknowledgement
This work is supported by India-Japan Joint Research Laboratory Project entitled “Data Science based farming support system for sustainable crop production under climatic change (DSFS)”, funded by Department of Science and Technology, India (DST) and Japan Science and Technology Agency (JST).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Mamatha, A., Krishna Reddy, P., Mondal, A., Ninomiya, S., Sreenivas, G. (2017). A Framework to Improve Reuse in Weather-Based DSS Based on Coupling Weather Conditions. In: Reddy, P., Sureka, A., Chakravarthy, S., Bhalla, S. (eds) Big Data Analytics. BDA 2017. Lecture Notes in Computer Science(), vol 10721. Springer, Cham. https://doi.org/10.1007/978-3-319-72413-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-72413-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72412-6
Online ISBN: 978-3-319-72413-3
eBook Packages: Computer ScienceComputer Science (R0)