Abstract
This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage (true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.
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References
Bhagat RB, Mohanty S (2009) Emerging pattern of urbanization and the contribution of migration in urban growth in India. Asian Population Studies 5(1):5–20
Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press, Oxford
Blankertz B, Muller KR, Curio G, Vaughan TM, Schalk G, Wolpaw JR, Schroder M (2004) The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE transactions on biomedical engineering, 51(6):1044–1051
Bollinger CR, David MH (1997) Modeling discrete choice with response error: food stamp participation. J Am Stat Assoc 92:827–835
Challinor AJ, Slingo JM, Wheeler TR, Craufurd PQ, Grimes DIF (2003) Toward a combined seasonal weather and crop productivity forecasting system: determination of the working spatial scale. J Appl Meteorol 42(2):175–192
Cortez P (2010) Data mining with neural networks and support vector machines using the R/rminer tool. In Advances in data mining. Applications and theoretical aspects. Springer Berlin Heidelberg, pp 572–583
Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A (2005) e1071: misc functions of the Department of Statistics (e1071), TU Wien, Version 1.5-11. http://CRAN.R-project.org/. Accessed 1 June 2015
Esen H, Inalli MA, Sengur Esen M (2008) Modeling a ground-coupled heat pump system by a support vector machine. Renew Energy 33(8):1814–1823
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874
Gadgil S, Abrol YP, Seshagiri Rao PR (1999) On growth and fluctuation of Indian foodgrain production. Curr Sci 76(4):548–556
Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning: data mining, inference, and prediction. Springer-Verlag, New York
Haykin S (2008) Neural networks and learning machines, 3rd edn. Prentice Hall, Upper Saddle River
Haykin S et al (2009) Neural networks and learning machines (Vol. 3). Upper Saddle River: Pearson Education. McMaster University, Canada
Henry Horng-Shing, Lu, Bernhard, S., Hongyu, Z. (2011) .Handbook of statistical bioinformatics. Springer
Hill MG, Connolly PG, Reutemann P, Fletcher D (2014) The use of data mining to assist crop protection decisions on kiwifruit in New Zealand. Comput Electron Agric 108:250–257
Hosmer DW Jr, Lemeshow S Sturdivant RX (2013) applied logistic regression (Vol. 398). John Wiley & Sons, Hoboken, p 528
Indian Meteorological Department, Frequently Asked Questions (FAQ), http://www.imd.gov.in/section/nhac/wxfaq.pdf. Accessed on 19 May 2016
Krishna Kumar K, Kumar RK, Ashrit RG, Deshpande NR, Hansen JW (2004) Climate impacts on Indian agriculture. Int J Climatol 24(11):1375–1393
Lu HH-S (2011) Handbook of statistical bioinformatics. In: Lu HH-S, Schölkopf B, Zhao H (eds) Series: springer handbooks of computational statistics. Springer-Verlag, Berlin, Heidelberg, p. 627 ISBN: 978-3-642-16344-9
Min JH, Lee YC (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst Appl 28(4):603–614
Mostafa MM, El-Masry AA (2013) Citizens as consumers: profiling e-government services’ users in Egypt via data mining techniques. Int J Inf Manag 33:627–641
Olson DL, Chae BK (2012) Direct marketing decision support through predictive customer response modeling. Decis Support Syst 54(1):443–451
Oluoko-Odingo AA (2009) Determinants of poverty: lessons from Kenya. GeoJournal 74(4):311–331
Paltasingh KR, Goyari P, Mishra RK (2012) Measuring weather impact on crop yield using aridity index: evidence from Odisha. Agric Econ Res Rev 25(2):205–216
Papageorgiou EI, Markinos AT, Gemtos TA (2011) Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application. Appl Soft Comput 11(4):3643–3657
Parthasarathy B, Munot AA, Kothawale DR (1988) Regression model for estimation of Indian foodgrain production from summer monsoon rainfall. Agric For Meteorol 42(2):167–182
Pattanaik DR, Kumar A (2010) Prediction of summer monsoon rainfall over India using the NCEP climate forecast system. Clim Dyn 34(4):557–572
Pino-Mejias R, Cubiles-de-la-Vega MD, Anaya-Romero M, Pascual-Acosta A, Jordan-Lopez A, Bellinfante-Crocci N (2010) Predicting the potential habitat of oaks with data mining models and the R system. Environ Model Softw 25:826–836
Prasanna V (2014) Impact of monsoon rainfall on the total foodgrain yield over India. Journal of Earth System Science 123(5):1129–1145
Preethi B, Revadekar JV (2013) Kharif foodgrain yield and daily summer monsoon precipitation over India. Int J Climatol 33(8):1978–1986
Provost FJ, Fawcett T, Kohavi R (1998) The case against accuracy estimation for comparing induction algorithms. In ICML 98:445–453
Salzberg SL (1997) On comparing classifiers: pitfalls to avoid and a recommended approach. Data Min Knowl Disc 1(3):317–328
Schultz A, Wieland R, Lutze G (2000) Neural networks in agroecological modeling-stylish application or helpful tool? Comput Electron Agric 29(1):73–97
Sharma BR, Rao KV, Vittal KPR, Ramakrishna YS, Amarasinghe U (2010) Estimating the potential of rainfed agriculture in India: Prospects for water productivity improvements. Agric Water Manag 97(1):23–30
Subash N, Sikka AK, Mohan HR (2011) An investigation into observational characteristics of rainfall and temperature in central Northeast India—a historical perspective 1889–2008. Theor Appl Climatol 103(3–4):305–319
Swaminathan MS (1987) Abnormal monsoons and economic consequences: the Indian experience. Monsoons. Wiley, Hoboken, p. 632
Swaminathan MS (1997) Sustainable agriculture—key to our survival. In: Patil JH, Chitale MA, Varade SB (eds) Productivity of land and water. New Age International, New Delhi pp, p. 31
Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin
Venables WN, Ripley BD (2002) Modern applied statistics with S. Springer Science & Business Media. 4th edn. New York
Zheng B, Myint SW, Thenkabail PS, Aggarwal RM (2015) A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. Int J Appl Earth Obs Geoinf 34:103–112
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Chattopadhyay, M., Mitra, S.K. Comparative decision models for anticipating shortage of food grain production in India. Theor Appl Climatol 131, 523–530 (2018). https://doi.org/10.1007/s00704-016-1961-0
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DOI: https://doi.org/10.1007/s00704-016-1961-0