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Rathod, Santosha
- Modelling and forecasting cotton production using tuned-support vector regression
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Authors
Affiliations
1 Central Sericultural Research and Training Institute, Central Silk Board, Srirampura, Mysuru 570 008, India, IN
2 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India, IN
3 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India, IN
4 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India, IN
1 Central Sericultural Research and Training Institute, Central Silk Board, Srirampura, Mysuru 570 008, India, IN
2 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India, IN
3 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India, IN
4 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India, IN
Source
Current Science, Vol 121, No 8 (2021), Pagination: 1090-1098Abstract
India is the largest producer of cotton in the world. For proper planning and designing of policies related to cotton, robust forecast of future production is utmost necessary. In this study, an effort has been made to model and forecast the cotton production of India using tuned-support vector regression (Tuned-SVR) model, and the importance of tuning has also been pointed out through this study. The Tuned-SVR performed better in both modelling and forecasting of cotton production compared to auto regressive integrated moving average and classical SVR modelsKeywords
ARIMA, cotton production forecasting, SVR, time series, tuned-SVR.Full Text
References
- Box, G. E. P. and Jenkins, G., Time series analysis, forecasting and control. Holden-Day, San Francisco, CA, 1970.
- Ariyo, A. A., Adewumi, A. O. and Ayo, C. K., Stock price prediction using the ARIMA model. In UK Sim-AMSS 16th International Conference on Computer Modelling and Simulation, IEEE, 2014, pp. 106–112.
- Badmus, M. A. and Ariyo, O. S., Forecasting cultivated areas and production of maize in Nigerian using ARIMA Model. Asian J. Agric. Sci., 2011, 3(3), 171–176.
- Bari, S. H., Rahman, M. T., Hussain, M. M. and Ray, S., Forecasting monthly precipitation in Sylhet city using ARIMA model. Civil Environ. Res., 2015, 7(1), 69–77.
- Suresh, K. K. and Priya, S. K., Forecasting sugarcane yield of Tamil Nadu using ARIMA models. Sugar Tech., 2011, 13(1), 23–26.
- Padhan, P. C., Application of ARIMA model for forecasting agricultural productivity in India. J. Agric. Soc. Sci., 2012, 8(2), 50– 56.
- Prabakaran, K. and Sivapragasam, C., Forecasting areas and production of rice in India using ARIMA model. Int. J. Farm Sci., 2014, 4(1), 99–106.
- Sarika, Iquebal, M. A. and Chattopadhyay, C., Modelling and forecasting of pigeonpea (Cajanuscajan) production using autoregressive integrated moving average methodology. Indian J. Agric. Sci., 2011, 81(6), 520–523.
- Cortes, C. and Vapnik, V., Support-vector network. Mach. Learn., 1995, 20, 1–25.
- Vapnik, V., Golowich, S. and Smola, A., Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems (eds Mozer, M., Jordan, M. and Petsche, T.), MIT Press, Cambridge, USA, 1997, vol. 9, pp. 281–287.
- Mattera, D. and Haykin, S., Support vector machines for dynamic reconstruction of achaotic system. In Advances in Kernel Methods – Support Vector Learning (eds Schölkopf, B. et al.), MIT Press, Cambridge, USA, 1999, pp. 211–242.
- Muller, K. R., Smola, A., R¨atsch, G., Schölkopf, B., Kohlmorgen, J. and Vapnik, V., Predicting time series with support vector machines. In Artificial Neural Networks (eds Gerstner, W. et al.), ICANN 1997, Lecture Notes in Computer Science, Springer, Berlin, Germany, 1997, vol. 1327, pp. 999–1004.
- Niu, D., Wang, Y. and Wu, D. D., Power load forecasting using support vector machine and ant colony optimization. Exp. Syst. Appl., 2010, 37, 2531–2539.
- Saha, A., Singh, K. N., Ray, M. and Rathod, S., A hybrid spatiotemporal modelling: an application to space-time rainfall forecasting. Theor. Appl. Climatol., 2020, 142, 1271–1282.
- Saha, A. and Bhattacharyya, S., Artificial insemination for milk production in India: a statistical insight. Indian J. Anim. Sci., 2021, 90, 1186–1190.
- Stitson, M., Gammerman, A., Vapnik, V., Vovk, V., Watkins, C. and Weston, J., Support vector regression with ANOVA decomposition kernels. In Advances in Kernel Methods – Support Vector Learning (eds Schölkopf, B., Burges, C. J. C. and Smola, A. J.), MIT Press, Cambridge, USA, 1999, pp. 285–292.
- Ortiz-Garcia, E. G., Salcedo-Sanz, S. and Casanova-Mateom, C., Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data. Atmos. Res., 2014, 139, 128–136.
- Kumar, T. L. M. and Prajneshu, Development of hybrid models for forecasting time-series data using nonlinear SVR enhanced by PSO. J. Stat. Theory Prac., 2015, 9(4), 699–711.
- Rathod, S., Singh, K. N., Patil, S. G., Naik, R. H., Ray, M. and Meena, V. S., Modeling and forecasting of oilseed production of India through artificial intelligence techniques. Indian J. Agric. Sci., 2018, 88(1), 22–27.
- De Giorgi, M. G., Campilongo, S., Ficarella, A. and Congedo, P. M., Comparison between wind power rediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energy, 2014, 7, 5251–5272.
- Balasundaram, S. and Gupta, D., Lagrangian support vector regression via unconstrained convex minimization. Neural Networks, 2014, 51, 67–79.
- Balasundaram, S. and Gupta, D., On implicit Lagrangian twin support vector regression by Newton method. Int. J. Comput. Intel. Syst., 2014, 7(1), 50–64.
- Balasundaram, S. and Gupta, D., Training Lagrangian twin support vector regression via unconstrained convex minimization. Knowl.-Based Syst., 2014, 59, 85–96.
- Balasundaram, S. and Gupta, D., On optimization based extreme learning machine in primal for regression and classification by functional iterative method. Int. J. Mach. Learn. Cybernet., 2016, 7(5), 707–728.
- Gupta, D., Richhariya, B. and Borah, P., A fuzzy twin support vector machine based on information entropy for class imbalance learning. Neural. Comput. Appl., 2019, 31(11), 7153–7164.
- Gupta, U. and Gupta, D., An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function. Appl. Intell., 2019, 49(10), 3606–3627.
- Hou, Q., Zhang, J., Liu, L., Wang, Y. and Jing, L., Discriminative information-based nonparallel support vector machine. Signal. Process., 2019, 162, 169–179.
- Meyer, D. et al., Package ‘e1071’. The R Journal, 2019.
- Extreme Temperature and Rainfall Event Trends in the Middle Gangetic Plains From 1980 to 2018
Abstract Views :82 |
PDF Views:58
Authors
S. Vijayakumar
1,
Sudhir Kumar Rajpoot
2,
N. Manikandan
3,
R. Jayakumara Varadan
4,
J. P. Singh
2,
Dibyendu Chatterjee
5,
Sumanta Chatterjee
6,
Santosha Rathod
7,
Anil Kumar Choudhary
8,
Adarsh Kumar
9
Affiliations
1 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India; ICAR-National Rice Research Institute, Cuttack 753 006, India., IN
2 Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221 005, India., IN
3 ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, India., IN
4 ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman & Nicobar Islands 744 101, India., IN
5 ICAR-National Rice Research Institute, Cuttack 753 006, India., IN
6 University of Wisconsin-Madison, Madison, WI 53706, USA., US
7 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India., IN
8 ICAR-Central Potato Research Institute, Shimla 171 001, India; ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
9 ICAR-National Bureau of Agriculturally Important Microorganisms, Mau Nath Bhanjan 275 103, India., IN
1 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India; ICAR-National Rice Research Institute, Cuttack 753 006, India., IN
2 Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221 005, India., IN
3 ICAR-Central Research Institute for Dryland Agriculture, Hyderabad 500 059, India., IN
4 ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman & Nicobar Islands 744 101, India., IN
5 ICAR-National Rice Research Institute, Cuttack 753 006, India., IN
6 University of Wisconsin-Madison, Madison, WI 53706, USA., US
7 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India., IN
8 ICAR-Central Potato Research Institute, Shimla 171 001, India; ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
9 ICAR-National Bureau of Agriculturally Important Microorganisms, Mau Nath Bhanjan 275 103, India., IN
Source
Current Science, Vol 124, No 11 (2023), Pagination: 1300-1307Abstract
Regional-level studies aimed at identifying and assessing various types of extreme weather events and comprehending their effects on various sectors are crucial. In the present study, we have utilized the RClimDex software to compute the trend in temperature and precipitation extreme events in the Varanasi district of Uttar Pradesh, India, from 1980 to 2018. We employed both Mann–Kendall test and linear regression to test the statistical significance of the computed trend. Out of 13 temperature indices, 8 showed a significant trend while the remaining showed a non-significant trend. The annual mean maximum temperature, warm days, diurnal temperature range and a monthly minimum of maximum temperature had decreased significantly by 0.029ºC, 0.159 days, 0.032ºC and 0.122ºC/yr respectively, whereas cool days and cold spell duration had increased significantly by 0.264ºC and 0.372 days/yr respectively, indicating an increased cooling effect over the study area. Similarly, out of the 11 rainfall indices, only two showed a significant trend, while the remaining showed a non-significant trend. The increasing drought over the study area is evident as the number of rainy days and consecutive wet days have decreased significantly by 0.262 days and 0.058 days/yr respectively, with a non-significant increase in consecutive dry days during the same period. The weak negative non-significant trend of a maximum of five consecutive days of rainfall, very heavy rainfall days and total annual precipitation indicate the decreasing trend of floods. This study stresses the development of adaptation plans to overcome the adverse consequences of extreme weather events in Varanasi district.Keywords
Adaptation Plans, Climate Change, Extreme Weather Events, Temperature and Rainfall, Statistical Significance, Trends.Full Text
References
- Luhunga, P. M. and Songoro, A. E., Analysis of climate change and extreme climatic events in the Lake Victoria Region of Tanzania. Front. Climate, 2020, 2, 559584.
- Sarker, Md. A. R., Alam, K. and Gow, J., How does the variability of Aus rice yield respond to climate change in Bangladesh? J. Agron. Crop Sci., 2012, 199, 189–194.
- Curtis, S., Fair, A., Wistow, J., Val, D. and Oven, K., Impact of ex-treme weather events and climate change for health and social care systems. Environ. Health, 2017, 16, 128.
- Manikandan, N., Das, D. K., Mukherjee, J., Sehgal, V. K. and Krishnan, P., Extreme temperature and rainfall events in National Capital Region of India (New Delhi) in the recent decades and its possible impacts. Theor. Appl. Climatol., 2019, 137(3), 1703–1713.
- Seneviratne, S. et al., Changes in climate extremes and their im-pacts on the natural physical environment. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Ad-aptation (eds Field, C. et al.), Special Report of the Intergovern-mental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 2012, pp. 109–230.
- Nayak, A. K. et al., Climate smart agricultural technologies for rice production system in Odisha, ICAR-National Rice Research Insti-tute, Cuttack, pp. 1–336.
- Dash, S. K., Kulkarni, M. A., Mohanty, U. C. and Prasad, K., Changes in the characteristics of rain events in India. J. Geophys. Res., 2009, 114, D10109.
- Kothawale, D. R., Kumar, K. K. and Srinivasan, G., Spatial asym-metry of temperature trends over India and possible role of aerosols. Theor. Appl. Climatol., 2012, 110(1–2), 263–280.
- Tripathi, M. K., Singh, K. K., Mehera, B. and Kumar, H., Quantifi-cation of extreme weather events at Allahabad district of Uttar Pra-desh. Curr. Adv. Agric. Sci., 2013, 5(2), 225–229.
- Mall, R. K., Sonkar, G., Bhatt, D., Sharma, N. K., Baxla, A. K. and Singh, K. K., Managing impact of extreme weather events in sugar-cane in different agro-climatic zones of Uttar Pradesh. Mausam, 2016, 67(1), 233–250.
- Vijayakumar, S. et al., Rainfall and temperature projections and their impact assessment using CMIP5 models under different RCP scenarios for the eastern coastal region of India. Curr. Sci., 2021, 121(2), 222–232.
- Ding, Y. et al., National assessment report of climate change (I): climate change in China and its future trend. Adv. Climate Change Res., 2006, 2(1), 3–8.
- Zhang, X. and Yang, F., RClimDex (1.0) User Manual, Climate Research Branch, Environment Canada, Ontario, Canada, 2004, pp. 1–23.
- Stevens, C. J., Dise, N. B., Mountford, J. O. and Gowing, D. J., Impact of nitrogen deposition on the species richness of grasslands. Science, 2004, 303, 1876–1879.
- Parry, M., The potential impact on agriculture of the greenhouse effect. Land Use Policy, 1990, 7, 109–123.
- Jee, O. P., Bihari, D. S. and Kumar, D. P., Temporal variability study in rainfall and temperature over Varanasi and adjoining areas. Disaster Adv., 2019, 12(1), 1–7.
- Bhatla, R., Gupta, P., Tripathi, A. and Mall, R. K., Cold wave/ severe cold wave events during post-monsoon and winter season over some stations of Eastern Uttar Pradesh, India. J. Climate Change, 2016, 2(1), 27–34.
- Prakash, A. et al., Climate Change: Impact on Crop Pests. Applied Zoologists Research Association, Central Rice Research Institute, Odisha, 2014; ISBN 81-900947-2-7.
- Deutsch, C. A., Tewksbury, J. J., Tigchelaar, M., Battisti, D. S., Merrill, S. C., Huey, R. B. and Naylor, R. L., Increase in crop losses to insect pests in a warming climate. Science, 2018, 361, 916–919.
- Luhunga, P. M., Chang’a, L. and Djolov, G. D., Assessment of the impacts of climate change on maize production in the Wami Ruvu basin of Tanzania. J. Water Climate Change, 2017, 8(1), 142–164.
- Kaur, P. and Hundal, S. S., Global climate change vis-à-vis crop productivity. In Natural and Anthropogenic Disasters (ed. Jha, M. K.), Springer, Dordrecht, The Netherlands, 2010, pp. 413–431.
- Luhunga, P. M., Evaluation of the impacts of climate variability on rainfed maize production over the Wami Ruvu basin of Tanzania. J. Water Climate Change, 2017, 9(1), 207–222.
- Parveen, U., Analyzing rainfall variability and trend in Ganga Plain, Uttar Pradesh. Int. J. Res. Anal. Rev., 2017, 4(2), 72–78.
- Frich, P., Alexander, L. V., Della-Marta, P., Gleason, B., Haylock, M., Klein, Tank, A. M. G. and Peterson, T., Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Res., 2002, 19(3), 193–212.
- Skendžić, S., Zovko, M., Živković, I. P., Lešić, V. and Lemić, D., The impact of climate change on agricultural insect pests. Insects, 2021, 12(5), 440.
- Pandey, V., Srivastava, P. K., Singh, S. K., Petropoulos, G. P. and Mall, R. K., Drought identification and trend analysis using long-term CHIRPS satellite precipitation product in Bundelkhand, India. Sustainability, 2021, 13(3), 1042.
- Guhathakurta, P., Sudeep, Kumar, B. L., Menon, P., Prasad, A. K., Sable, S. T. and Advani, S. C., Observed rainfall variability and changes over Uttar Pradesh State. Climate Research and Services, India Meteorological Department, Pune, 2020, pp. 1–31.
- Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S. and Xavier, P. K., Increasing trend of extreme rain events over India in a warming environment. Science, 314, 1442–1445.
- Chang’a, L. B., Kijazi, A. L., Luhunga, P. M., Ng’ongolo, H. K. and Mtongor, H. I., Spatial and temporal analysis of rainfall and temperature extreme indices in Tanzania. Atmos. Climate Sci., 2017, 7, 525–539.
- Shrestha, S., Effects of climate change in agricultural insect pest. Acta Sci. Agric., 2019, 3, 74–80.
- Pathak, H., Aggarwal, P. K. and Singh, S. D., Climate Change Impact, Adaptation and Mitigation in Agriculture, Methodology for As-sessment and Applications; Indian Agricultural Research Institute: New Delhi, 2012, pp. 1–302; ISBN 978-81-88708-82-6.
- Luhunga, P. M., Kijazi, A. L., Chang’a, L., Kondowe, A., Ng’ongolo, H. and Mtongori, H., Climate change projections for Tanzania based on high-resolution regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX)-Africa. Front. Environ. Sci., 2018, 6, 122.