Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Dinesh Kumar, P.
- Coral Mortality in the Gulf of Mannar, Southeastern India, Due to Bleaching Caused by Elevated Sea Temperature in 2016
Abstract Views :257 |
PDF Views:101
Authors
J. K. Patterson Edward
1,
G. Mathews
1,
K. Diraviya Raj
1,
R. L. Laju
1,
M. Selva Bharath
1,
A. Arasamuthu
1,
P. Dinesh Kumar
1,
Deepak S. Bilgi
2,
H. Malleshappa
3
Affiliations
1 Suganthi Devadason Marine Research Institute, 44-Beach Road, Tuticorin 628 001, IN
2 Gulf of Mannar Marine National Park, Ramanathapuram 623 503, IN
3 Department of Environment, Govt of Tamil Nadu, Chennai 600 015, IN
1 Suganthi Devadason Marine Research Institute, 44-Beach Road, Tuticorin 628 001, IN
2 Gulf of Mannar Marine National Park, Ramanathapuram 623 503, IN
3 Department of Environment, Govt of Tamil Nadu, Chennai 600 015, IN
Source
Current Science, Vol 114, No 09 (2018), Pagination: 1967-1972Abstract
Intensive underwater surveys have been conducted to assess the extent of coral bleaching and subsequent mortality in the Gulf of Mannar between March and October 2016. The extent of bleaching was 23.92% ± 10.55% during the period between March and June 2016, and the live coral cover was drastically reduced to 22.69 ± 9.07% during October 2016 with a mortality of 16.17 ± 8.46%. Fast-growing coral forms, including the genera Acropora, Montipora and Pocillopora were most affected, not only by bleaching but also by severe mortality. Boulders, including the genera Porites, Favia and Favites were found to be resistant to bleaching. During the bleaching period, water temperature was between 31.2°C and 32.6°C. The current bleaching is in alignment with the third global coral bleaching event which occurred between 2014 and 2017. Management interventions, including protection and rehabilitation using the native resistant coral species will not only help in the recovery process, but also increase the live coral cover.Keywords
Bleaching, Climate Change, Coral Reefs, Mortality, Sea Surface Temperature.Full Text
References
- Buddemeier, R. W., Baker, A. C., Fautin, D. G. and Jacobs, J. R., The daptive hypothesis of bleaching. In Coral Health and Disease (eds Rosenberg, E. and Loya, Y.), Springer-Verlag, Berlin, 2004, pp. 427–444.
- Pandolfi, J. M. et al., Global trajectories of the long-term decline of coral reef ecosystems. Science, 2003, 301, 955–958.
- Hughes, T. P. and Connell, J. H., Multiple stressors on coral reefs: a long-term perspective. Limnol. Oceanogr., 1999, 22, 932–940.
- Hughes, L., Steffen, W. and Rice, M., Australia’s Coral Reefs under Threat from Climate Change, Climate Council of Australia Ltd, Potts Point, 2016, pp. 1–22.
- Heron, S. F. et al., Validation of reef-scale thermal stress satellite products for coral bleaching monitoring. Remote Sensing, 2016, 8(1), 59.
- Eakin, C. M. et al., Global coral bleaching 2014-17. Reef Encounter, 2016, 31(1), 20–26.
- National Oceanic and Atmospheric Administration, NOAA declares third ever global coral bleaching event: bleaching intensifies in Hawaii, high ocean temperatures threaten Caribbean corals. 8 October 2015; http://www.noaanews.noaa.gov/stories2015/100815-noaa-declares-third-ever-globalcoral-bleaching-event.html
- Hoegh-Guldberg, O. and Ridgway, T., Coral bleaching comes to the Great Barrier Reef as record-breaking global temperatures continue. The Conversation, 21 March 2016; http://theconversation.com/coral-bleaching-comes-to-the-great-barrier-reef-as-record-breaking-global-temperatures-continue-56570
- Wake, B., Snapshot: snow white coral. Nature Climate Change, 2016, 6, 439.
- Edward, J. K. P., Mathews, G., Patterson, J., Wilhelmsson, D., Tamelander, J. and Linden, O., Coral reefs of the Gulf of Mannar, southeastern India – distribution, diversity and status. Suganthi Devadason Marine Research Institute Special Publication no. 12, 2007.
- Edward, J. K. P., Mathews, G., Raj, K. D. and Tamelander, J., Coral reefs of the Gulf of Mannar, southeastern India – observations on the effect of elevated SST during 2005−2008. In Proceedings of the 11th International Coral Reef Symposium, Fort Lauderdale, Florida, USA, 2008.
- Edward, J. K. P., Annual coral bleaching in Gulf of Mannar. South Indian Coast. Mar. Bull., 2009, 1(1&2), 18.
- Raj, K. D. and Edward, J. K. P., Observations on the reproduction of Acropora corals along the Tuticorin coast of the Gulf of Mannar, southeastern India. Indian J. Geomar. Sci., 2010, 39(2), 219–226.
- Edward, J. K. P., Mathews, G., Raj, K. D., Thinesh, T., Patterson, J., Tamelander, J. and Wilhelmsson, D., Coral reefs of Gulf of Mannar, India – signs of resilience. In Proceedings of the 12th International Coral Reef Symposium, Cairns, Australia, 2012.
- English, S., Wilkinson, C. and Baker, V., Survey Manual for Tropical Marine Resources, ASEAN Australian Marine Science Project: Living Coastal Resources, Townsville, 1997.
- Hoegh-Guldberg, O., Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshwater Res., 1999, 50, 839–866.
- Eakin, C. M., Lough, J. M. and Heron, S. F., Climate, weather and coral bleaching. In Coral Bleaching: Patterns, Processes, Causes and Consequences (eds van Oppen, M. J. H. and Lough, J. M.), Springer, Berlin, Heidelberg, 2009, pp. 41–67.
- Grottoli, A. G. et al., The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Global Change Biol., 2014, 20, 3823–3833.
- NOAA, Global Analysis – Annual, 2015; https://www.ncdc.noaa.gov/sotc/global/201513
- Hughes, T., Coral crisis: great barrier reef bleaching is ‘the worst we’ve ever seen’. Nature, 13 April 2016; http://www.nature.com/news/coral-crisis-greatbarrier-reef-bleaching-is-the-worst-we-ve-ever-seen1.19747
- Brown, B. E., Dunne, R. P., Goodson, M. S. and Douglas, A. E., Bleaching patterns in reef corals. Nature, 2000, 404, 142–143.
- Coles, S. L. and Brown, B. E., Coral bleaching – capacity for acclimatisation and adaptation. Adv. Mar. Biol., 2003, 46, 183–223.
- Oliver, T. A. and Palumbi, S. R., Many corals host thermally resistant symbionts in high-temperature habitat. Coral Reefs, 2011, 30(1), 241–250.
- Raj, K. D. et al., Climate change-induced coral bleaching in Malvan Marine Sanctuary, Maharashtra, Arabian Sea. Curr. Sci., 2018, 114(2), 384–387.
- Ainsworth, T. D. et al., Climate change disables coral bleaching protection on the Great Barrier Reef. Science, 2016, 352(6283), 338–342.
- Raj, K. D., Mathews, G. and Edward, J. K. P., Reproductive success of restored coral colonies in Vaan Island, Gulf of Mannar, Southeastern India. Indian J. Geomarine Sci., 2015, 44(4), 589–598.
- Predicting the Area and Production of Sugarcane in Tamil Nadu, India Using Neural Networks
Abstract Views :97 |
PDF Views:58
Authors
Affiliations
1 Department of Agriculture, Karunya University, Coimbatore 741 114, IN
1 Department of Agriculture, Karunya University, Coimbatore 741 114, IN
Source
Current Science, Vol 124, No 4 (2023), Pagination: 500-504Abstract
Sugarcane is a major cash crop in India, grown in almost 5 million hectares with a production of 339 million tonnes. Tamil Nadu contributes significantly to the production of sugarcane. Data from the past year show a huge fluctuation in the area and production of sugarcane in the state. Predicting the area and production employing traditional modelling techniques fails because the assumptions are never attained in the field. To overcome this, soft computing techniques like artificial neural networks (ANNs) are used. In this study, a multilayer perceptron neural network (MLP-NN) with back-propagation was used to predict the area and production of sugarcane in Tamil Nadu. The MLP-NN (2,2) model predicts the area with minimum mean absolute error (MAE; 18.139) and root mean squared error (RMSE; 23.058) values and with high accuracy (99%). For production, the MLP-NN (2,1) model estimates minimum MAE (24.875) and RMSE (31.199) values with high accuracy (99%). So, MLP-NN (2,2) and MLP-NN (2,1) are the best ANN models to predict the area and production of sugarcane in Tamil Nadu respectively. Additionally, ANN models perform better in predicting nonlinear stochastic data.Keywords
Back Propagation, Multilayer Perceptron, Neural Network, Nonlinear Stochastic Data, Sugarcane Area and Production.Full Text
References
- Suresh, K. K. and Krishna Priya, S. R., Forecasting sugarcane yield of Tamil Nadu using ARIMA models. Sugar Tech., 2011, 13, 23–26.
- Solomon, S., Sugarcane agriculture and sugar industry in India: at a glance. Sugar Tech., 2014, 16, 113–124.
- Bala, B. K., Ashraf, M. A., Uddin, M. A. and Janjai, S., Experimental and neural network prediction of the performance of a solar tunnel drier for drying jackfruit bulbs and leather. J. Food Process Eng., 2005, 28, 552–566.
- Ehret, D. L., Hill, B. D., Raworth, D. A. and Estergaard, B., Artificial neural network modelling to predict cuticle cracking in greenhouse peppers and tomatoes. Comput. Electron. Agric., 2008, 61, 108–116.
- Savin, I. Y., Stathakis, D., Negre, T. and Isaev, V. A., Prediction of crop yields with the use of neural networks. Russ. Agric. Sci., 2007, 33, 361–363.
- Rahman, M. M. and Bala, B. K., Modelling of jute production using artificial neural networks. Biosyst. Eng., 2010, 105, 350–356.
- Sanghani, A., Bhatt, N. and Chauhan, N. C., A review of soft computing techniques for time series forecasting. Indian J. Sci. Technol., 2016, 9, 93–102.
- Hornik, K., Stinchcombe, M. and White, H., Multilayer feedforward networks are universal approximators. Neural Networks, 1989, 2, 359-366.
- Maier, H. R. and Dandy, G. C., Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model. Softw., 2000, 15, 101–124.