Prediction and Validation of Rainfall Classes for Vaigai River Catchment using El Nino
Mahadevan Palanichamy1, Ramaswamy Sankaralingam Narayanasamy2
1Mahadevan Palanichamy, Department of School of Environmental and Construction Technology, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil – 626126, (Tamil Nadu), India.
2Ramaswamy Sankaralingam Narayanasamy, Department of Civil Engineering, Anjalai Ammal Mahalingam Engineering College, Kovilvenni, Thiruvarur District – 614403, (Tamil Nadu), India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 1412-1427 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2078078219/19©BEIESP | DOI: 10.35940/ijrte.B2078.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Extraordinary weather patterns are being observed globally during the past 30 years due to climate change resulting in variations in temperature and rainfall. Studies on long-term trend pattern of temperature and rainfall since 1980 distinctly shows a rise in mean temperature and declining rainfall trend. Due to change of climate at global level change, forecasting of rainfall with the conventional statistical analysis could not predict satisfactory results. Among the available processes, the El Niño Southern Oscillation (ENSO) cycle is considered efficient. Statistical analysis was carried out in this study so as to investigate the implication of rainfall data in seven rain gauge stations located in Vaigai River Catchment through the period from 1959 to 2016. ENSO Cycle was used also to predict rainfall for Vaigai River catchment of the Tamil Nadu State, India. Quadratic discrimination analysis (QDA) and Neural Network models are used to identify the class of rainfall classes with reference to ENSO cycle. The patterns recognized on the study area offer constructive information to administrators of water resource management, to implement the same for agriculture, water supply and power generation.
Index Terms: Climate Change, Statistical Analysis, Trend Pattern, Vaigai River Catchment, Water Resources Management.
Scope of the Article: Regression and Prediction