Early Prediction of Rainfall in Coastal Region using Optimized Advanced ANN
Rakesh Kumar Godi

Rakesh Kumar Godi Department of Information Technology, CVR College of Engineering, Hyderabad, India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 15 March 2019 | Manuscript published on 30 July 2019 | PP: 126-130 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1713078219/19©BEIESP | DOI: 10.35940/ijrte.B1713.078219
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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: Agriculture is major resource for Indian economy and rainfall prediction plays a vital role for proper agriculture. It is very complex to predict the rainfall and due to globalization, the uncertainty is more to get rainfall at the expected monsoon. The current figuring approach is observed to be extremely powerful in creating models which causes for people to adjust the circumstance. In this paper, rainfall expectation for Karnataka state is done with Artificial Neural Network (ANN). Another techniquecalled Teaching Learning Based advancement [16] (mTLBO) is utilized to prepare the loads of the ANN produced for result expectation. Later examination is carried with established back Propagation learning approach and mTLBO (a variation of traditional TLBO). The outcomes result of ANN-mTLBO over ANN-BP [38] on given datasets. The main aim of our work will be helpful in estimating the drought conditions in Karnataka from the forecasts.
Keywords: Economy Region using Optimized Advanced ANN
Scope of the Article: Social Sciences