Flood and Drought Prediction Using the Machine Learning Algorithm Support Vector Regression
K. Sangeetha1, K. Mohan Kumar2
1K. Sangeetha *, PG & Research Department of Computer Science, Rajah Serfoji Government College, Thanjavur, Affiliated to Bharathidasan University, T.N, India.
2K. Mohankumar, Head, PG& Research Department of Computer Science, Rajah Serfoji Government College, Thanjavur, Affiliated to Bharathidasan University, T.N, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 5194-5199 | Volume-9 Issue-1, October 2019 | Retrieval Number: A3001109119/2019©BEIESP | DOI: 10.35940/ijeat.A3001.109119
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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: Flood and drought are frequently happening natural disasters in most of the countries. These disasters can cause considerable damage to agriculture, ecology and economy of the country. Mitigating the impacts of flood and drought is a valuable help to the human being. The main cause of these disasters is precipitation. If the past precipitation data are analyzed properly, the future flood and drought events can be easily found. Prediction using the Standard Precipitation Index (SPI) is a way to find the wet or dry condition of a region or country. In this paper the SPI values with different lead times are calculated for a long period of time. These SPI indices are analysed by a predictive model using the machine learning algorithm called Support Vector Regression (SVR) with RBF (Radial Basis Function) kernel. In this model the Grid Search approach is used for optimization. The forecast result of this predictive model shows the predictive skill of the SVR-RBF kernel.
Keywords: Flood and drought, Support Vector Regression, SPI.