Open AccessFlood and Drought Prediction Using the Machine Learning Algorithm Support Vector Regression
Author(s) -
K. Sangeetha*,
K. Mohankumar
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a3001.109119
Subject(s) - flood myth , support vector machine , natural disaster , machine learning , precipitation , hyperparameter optimization , radial basis function , kernel (algebra) , radial basis function kernel , algorithm , kriging , regression , artificial intelligence , computer science , meteorology , geography , mathematics , kernel method , statistics , artificial neural network , archaeology , combinatorics
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.