Using SVM to predict Dissolved Oxygen Prediction

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In 2014, Malek and his team conducted a study on two lakes named Chini and Bera. They collected samples from 2005 to 2009. The data sample consisted of 11 parameters which were used to predicate Dissolved Oxygen  concentration. The Dissolved Oxygen concentration was dichotomized into three different levels such as, High, Medium and Low. They ranked the input parameters and they used forward selection method to determine the optimum parameters that yield the lowest errors and highest accuracy. The initial results showed that pH, temperature and conductivity significantly affect the prediction of Dissolved Oxygen. Then, they applied SVM model using the Anova kernel with those parameters yielded 74% accuracy rate. They concluded that using dichotomized value of Dissolved Oxygen yields higher prediction accuracy than using precise Dissolved Oxygen  value and ANOVA is the most appropriate kernel to obtain the highest accuracy.

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