Drainage water level classification using support vector machines

Environmental monitoring is one of the key approaches to safeguard the global ecosystem. Classifications of different water levels facilitate in preserving water reserves and maintain the equilibrium in the ecosystem. In this paper we shall inspect the classification of drainage water levels in Canada. A powerful statistical tool called support vector machines is used to classify the said drainage water remote sensed spatial data sets. To boost the performance of support vector machines classifier a new generic algorithm based on parametric distribution model will be proposed. Later several evaluation metrics like kappa statistics are used to compare the results of the proposed algorithm with multi-layer perceptron neural networks and naive bayes classifiers.