Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall
Prediction of rainfall for a river basin is of utmost importance for planning and design of irrigation and drainage systems as also for command area development. Since the distribution of rainfall varies over space and time, it is required to analyze the data covering long periods and recorded at various locations to arrive at reliable information for decision support. Further, such data need to be analyzed in different ways, depending on the issue under consideration. In the present study, Extreme Value Type-1 (EV1) distribution based on statistical approach and Multi Layer Perceptron (MLP) network based on Artificial Neural Network (ANN) is adopted for prediction of rainfall at Fatehabad and Hansi. The performance of the statistical and ANN approaches used in rainfall predication are evaluated by model performance indicators viz., correlation coefficient, model efficiency and mean absolute percentage error. The study shows the MLP is found to be better suited network for prediction of rainfall at Fatehabad whereas EV1 for Hansi.
Artificial Neural Network, Correlation Coefficient, Extreme Value Type-1, Mean Absolute Percentage Error, Model Efficiency, Multi Layer Perceptron
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[N.Vivekanandan (2017) Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall IJIRCST Vol-5 Issue-4 Page No-323-327] (ISSN 2347 - 5552). www.ijircst.org
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