Prediction of Rainfall in India using Artificial Neural Network (ANN) Models
Автор: Santosh Kumar Nanda, Debi Prasad Tripathy, Simanta Kumar Nayak, Subhasis Mohapatra
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 12 vol.5, 2013 года.
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In this paper, ARIMA(1,1,1) model and Artificial Neural Network (ANN) models like Multi Layer Perceptron (MLP), Functional-link Artificial Neural Network (FLANN) and Legendre Polynomial Equation ( LPE) were used to predict the time series data. MLP, FLANN and LPE gave very accurate results for complex time series model. All the Artificial Neural Network model results matched closely with the ARIMA(1,1,1) model with minimum Absolute Average Percentage Error(AAPE). Comparing the different ANN models for time series analysis, it was found that FLANN gives better prediction results as compared to ARIMA model with less Absolute Average Percentage Error (AAPE) for the measured rainfall data.
Autoregressive Integrated Moving Average Model, ARIMA, Autocorrelation Function, FLANN, MLP, Legendre neural Network (LeNN)
Короткий адрес: https://sciup.org/15010496
IDR: 15010496
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