Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural Networks
| Autor: | Ashvin Harradon, Ashvin Gopaul, Jayrani Cheeneebash |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation Mean squared error Artificial neural network Statistics 0202 electrical engineering electronic engineering information engineering Percentage difference 020201 artificial intelligence & image processing 02 engineering and technology Autoregressive integrated moving average Mean difference Mathematics |
| Zdroj: | Environmental Management and Sustainable Development. 7:115 |
| ISSN: | 2164-7682 |
| Popis: | In this paper, two forecasting methods namely, the autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) are studied to forecast the amount of rainfall in Mauritius. Indeed due to the geographical location of Mauritius, the rainfall pattern is deeply affected by the season prevailing whereby the period of summer receives a relatively high amount of rainfall when compared to winter. As such, forecasting rainfall can help the local authorities to manage the distribution of water in the country especially during droughts. The results obtained from both methods are compared in terms of their mean square error, mean absolute difference and mean absolute percentage difference. It is then seen that artificial neural network is a much better model as it is more accurate. This is due to its nonlinearity characteristic and ability to learn and train itself. |
| Databáze: | OpenAIRE |
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