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Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index
Jalalkamali, A.; Moradi, M. & Moradi, N.
Abstract
Drought is among the most important natural
disasters influencing different aspects of human life. In
recent decades, intelligent techniques have shown to be
highly capable of modeling and forecasting nonlinear and
dynamic time series. Hence, the present study aimed to
forecast drought using and comparing the multilayer perceptron
artificial neural network (MLP ANN), adaptive
neuro-fuzzy inference systems (ANFIS), support vector
machine (SVM) model, and the autoregressive integrated
moving average (ARIMAX) multivariate time series. To
this end, the precipitation data obtained from the Yazd
synoptic station for a 51-year statistic period were used.
Moreover, the humidity levels for short-term (3 and
6 months) and long-term (9, 12, 18, and 24 months) periods
were calculated using the Standardized Precipitation
Index (SPI). Next, based on the results of calculations, the
1961–2002 period was selected as the control group and
the 2003–2012 period was selected as the experimental
group. In order to forecast the SPI for the t + 1 period,
values of SPI, precipitation, and temperature of previous
eras were used. Results indicated that in a 9-months period
(as the timescale), the ARIMAX model gives SPI values
and forecast drought with more precision than the SVM,
ANFIS, and MLP models.
Keywords
Drought; Forecasting; SPI; ANFIS; ANN; ARIMAX; SVM; Yazd
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