Prediction index drought use neural network based rainfall

Abstract

Prolonged dry seasons compared to rainy seasons often lead to drought, making drought index observations essential. In Indonesia, drought monitoring commonly uses the standardized precipitation index (SPI), yet there is no common standard for drought index measurement. Therefore, this research applies the Z-score index (ZSI) and China-Z index (CZI), which, like SPI, are rainfall-based drought indices but have rarely been explored in previous research. To predict ZSI and CZI, this research compares the weighted moving average (WMA) and multilayer perceptron (MLP) methods. Two input scenarios are tested: the previous two periods (t-2, t-1) and the previous three periods (t-3, t-2, t-1). The results show that MLP outperforms WMA, with the best performance achieved by the MLP model at a mean absolute percentage error (MAPE) of 4.177% using the three variable input scenario and MLP architecture 3-6-10-1.