# Rainfall Standard of Disaster Prediction for Agricultural Droughs in S. Korea
## 1 Introduction  
Unlike other natural disasters, such as floods, typhoons, earthquakes and tsunamis, droughs occur slowly, expansively and persistently for months or years. Droughs cause extensive damage, and their spatial scale or intensity is difficult to identify [1]. As part of the impact of global climate change, the rising temperature is projected to cause more frequent droughs, which will presumably last for years, exerting an enormous impact on agriculture and water resources. Recently, years-long droughs have been defined as mega-droughs in studies [2; 3; 4; 5; 6]. Furthermore, many researchers have developed methods of assessing and predicting droughs and analyzed their applicability, in view of diverse hydrologic factors, with the intent to minimize the damage from droughs. Utilizing parameters such as rainfall data, drought indices, statistical methods and climate models, drought researchers have assessed and predicted droughs, as well as developed some quantitativedisaster indices. Hydrologic drought indices such as PDSI (Palmer Drought Severity Index), SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) are widely used to assess droughs and applied in research as the reference points for predicting and assessing short-term or prolonged droughs [7; 8]. Neuro-Fuzzy, Log linear and ANN (Artificial Neural Network) methods of predicting droughs were used to analyze the drought indices and verify the accuracy thereof [9; 10]. Moreover, to develop the methods of predicting and assessing droughs in relation to future climate, hydrologic factors from climate change models were used to calculate the drought indices by means of such analytical methods as MLP (Multi-Layer Perceptron), ANN, ANFIS (Adaptive Neuro-Fuzzy Inference System), SVM (Support Vector Machine), ARIMAX (Autoregressive Integrated Moving Average with Explanatory Variable) and SVR (Support Vector Regression) [11; 12; 13; 14; 15; 16]. In North America, data from the Multi Model Ensemble Prediction and Global Precipitation Climatology Centre were used to calculate the SPI based on a 100 km grid for analytical drought predictions. In other regions, high-resolution drought-prediction models were developed and applied based on the SPI and SPEI [17; 18]. In Virginia, a US state, long-term trends of droughs were calculated in PDSI to develop some reference points for a drought early warning system [19]. The drought-duration thresholds in relation to hydrologic shapes over varying periods were used as a parameter to predict droughs via the PDSI [20]. Although many previous studies calculated drought indices and assessed the accuracy thereof to predict droughs, no research has analyzed the extent of rainfall or set up applicable reference points by comparatively analyzing hydrologic factors associated with the actual droughs. In case of South Korea, drought occurs in the same region for three to four months, rather than long-term drought, as in the United States. Therefore, it is necessary to establish standards for disaster prediction and disaster prevention through drought damage analysis. By applying the Time Series Analysis or ANA (Artificial Neural Network) to non-linear actual rainfall, some researchers predicted and analyzed droughs effectively. Based on the observed rainfall data, others applied the multiple linear regression analysis, PN (Percent of Normal Precipitation) and UM (Unified Model) to short-term droughs to develop prediction techniques and test their accuracy [21; 22]. To predict prolonged droughs, many studies applied non-linear hydrologic data to the probabilistic analysis of ANN and ARIMA [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Some studies reported that SVR analysis proved to be more accurate than ANN when it comes to predicting a 12-month-long drought [34; 35; 36]. However, criteria for disaster management by using long-term damage data from past droughs have not been collected. In addition, it was only a study on disaster prediction, and studies on droughs were not conducted.