@article {
author = {Jouybari Moghaddam, Yaser and Rostami, Seyyed Qasem},
title = {Fusion of Markov Chain and SAX Method for Drought Probability Analysis (Case Study: Eastern District of Isfahan, Iran)},
journal = {Environmental Management Hazards},
volume = {5},
number = {3},
pages = {295-311},
year = {2018},
publisher = {},
issn = {2423-415X},
eissn = {2423-4168},
doi = {10.22059/jhsci.2018.267316.414},
abstract = {Drought is one of the most powerful natural disasters, which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area. Monitoring and prediction the severity of the drought can be useful in the management of the natural disaster caused by drought. Many indices were used in predicting droughts such as SPI, VCI, and TVX. In this paper, based on three data sets (rainfall, NDVI, and land surface temperature) which are acquired from MODIS satellite imagery, time series of SPI, VCI, and TVX in time-limited between winters 2000 to summer 2015 for the east region of Isfahan province were created. Using these indices and fusion of symbolic aggregation approximation and hidden Markov chain drought was predicted for fall 2015. For this purpose, at first, each time series was transformed into the set of quality data based on the state of drought (5 group) by using SAX. Algorithm then the probability matrix for the future state was created by using Markov hidden chain. The fall drought severity was predicted by fusion the probability matrix and state of drought severity in summer 2015. The prediction based on the likelihood for each state of drought includes severe drought, middle drought, normal drought, severe wet and middle wet. The analysis and experimental result from proposed algorithm show that the product of this algorithm is acceptable and the proposed algorithm is appropriate and efficient for predicting drought using remote sensor data. Introduction Drought is such natural disasters that usually covers a large area and have long-term effects. Due to the impact of this phenomenon on weather, agriculture, water and socio-economic issues, it can have an infrastructural and destructive effect on the environment. In general, due to drought dependence on multiple parameters and its complexity, a definition for this phenomenon is no easy task [1]. Drought forecasting can have a useful role in mitigation of this phenomenon's damages, which depends on the exact definition of drought and linking drought with a series of associated indices. Several parameters have been defined on this basis to be modeled during the period of drought forecasting. Based on studies in the field, these indicators can be divided into two general categories meteorological indicators and satellite remote sensing indicators [6]. The most common weather indices are Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI).Generally, satellite indices are vegetation index (VI) and land surface temperature (LST) and its derivatives [3]. Currently, the analysis of time series of drought indicators used to predict drought which is forecast the absolute numerical value based on an extrapolation of the function fitted to the time series. Firstly, if the drought is a phenomenon with qualitative nature, so even if we express this phenomenon numerically, ultimate results must be expressed qualitatively. Secondly, the nature of the predictions is always probabilistic thus providing a fixed amount is not meaningful. Another problem of existing methods is in determining the communicational interval of any data with previous data. Due to the uncertainty in determining these ranges (delay), an error entered into the prediction process. In this study, prediction carried out in a way that the preceding be considered in it. Material and Methods: This research study area is eastern Isfahan Province where has five sub-regional. The study area has semi-desert climate and is located in the range of latitude N "40 '29 ° 32 and N" 47 '45 ° 32 and longitude E "29 '42 ° 51 to" E52 '59 ° 51. Figure 1 shows the study area. The data used in this research is land surface temperature (LST), and normal differential vegetation index (NDVI) from MODIS satellite products that are free and downloaded from the NASA Earth Observations (NEO) Other data were also used is precipitation data from TRMM. The data for a period of 16 years from winter 2000 to summer 2015 were downloaded. A. Symbolic Aggregate Approximation method Symbolic Aggregate approximation method is one of the approaches to show time series offered by Lin et al. in 2003. This process took a time series as input and turned it into a set of strings as output [15]. By the use of Symbolic Aggregate Approximation method, a time series of arbitrary length n can be converted to an arbitrary string with length w (w