## 1 Introduction  
Drought is reckoned to be one of the prevalent and economically injurious climatic phenomena. Wireless sensor networks are one of the most efficient techniques for real-time monitoring of areas that are prone to disasters [1]. [PERSON] et al. [2] introduced a reconfigurable antenna board to control nearly the entire planar antenna design through a phase transition procedure with a combined independently addressable micro heater matrix. [PERSON] [3] studied the annual precipitation based on nonlinear input-output (NIO), a nonlinear autoregressive neural network (NARNN), and NARNN with exogenous input (NARNNX). They showed that the accuracy of the NARNNX was better than that of the NARNN and NIO. Decreasing of drought aftermath requires management and nonstop monitoring of this incident [4; 5]. Centers such as National Aeronautics and Space Agency, NASA and National Oceanic and Atmospheric Administration, NOAA, by means of various tools like Tropical Rainfall Measuring Mission, TRMM, have made a conspicuous bunch of climatic data with different spatial and temporal resolutions in global and regional scale available to users [6]. According to [PERSON] et al. [7], drought indices are single values that determine the current situation of the region precipitation. Considering the negative effects of drought on water resources, [PERSON] [8] suggested alternative managerial solutions for drought seasons. Using TRMM and stations data, [PERSON] et al. [9] addressed the monitoring of the droughts happening in four river basins in Africa. Their results showed that TRMM data with high spatial resolutions are reliable for drought monitoring of those rivers. [PERSON] et al. [10] used TRMM images of an 11-year period, as well as data of the upstream and downstream meteorological stations, to study droughts. The comparison between the results obtained from standardized precipitation index (SPI), TRMM images, and meteorological stations in the one- and three-month scales was indicated by a more than 60% conformity. [PERSON] et al. [11], using TRMM data and monthly precipitation data of meteorological stations in Morocco, concentrated on droughts in the one- and three-month scales with standardized precipitation index (SPI). Their results showed a good agreement among TRMM data and meteorological station data in the three-month scale. [PERSON] and [PERSON] [12] studied the droughts of Huanghuai Hai plain through downscaling of TRMM data with the spatial resolution of \(0.25\times 0.25\) degrees to 0.05 degrees, and also by using SPI and stations' data. Their results corresponded high accuracies to the downscaled data and showed that this approach could be used in drought monitoring. [PERSON] et al. [13] compared the TRMM data with the APHRODITE database to investigate the droughts of Pemali-coral river basin. Their results were compared to the meteorological stations' data to infer their apt procedure. The TRMM data were yet of higher performance in monitoring of hydrological droughts than APHRODITE data. Using TRMM monthly precipitation data, [PERSON] et al. [14] studied the droughts in Mexico in the duration of 1998-2013. The results showed that the data, though not enjoying adequate time periods, could be well applied in the monitoring of droughts in the region. [PERSON] et al. [15] investigated the drought situation, along with water resources, of Oum-Er-Rbia basin in Morocco by means of TRMM precipitation data. They showed, through their results, that TRMM precipitation data can be employed in monthly and annual scale in monitoring and management of water resources in ungauged watersheds of semiarid regions. By surveying groundwater recharge rates in drought periods relating to Santa Catalina Island, S. California, Harlow and Hagedorn [16] got the conclusion that the recharges have conspicuously decreased in drought periods. [PERSON] et al. [17] used drought indices to study drought monitoring in the south of Thailand.