information while minimizing costs to a small number of rain gauge stations. This could also allow a new rain gauge station to be operated at a point whereat observations are needed thought the cost saved. Therefore, the optimal rain gauge network must be constructed based on the spatial distribution of rainfall, and the appropriateness of the constructed network must be evaluated. Information entropy approaches have been widely used to evaluate the design and uncertainties of networks for collecting hydrological data [3; 4; 5; 6; 7; 8]. The basis for designing a rain gauge network using entropy is that the network must have the least amount of overlapping information possible, which means that all rain gauge stations must be independent of each other, as far as possible. One study [9] used the maximum entropy value of rainfall information to develop a model that predicts long-term. One study [10] used the maximum value of entropy to determine the threshold of rainfall. One study [11] objectively evaluated the groundwater quality monitoring network of the Gaza strip. Palestine, using the concept of entropy. The study [12] investigated network designs for different Canadian basins using marginal entropy, joint entropy, and transmitted information. The study [13] showed that the entropy theory can help design the optimal water-level station in the Netherlands. The study [14] developed the maximum information minimum redundancy criterion for the evaluation and planning of water level monitoring networks. Additionally, the study [14] used information theory to quantify the common information among all the monitoring stations of the system. The study [15] evaluated the optimal stations for rainfall data using the information theory of Shannon. The optimal gauge station combination was proposed based on the maximum information transmission using joint entropy. The study [4] calculated the uncertainty of hydrological data based on entropy and evaluated the degree of uncertainty reduction. The studies [16; 17] evaluated the gauge station network in Louisiana, USA, and defined the optimal number of stations. The study [6] approached the optimal gauge network design by evaluating the information flow measurement between stations based on entropy. The study [18] used entropy to find locations that could maximize optimal watercourse and hydrological information. The study [19] investigated temporal variations in the quantity and uncertainty of rainfall events using entropy. They showed that entropy is a means of probabilistically comparing and evaluating regional precipitation characteristics and that it can be used as a measure of evaluating water resources. Entropy cannot reflect the spatial distribution of rain gauge networks because it is calculated based on the probability distribution of observation data. As such, studies have been conducted to reflect the spatial distribution of rain gauge stations along with entropy. The study [20] used the minimum variance estimation method of kriging to select the optimal rain gauge station locations in the basin. The study [21] proposed a method for optimal data collection at random locations through the variance reduction analysis based on kriging. Moreover, studies have been conducted using the entropy theory, which is a concept of information quantity that quantitatively represents a reduction in uncertainty. The study [22] used kriging and entropy to evaluate the optimal rain gauge stations considering the spatial distribution of stations. Kriging was used in the reconstruction of the precipitation of a basin to determine the spatial distribution of rain gauge stations. The optimal gauge station locations were then selected from the stations constructed in the basin through joint entropy. The study [23] secured spatiality through rainfall prediction using kriging and determined the minimum number of rain gauge stations required in a Taiwanese basin using joint entropy. The study [24] used kriging and joint entropy for the optimal network design of Shanghai.