• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.171-181
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• 2022

The real estate price index plays key roles as quantitative data in real estate market analysis. International organizations including OECD publish the real estate price indexes by country, and the Korea Real Estate Board announces metropolitan-level and municipal-level indexes. However, when the index is set on the smaller spatial unit level than metropolitan and municipal-level, problems occur: missing values. As the spatial scope is narrowed down, there are cases where there are few or no transactions depending on the unit period, which lead index calculation difficult or even impossible. This study suggests a supervised learning-based machine learning model to compensate for missing values that may occur due to no transaction in a specific range and period. The models proposed in our research verify the accuracy of predicting the existing values and missing values.

• Journal of Wetlands Research
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• v.7 no.4
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• pp.97-107
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• 2005

As the distributed model is developed and widely used, the accuracy of a rainfall measurement and more dense rainfall observation network are required for the reflection of various spatial properties. However, in reality, it is not easy to get the accurate data from dense network. Generally, we could not have the proper rainfall gages in space and even we have proper network for rainfall gages it is not easy to reflect the variations of rainfall in space and time. Often, we do also have missing rainfall data at the rainfall gage stations due to various reasons. We estimate the distribution of mean areal rainfall data from the point rainfalls. So, in the aspect of continuous rainfall property in time, we should fill the missing rainfall data then we can represent the spatial distribution of rainfall data. This study uses the Fuzzy-Genetic algorithm as a interpolation method for filling the missing rainfall data. We compare the Fuzzy-Genetic algorithm with arithmetic average method, inverse distance method, normal ratio method, and ratio of distance and elevation method which are widely used previously. As the results, the previous methods showed the accuracy of 70 to 80 % but the Fuzzy-Genetic algorithm showed that of 90 %. Especially, from the sensitivity analysis, we suggest the values of power in the equation for filling the missing data according to the distance and elevation.