## 1 Introduction
However, because the Korean government's environmental data production policy has changed over time, some environmental data required for KEIDP calculations are no longer measured. It is therefore difficult to calculate the KEIDP for recent development projects. In particular, the Degree of Green Naturality (DGN) map, which is essential for the calculation of KEIDP, was discontinued in 2009 and is no longer available. If KEIDP is calculated by treating all DGN-related indicators as missing data, there are problems that may occur due to limitations of the AHP technique [26; 27; 28; 29; 30]. The AHP method determines the weight of each indicator by constructing a comparison matrix that evaluates all possible indicators with relative importance. If it is necessary to forcibly exclude specific indicators like KEIDP in the current situation, the relative importance must be re-evaluated from the highest criteria to the lowest indicators [31]. The biases and errors of AHP results can be larger [32]. Moreover, re-evaluating their relative importance for current development projects also requires costly and time-consuming public hearings. Thus, a new approach and data are needed to comprehensively evaluate the impact of the development project on the environment like the existing KEIDP. EIA Big Data are the result of field surveys to evaluate environmental impact statements for development projects. EIA Big Data consist of materials related to air quality, water quality, and soil quality and have been produced since 2007 by government EIA policies [33]. Since EIA Big Data have recently been provided in conjunction with a geographical information system (GIS), its usefulness is high. The measurement results can fully reflect the environmental aspects considered in the existing KEIDP, so they can be used to compare the impact of a development project, before and after the project. However, there is a limit to providing a comprehensive index by synthesizing them due to independent EIA Big Data characteristics. They cannot be used as input data for KEIDP calculation because they do not directly match. The aims of this study are (1) to develop a model that could provide values similar to existing KEIDP results using EIA Big Data, which are new environmental data for recent development projects, and (2) to analyze the trend of environmental change that occurred during the development project process using the environmental index produced from the developed model. This is to analyze how effectively Korea's EIA policy works with development projects. For the study, development projects carried out during the transition period of the EIA policy change (2010-2015) were selected. Both the environmental data used for the KEIDP calculation and EIA Big Data were available for the selected development projects. The KEIDP results were used as the reference set for the model. The EIA Big Data were used as the input set, and a multi-layer perceptron based artificial neural network (MLP-ANN) approach was used to develop a relationship between the input and reference sets. Despite the use of the new EIA Big Data, it was expected that the model results would be similar to the existing KEIDP results. In addition, an environmental index was calculated and used to analyze the environmental changes that occurred during the development projects.