With a steady increase in impervious surfaces due to urbanization in Korea, there is a growing burden and an urgent need to fund better management of nonpoint sources of pollution and stormwater. A prerequisite for securing the necessary financial resources is the determination of basic data for the accurate calculation of impervious surfaces as the basis for estimating the costs of nonpoint source pollution control and billing of stormwater utility fees. This requires the extraction of landcover information in a Geographic Information System (GIS) environment and development of a landcover map that accurately delineates pervious surfaces within impervious surface areas. However, since landcover maps in Korea are generated and updated at irregular intervals, it is difficult to properly track land use changes. To address this problem, this study has developed a new method for the detailed updating of landcover maps in developing urban areas to facilitate the calculation of stormwater utility fees. Sejong City was selected as the study site because it has experienced large-scale land use changes due to the recent relocation of the national administrative capital and continuous urbanization. The methodology proposed in this study is based on various spatial data such as aerial photographs and digital topographic maps and follows four process steps: preprocessing, first and second updates of landcover information, and quality assurance. In a test of this method, a total of 19,049 reclassified items were generated in the first and second updates, affecting a total area of 26.49 km² within the original landcover map. The accuracy of these updates reached 99.78%, considering the changed areas and rate of change. This study provides fundamental data for further application of a stormwater utility fee policy in Korea. However, further research is required to automate the generation of accurate pervious/impervious maps and develop pertinent guidelines so that individual municipal and provincial governments can generate and update their own pervious/impervious maps as a basis for calculating the impervious surfaces in their regions.

The majority of ocean pollution stems from terrestrial sources, with more than 80% attributed to chemicals, industrial waste, toxic metals, plastics, sewage, and other land-based materials. Therefore, the management of ocean pollution must initiate from terrestrial interventions, necessitating the control of pollutants discharged from land sources and the crucial pursuit of identifying the roots of ocean pollution. Identifying the causes of ocean pollution enables the exploration of mitigation strategies for the entities responsible for pollution generation and facilitates the formulation of relevant policies. Furthermore, it can contribute to imposing costs for purification and raising awareness about the seriousness of ocean pollution. However, Rivers and streams that lead to the ocean are influenced by various changing factors as they pass through the land, making it a globally challenging task to pinpoint the sources of pollution. Accordingly, in this study, we collected water quality observation data measured at five points in a specific estuary area and water quality data from five areas near the source of pollution, and conducted a study to calculate the contribution of pollution from the source. We performed statistical analysis and machine learning-based pollution source analysis, and developed an improved artificial intelligence model proposed in this study that complements the limitations of existing analysis methods. The variables used in the analysis were POC average concentration, POC δ13C, PN average concentration, PN δ15N, average concentration, and DOC δ13C. For basic data analysis, data distribution analysis, similarity/discrimination analysis, and clustering analysis of variables by branch were performed. Basic data analysis allowed for dividing the data's characteristics into four groups, but discrepancies in similarities emerged among items based on each analysis method, limiting the meaningfulness of the data analysis. For this, we analyzed which pollutants contaminated the five points in the river estuary using machine learning techniques such as XGBoost and a deep learning neural network, an artificial neural network model. The XGBoost analysis categorized pollution sources for each location into 1 to 2 categories, showing accuracies ranging from 51.04% to 99.92%. However, due to the intrinsic nature of machine learning, predicted values tend to maximize similarity to the most similar pollutant source, resulting in extreme values exceeding 99%. The artificial neural network analysis resulted in the classification of 2 or more pollution sources for each location, with accuracies ranging from 33.23% to 62.45%. This is considered a result of relatively lower accuracy due to the unique characteristics of each location. To overcome the limitations of each model, this study created an integrated model that aggregates results from multiple models to determine the similarity. The analysis using the integrated model effectively identified pollution sources excellently without encountering extreme accuracy issues. To ensure the reliability of future pollution contribution assessment models, determining pollution contribution through isotopic fraction analysis will be necessary.