• Journal of Environmental Impact Assessment
• /
• v.20 no.5
• /
• pp.705-716
• /
• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Korean Journal of Remote Sensing
• /
• v.40 no.4
• /
• pp.363-375
• /
• 2024

Waterbody change detection using satellite images has recently been carried out in various regions in South Korea, utilizing multiple types of sensors. This study utilizes optical satellite images from Landsat and Sentinel-2 based on Google Earth Engine (GEE) to analyze long-term surface water area changes in four monitored small and medium-sized water supply dams and agricultural reservoirs in South Korea. The analysis covers 19 years for the water supply dams and 27 years for the agricultural reservoirs. By employing image analysis methods such as normalized difference water index, Canny Edge Detection, and Otsu'sthresholding for waterbody detection, the study reliably extracted water surface areas, allowing for clear annual changes in waterbodies to be observed. When comparing the time series data of surface water areas derived from satellite images to actual measured water levels, a high correlation coefficient above 0.8 was found for the water supply dams. However, the agricultural reservoirs showed a lower correlation, between 0.5 and 0.7, attributed to the characteristics of agricultural reservoir management and the inadequacy of comparative data rather than the satellite image analysis itself. The analysis also revealed several inconsistencies in the results for smaller reservoirs, indicating the need for further studies on these reservoirs. The changes in surface water area, calculated using GEE, provide valuable spatial information on waterbody changes across the entire watershed, which cannot be identified solely by measuring water levels. This highlights the usefulness of efficiently processing extensive long-term satellite imagery data. Based on these findings, it is expected that future research could apply this method to a larger number of dam reservoirs with varying sizes,shapes, and monitoring statuses, potentially yielding additional insights into different reservoir groups.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.925-938
• /
• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.