• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Journal of Wetlands Research
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• v.11 no.3
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• pp.145-151
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• 2009

Recently the water quality management policy has been changed from managing the point source to controlling the nonpoint sources (NPSs) because of TMDL program. Most NPSs are accumulated on the surface during dry periods. These accumulated pollutants are washed-off during a storm event and highly impairing the water quality of the receiving water bodies. Usually NPS has high uncertainty and is hard to control because of the variability of the rainfall and watershed characteristics. Also, NPS is derived from various land uses. The Ministry of Environment (MOE) is studying and monitoring the pollutant loads from each land use since 2007 to determine the unit pollutant loads. This research was a part of long-term monitoring program conducted to characterize the washoff and provide the mean EMC of artificial grassland. The average EMCs result of BOD, COD, DOC, SS, TN, NH4-N, NO3-N, TP, and PO4-P of the artificial grassland were deterined to 8.2, 17.5, 11.3, 110.1, 3.07, 0.20, 0.75, 0.86 and 0.08 mg/L, respectively. The results of statistical analysis conducted showed a low correlation to the contaminants.

• The Journal of Engineering Geology
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• v.22 no.3
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• pp.263-274
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• 2012

We performed numerical modeling to estimate the groundwater level around a riverside area following the construction of an artificial structure. The groundwater level of the alluvial deposit responded more rapidly to the river water level than to the rainfall event itself, indicating that the groundwater and river water are directly interrelated through the riverbed. Furthermore, transient modeling showed raised groundwater levels at the southern part of Mt. Dok and the eastern part of Mt. Dummit in an area of low plains. The artificial structure caused a rise in groundwater level of up to approximately 6 m.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.41-53
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• 2020

The production of near- and off-shore fisheries in South Korea is decreasing due to rapid changes in the fishing environment, particularly including higher sea temperature in recent years. To improve the competitiveness of the fisheries, it is necessary to provide fish catch information that changes spatiotemporally according to the sea state. In this study, artificial intelligence models that predict the CPUE (catch per unit effort) of mackerel, anchovies, and squid (Todarodes pacificus), which are three major fish species in the near- and off-shore areas of South Korea, on a 15-km grid and daily basis were developed. The models were trained and validated using the sea surface temperature, rainfall, relative humidity, pressure,sea surface wind velocity, significant wave height, and salinity as input data, and the fish catch statistics of Suhyup (National Federation of Fisheries Cooperatives) as observed data. The 10-fold blind test results showed that the developed artificial intelligence models exhibited accuracy with a corresponding correlation coefficient of 0.86. It is expected that the fish catch models can be actually operated with high accuracy under various sea conditions if high-quality large-volume data are available.

• Journal of the Korean GEO-environmental Society
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• v.13 no.8
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• pp.65-73
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• 2012

Soil-rock interface, mainly founded in Granite region of Korea, is known as one of the important factor of the slope failure at the rainfall due to smaller shear strength than soil itself. However, research of the effect on slope stability by soil-rock interfaces is insufficient. Therefore, a series of direct shear tests were performed in order to investigate the effect of soil-rock interface on slope stability. The method of tests is to get sand itself and sand-artificial rock interface shear strength from different grain size of sands and artificial rock samples. The results of tests show that the friction angle of interface depends primarily on particle size and surface roughness. Interface friction angle ratio ${\mu}(={\delta}/{\Phi})$ is in the range of 0.75 ~ 0.96, this results indicate that interface friction angle is smaller than sand itself.

• Korean Journal of Environmental Agriculture
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• v.40 no.4
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• pp.270-278
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• 2021

BACKGROUND: Horticultural plant growth under field and/or greenhouse conditions is affected by the climate changes (e.g., temperature, humidity, and rainfall). Therefore investigation of hydroponics on field horticultural crops is necessary for year-round production of the plants regardless of external environment changes under plant factory system with artificial light sources. METHODS AND RESULTS: Common sage (Salvia plebeia), nasturtium (Tropaeolum majus), and hooker chive (Allium hookeri) plants were hydroponically culturing in the plant factory with blue-red-white LEDs (Light-Emitting Diodes) and fluorescent lights (FLs). Leaf numbers of common sage under mixture LED and FL treatments were 134% and 98% greater, respectively than those in the greenhouse condition. In hooker chives, unfolded leaf numbers were 35% greater under the artificial lights and leaf elongation was inhibited by the conventional sunlight compared to the artificial light treatments. Absorption pattern of NO₃-N composition in hydroponic solution was not affected by the different light qualities. CONCLUSION(S): Plant factory system with different light qualities could be applied for fresh-leaf production of common sage, nasturtium, and hooker chive plants culturing under field and/or greenhouse. Controlled light qualities in the system resulted in significantly higher hydroponic growth of the plants comparing to conventional greenhouse condition in present.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.198-203
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• 2020

In this paper, we propose an artificial water level prediction system for small river flood prediction. River level prediction can be a measure to reduce flood damage. However, it is difficult to build a flood model in river because of the inherent nature of the river or rainfall that affects river flooding. In general, the downstream water level is affected by the water level at adjacent upstream. Therefore, in this study, we constructed an artificial intelligence model using Recurrent Neural Network(LSTM) that predicts the water level of downstream with the water level of two upstream points. The proposed artificial intelligence system designed a water level meter and built a server using Nodejs. The proposed neural network hardware system can predict the water level every 6 hours in the real river.

• Journal of Bio-Environment Control
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• v.11 no.1
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• pp.1-4
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• 2002

The occurrence rate of russet in 'Whangkeumbae' pear showed significant difference by years because the russet occurrence is greatly affected by the amount of rainfall. This study was conducted to analyse the relationship between rainfall and russet occurrence by artificial treatment of high humidity. Under high relative humidity condition, stomatal resistance decreased and average fruit weight was higher since the increased net photosynthesis rate accumulation accelerated fruit growth. The russet occurrence began on July 25, when the growth speed of fruit weight and fruit surface is the most fast. Russet occurrence rate was higher in high relative humidity condition because the fruit growth was accelerated. Since the fruit calcium concentration change is extreme in late July, it is assumed that the deceased calcium content is related to the occurrence of russet in 'Whangkeumbae' pear, When the high relative humidity condition is maintained after rainfall, the amount of net photosynthesis rate increase and fruit growth is accelerated. Therefore, the unbalance in the amounts of transferred photosynthesis assimilation product, water and mineral elements would be one of the reasons for the russet occurrence in 'Whangkeumbae' pear.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.383-393
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• 2018

The regional frequency analysis is the method which uses not only sample of target station but also sample of neighborhood stations in which are classified as hydrological homogeneous regions. Consequently, identification of homogeneous regions is a very important process in regional frequency analysis. In this study, homogeneous regions for regional frequency analysis of precipitation were identified by the self-organizing map (SOM) which is one of the artificial neural network. Geographical information and hourly rainfall data set were used in order to perform the SOM. Quantization error and topographic error were computed for identifying the optimal SOM map. As a result, the SOM model organized by $7{\times}6$ array with 42 nodes was selected and the selected stations were classified into 6 clusters for rainfall regional frequency analysis. According to results of the heterogeneity measure, all 6 clusters were identified as homogeneous regions and showed more homogeneous regions compared with the result of previous study.

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.631-641
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• 2012

In this study, satellite data (MTSAT-1R), a numerical weather prediction model, RDAPS (Regional Data Assimilation and Prediction System) output, ground weather station data, and artificial neural networks were used to improve the accuracy of summer rainfall forecasts. The developed model was applied to the Seoul station to forecast the rainfall at 3, 6, 9, and 12-hour lead times. Also to reflect the different weather conditions during the summer season which is related to the frontal precipitation and the cyclonic precipitation such as Jangma and Typhoon, the neural network models were formed for two different periods of June-July and August-September respectively. The rainfall forecast model was trained during the summer season of 2006 and 2008 and was verified for that of 2009 based on the data availability. The results demonstrated that the model allows us to get the improved rainfall forecasts until lead time of 6 hour, but there is still a large room to improve the rainfall forecast skill.