• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Journal of Korean Society of Forest Science
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• v.86 no.3
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• pp.334-341
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• 1997

Artificial precipitation test with sprinkler system was carried out to develop the collection method of arboreal larvae(proctotrupoid wasps) of pine needle gall midge for biological control in 1995. Effects of larvae falling on each amount of precipitation, season of precipitation and time of precipitation of a day following artificial precipitation were examined during the period of larvae falling season. The results obtained were summarized as follows; 1. Artificial precipitation with sprinkler system was highly effective for collection of pine needle gall midge larvae and the most suitable amount of precipitation was 5.3-9.4mm; application amount and hours of water were $8,000-16.000{\ell}$ and 180-360 minutes, respectively. 2. The most effective period of larvae collection for artificial precipitation was approximately 20 days, from early through mid November, and larvae falling was 93.4% of the total number of larvae collection during this period. 3. Larvae falling from the tree crown was not affected by the artificial precipitation for the precipitation hour intervals in a day. 4. The percentage of parasitism of collected larvae of pine needle gall midge in November exceeded that of December but was not significantly different between two seasons. 5. Artificial precipitation of sprinkler system was effective in reducing 34% of gall formation after one year at collected sites of pine needle gall midge larvae. 6. The collection method of larvae following artificial precipitation was effective in reducing the expenses by 14-50% than that of collection method of infested needles.

• Journal of Soil and Groundwater Environment
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• v.16 no.6
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• pp.66-78
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• 2011

It is getting difficult to manage water resources in South Korea because more than half of annual precipitation is concentrated in the summer season and its intensity is increasing due to global warming and climate change. Artificial recharge schemes such as well recharge of surface water and roof-top rainwater harvesting can be a useful method to manage water resources in Korea. In this study, potential artificial recharge site is evaluated using geographic information system with hydrogeological and social factors. The hydrogeological factors include annual precipitation, geological classification based on geological map, specific capacity and depth to water level of national groundwater monitoring wells. These factors were selected to evaluate potential artificial recharge site because annual precipitation is closely related to source water availability for artificial recharge, geological features and specific capacity are related to injection capacity and depth to water is related to storage capacity of the subsurface medium. In addition to those hydrogeological factors, social aspect was taken into consideration by selecting the areas that is not serviced by national water works and have been suffered from drought. These factors are graded into five rates and integrated together in the GIS system resulting in spatial distribution of artificial recharge potential. Cheongsong, Yeongdeok in Gyeongsangbuk-do and Hadong in Gyeongsangnam-do, and Suncheon in Jeollanam-do were proven as favorable areas for applying artificial recharge schemes. Although the potential map for artificial recharge in South Korea developed in this study need to be improved by using other scientific factors such as evaporation and topographical features, and other social factors such as water-curtain cultivation area, hot spring resorts and industrial area where groundwater level is severely lowered, it can be used in a rough site-selection, preliminary and/or feasibility study for artificial recharge.

• Geomechanics and Engineering
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• v.17 no.5
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• pp.421-427
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• 2019

Coastal erosion is becoming a significant problem in Greece, Bangladesh, and globally. For the prevention and minimization of damage from coastal erosion, combinations of various structures have been used conventionally. However, most of these methods are expensive. Therefore, creating artificial beachrock using local ureolytic bacteria and the MICP (Microbially Induced Carbonate Precipitation) method can be an alternative for coastal erosion protection, as it is a sustainable and eco-friendly biological ground improvement technique. Most research on MICP has been confined to land ureolytic bacteria and limited attention has been paid to coastal ureolytic bacteria for the measurement of urease activity. Subsequently, their various environmental effects have not been investigated. Therefore, for the successful application of MICP to coastal erosion protection, the type of bacteria, bacterial cell concentration, reaction temperature, cell culture duration, carbonate precipitation trend, pH of the media that controls the activity of the urease enzyme, etc., are evaluated. In this study, the effects of temperature, pH, and culture duration, as well as the trend in carbonate precipitation of coastal ureolytic bacteria isolated from two coastal regions in Greece and Bangladesh, were evaluated. The results showed that urease activity of coastal ureolytic bacteria species relies on some environmental parameters that are very important for successful sand solidification. In future, we aim to apply these findings towards the creation of artificial beachrock in combination with a geotextile tube for coastal erosion protection in Mediterranean countries, Bangladesh, and globally, for bio-mediated soil improvement.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.51-56
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• 2006

Biases embedded in numerical weather precipitation forecasts by the RDAPS model was determined, quantified and corrected. The ultimate objective is to eventually enhance the reliability of reservoir operation by Korean Water Resources Corporation (KOWACO), which is based on precipitation-driven forecasts of stream flow. Statistical post-processing, so called MOS (Model Output Statistics) was applied to RDAPS to improve their performance. The Artificial Neural Nwetwork (ANN) model was applied for 4 cases of 'Probability of Precipitation (PoP) for wet and dry season' and 'Quantitative Precipitation Forecasts (QPF) for wet and dry season'. The reduction on the large systematic bias was especially remarkable. The performance of both networks may be improved by retraining, probably every month. In addition, it is expected that performance of the networks will improve once atmospheric profile data are incorporated in the analysis. The key to the optimal performance of ANN is to have a large data set relevant to the predictand variable. The more complex the process to be modeled by the ANN, the larger the data set needs to be.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.167-175
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• 2023

Precipitation data is one of the essential input datasets used in various fields such as wetland management, hydrological simulation, and water resource management. In order to efficiently manage water resources using precipitation data, it is essential to secure as much data as possible by minimizing the missing rate of data. In addition, more efficient hydrological simulation is possible if precipitation data for ungauged areas are secured. However, missing precipitation data have been estimated mainly by statistical equations. The purpose of this study is to propose a new method to restore missing precipitation data using machine learning algorithms that can predict new data based on correlations between data. Moreover, compared to existing statistical methods, the applicability of machine learning techniques for restoring missing precipitation data is evaluated. Representative machine learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were applied. For the performance of classifying the occurrence of precipitation, the RF algorithm has higher accuracy in classifying the occurrence of precipitation than the ANN algorithm. The F1-score and Accuracy values, which are evaluation indicators of the classification model, were calculated as 0.80 and 0.77, while the ANN was calculated as 0.76 and 0.71. In addition, the performance of estimating precipitation also showed higher accuracy in RF than in ANN algorithm. The RMSE of the RF and ANN algorithms was 2.8 mm/day and 2.9 mm/day, and the values were calculated as 0.68 and 0.73.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.330-330
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• 2022

우리나라에서 발생하는 기상 재해 현상은 주로 태풍, 집중호우, 장마 등 인명 및 경제적인 피해가 크며, 단기간에 국지적으로 나타난다. 현재 재해 감시 및 예보는 주로 종관기상관측체계를 이용하고 있다. 하지만, 우리나라의 복잡한 지형, 인구 밀집 지형, 관측 시기가 일정하지 않은 지형과 같은 조건에서 미계측 자료 및 지역이 다수 존재 때문에 강수의 공간 분포와 강도에 대한 정밀한 정보를 제공하지 못하는 실정이다. 최근 광범위한 관측영역과 공간 분해능의 개선, 자료추출 알고리즘의 개발로 전세계적으로 위성영상 기반 기상관측 자료의 활용성이 증대되고 있다. 본 연구에서는 한반도 지역의 지상 관측데이터와 전지구 격자형 위성 강우자료를 비교하여 한반도의 적용성을 분석하고자 한다. 다양한 위성영상 기반 기상자료인 Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Centre (GPCC), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) 4개의 강우위성영상을 수집하여, 1991년부터 2020년까지 30년 데이터를 활용하였다. 강수량 변동성 비교를 위하여 기상청의 종관기상관측장비 (Automated Synoptic Observation System, ASOS), 자동기상관측시설 (Automatic Weather System, AWS) 데이터와 상관 분석을 수행하고, 강우위성영상의 국내 적합성을 판단하고자 한다.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.329-340
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• 2021

Purpose: In this paper, using artificial neural network algorithm, the Korean Peninsula was analyzed for drought vulnerable areas by predicting weather data changes. Method: Monthly cumulative precipitation data were utilized for research areas considering the specific nature areas, and weather data prediction through artificial neural network algorithm was carried out using statistical program R. The predicted data were applied to the Standardized Precipitation Index (SPI) to analyze drought vulnerable areas in the Korean Peninsula. Result: In this paper, the correlation coefficient values between real and predicted data are found to be 0.043879 higher on average than the regression results, using artificial neural network algorithms. Conclusion: The results of the research are expected to be used as basic research materials for responding to drought.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.465-474
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• 2010

Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and $12-{\mu}m$ channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.