• The Plant Pathology Journal
• /
• v.38 no.4
• /
• pp.395-402
• /
• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Proceedings of the National Institute of Ecology of the Republic of Korea
• /
• v.1 no.1
• /
• pp.74-82
• /
• 2020

Predictions of suitable habitat areas can provide important information pertaining to the risk assessment and management of alien plants at early stage of their establishment. Here, we predict the invasion potential of Muhlenbergia capillaris (pink muhly) in South Korea using five bioclimatic variables. We adopt four models (generalized linear model, generalized additive model, random forest (RF), and artificial neural network) for projection based on 630 presence and 600 pseudo-absence data points. The RF model yielded the highest performance. The presence probability of M. capillaris was highest within an annual temperature range of 12 to 24℃ and with precipitation from 800 to 1,300 mm. The occurrence of M. capillaris was positively associated with the precipitation of the driest quarter. The projection map showed that suitable areas for M. capillaris are mainly concentrated in the southern coastal regions of South Korea, where temperatures and precipitation are higher than in other regions, especially in the winter season. We can conclude that M. capillaris is not considered to be invasive based on a habitat suitability map. However, there is a possibility that rising temperatures and increasing precipitation levels in winter can accelerate the expansion of this plant on the Korean Peninsula.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.123-123
• /
• 2019

최근 기후변화로 인해 중국, 한국, 일본, 몽골 등을 포함한 동아시아 지역은 태풍, 가뭄, 홍수와 같은 자연재해의 발생 빈도가 증가하고 있는 추세이다. 중국의 경우 2017년 극심한 가뭄으로 1,850만 (ha)의 농작물 피해가 발생하였으며, 몽골 또한 2017년 4월 이후 극심한 가뭄으로 사막화가 급속도로 진행되고 있다. 위성 기반의 강우 자료는 공간과 시간 해상도가 높아짐에 따라 지상관측소 강수량 자료의 대체 수단으로 이용되고 있다. 본 연구에서는 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) 강우 위성 자료를 활용하여 기상학적 가뭄지수인 표준강수지수 (Standardized Precipitation Index, SPI)를 산정하였다. 시간 해상도는 월별 영상을 기준으로 2008년부터 2017년까지 지난 10년간의 데이터를 이용하였으며, 각각 격자가 다른 위성영상을 기존 기상관측소와 비교하였다. 피어슨 상관계수 (Pearson Correlation Coefficient, R)를 활용하여 강우 위성 영상과 지상관측소의 상관관계를 분석하고, 평균절대오차 (Mean Absolute Error, MAE), 평균제곱근오차 (Root Mean Square Error, RMSE)를 통해 통계적으로 정확도를 분석하였다. 인공위성 강수량 자료는 미계측 지역이 많은 곳이나 측정이 불가능한 지역에 효율성 측면에서 중요한 이점을 제공할 것으로 판단된다.

• Journal of Korea Water Resources Association
• /
• v.53 no.4
• /
• pp.293-301
• /
• 2020

This study determined appropriate threshold level (cumulative period and percentage) of precipitation for drought management in dam basin. The 5 dam basins were selected, the daily dam storage level and daily precipitation data were collected. MAP (Mean Areal Precipitation was calculated by using Thiessen polygon method, and MAP were converted to accumulated values for 6 cumulative periods (30-, 60-, 90-, 180-, 270-, and 360-day). The correlation coefficient and ratio of variation coefficient between storage level and MAP for 6 cumulative periods were used to determine the appropriate cumulative period. Correlation of cumulative precipitation below 90-day was low, and that of 270-day was high. Correlation was high when the past precipitation during the flood period was included within the cumulative period. The ratio of variation coefficient was higher for the shorter cumulative period and lower for the longer in all dam, and that of 270-day precipitation was closed to 1.0 in every month. ROC (Receiver Operating Characteristics) analysis with TLWSA (Threshold Line of Water Supply Adjustment) was used to determine the percentage of precipitation shortages. It is showed that the percentage of 270-day cumulative precipitation on Boryung dam and other 4-dam were less than 90% and 80% as threshold level respectively, when the storage was below the attention level. The relationship between storage and percentage of dam outflow and precipitation were analyzed to evaluate the impact of artificial dam operations on drought analysis, and the magnitude of dam outflow caused uncertainty in the analysis between precipitation and storage data. It is concluded that threshold level should be considered for dam drought analysis using based on precipitation.

• Journal of Korean Society of Rural Planning
• /
• v.28 no.4
• /
• pp.105-117
• /
• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• Journal of Korea Water Resources Association
• /
• v.46 no.12
• /
• pp.1249-1263
• /
• 2013

In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.

• Journal of Korea Water Resources Association
• /
• v.51 no.1
• /
• pp.71-80
• /
• 2018

For the stream management, time of concentration is one of the important factors. In particular, as the requirement about various application of the stream increased, accuracy assessment of concentration time in the stream as waterfront area is extremely important for securing evacuation at the flood. the past studies for the assessment of concentration time, however, were only performed on the single hydrological event in the complex basin of natural streams. The development of a assessment methods for the concentration time on the complex hydrological event in a single watershed of urban streams is insufficient. Therefore, we estimated the concentration time using the rainfall- runoff data for the past 10 years (2006~2015) for the Oncheon stream, the representative stream of the Busan, where frequent flood were taken place by heavy rains, in addition, reviewed the reliability using artificial neural network method based on Matlab. We classified a total of 254 rainfalls events based on over unrained 12 hours. Based on the classification, we estimated 6 parameters (total precipitation, total runoff, peak precipitation/ total precipitation, lag time, time of concentration) to utilize for the training and validation of artificial neural network model. Consequently, correlation of the parameter, which was utilized for the training and the input parameter for the predict and verification were 0.807 and 0.728, respectively. Based on the results, we predict that it can be utilized to estimate concentration time and analyze reliability of urban stream.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.2B
• /
• pp.181-191
• /
• 2009

This study suggests the assessment technique for climate change effect on drought in Korea based on the AR4 SRES A2 scenario reported in IPCC fourth assessment report in 2007. IPCC provides monthly outputs of 24 climate models through the DDC. One of the models is BCM2 model which was developed at BCCR in Norway and NCEP data is used for downscaling. The K-NN(K-Nearest Neighbor) and ANN(Artificial Neural Network) are selected as downscaling technique to downscale the temperature and precipitation at Seoul station in Korea. K-NN could downscale both temperature and precipitation well. ANN made a good result for temperature, but it gave a divergence result in precipitation. Finally, SPI of Seoul station is computed to evaluate the effect of climate change on drought. BCM2 predicted that temperature will increase and drought severity will increase because of the increased drought spell at Seoul station.

• Journal of Korean Society of Forest Science
• /
• v.79 no.4
• /
• pp.376-387
• /
• 1990

A research effort has been made to determine soil buffer capacity in forest soils nearby urban and industrialized regions. Buffer capacities of soils from four regions were measured by different pH levels of artificial acid precipitation. The following conclusions have been drawn in response to the overall research objectives. Soil Suffer capacity was the highest in Kangwondo followed by Uisan, Yeochon and Seoul when simulated acid precipitation were treated at the level of pH 3.0-5.7. With the acid precipitation treatment below pH 2.0 level, however, the capacity dropped seriously with no significant differences between the regions. In Kangwondo region soils weathered from granite and limestone showed significant differences in the buffer capacities. Soil collected in Seoul and Ulsean revealed that the capacities tended to increase with the distance from the pollution sources when treated at pH 3.0, 4.5 and 5.7 level of acid precipitation. The major mechanism of soil buffer observed during simulated acid precipitation experiment was canon exchange for Kangwondo forest soils. In Seoul region canon exchange also played an important role in soil buffering under artificial acid precipitation between 3.0 and 5.7 pH levels, yet under pH 2.0 level aluminum and silicate hydrolysis. In Ulsan canon exchange was a msjor determinant for the buffer capacity above pH 4.5 level, between pH 3.0-4.5 aluminum hydrolysis and below pH 3.0 aluminum and silicate hydrolysis. In Yeochon silicate hydrolysis led buffer capacity above pH 4.5 and below pH 4.5 aluminum hydrolysis.

• Journal of Korea Water Resources Association
• /
• v.55 no.6
• /
• pp.447-459
• /
• 2022

This study estimated the grid-type precipitation quantile for the Korean Peninsula using PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record), a satellite based re-analysis precipitation data. The period considered is a total of 38 years from 1983 to 2020. The spatial resolution of the data is 0.04° and the temporal resolution is 3 hours. For the probability distribution, the Gumbel distribution which is generally used for frequency analysis was used, and the probability weighted moment method was applied to estimate parameters. The duration ranged from 3 hours to 144 hours, and the return period from 2 years to 500 years was considered. The results were compared and reviewed with the estimated precipitation quantile using precipitation data from the Automated Synoptic Observing System (ASOS) weather station. As a result, the parameter estimates of the Gumbel distribution from the PERSIANN-CCS-CDR showed a similar pattern to the results of the ASOS as the duration increased, and the estimates of precipitation quantiles showed a rather large difference when the duration was short. However, when the duration was 18 h or longer, the difference decreased to less than about 20%. In addition, the difference between results of the South and North Korea was examined, it was confirmed that the location parameters among parameters of the Gumbel distribution was markedly different. As the duration increased, the precipitation quantile in North Korea was relatively smaller than those in South Korea, and it was 84% of that of South Korea for a duration of 3 h, and 70-75% of that of South Korea for a duration of 144 h.