• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.201-210
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• 2002

The object of this study is to evaluate the factors affecting the water quality and to propose the influence of dominant factor quantitatively. The correlation analysis was performed to know the correlationship among the water quality items As a result of partial correlation analysis, it was shown that the water quality items are affected by the rainfall item directly. The factor analysis was performed to grasp some number of factors on each point for deducing the items of similar variable characteristics. The four points were divided into different factor groups. It was grasped that $NH_3-N$ and $NO_3-N$ Items have different variable characteristics after comparing the items. The Multiple regression analysis can decrease the number of observation. In the deduced multiple regression formula, it was shown that the rate of T-N, $NH_3-N$ and $NO_3-N$ in the independent variable took about 60% among all the regression formulas.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.3-6
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• 2005

In this paper, the real scenario of water situation (e.g. water management, water availability and flooding) in an irrigated rice cultivation area in Suphanburi Province, Central-West Thailand is discussed together with the NDVI time series data. The result shown is derived by our classifier named 'Peak Detector Algorithm (PDA)'. The method discriminated 5 classes in terms of irrigation activities and cropping intensities, namely, Non-irrigated, Poorly irrigated - 1 crop/year, Irrigated - 2 crops/year, Irrigated - 3 crops/year and Others (no cultivation happens in a year or other land covers). The overall accuracy of all classified results (1999-2001) is around $77\%$ against independent ground truth data (general activities or function of an area). In the classified results, spatial and temporal inconsistency appeared significantly in the Western and Southern areas of Suphanburi. The inconsistency resulted mainly by anomaly of rainfall pattern in 1999 and their temporal irrigation activity. The algorithm however, was proved that it could detect actual change of irrigation status in a year.

• Advances in environmental research
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• v.7 no.2
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• pp.109-120
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• 2018

In the present study research air quality analyses for $PM_{10}$, were conducted in Shiraz, a city in the south of Iran. The measurements were taken from 2011 through 2012 in two different locations to prepare average data in the city. The averages concentrations were calculated for every 24 hours, each month and each season. Results showed that the highest concentration of $PM_{10}$ occurs generally in the night while the least concentration was found at the afternoon. Monthly concentrations of $PM_{10}$ showed highest value in August, while least value was found in January. The seasonal concentrations showed the least amounts in autumn while the highest amounts in summer. Relations between the air pollutant and some meteorological parameters were calculated statistically using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation, dew point and rainfall were considered as independent variables. The relationships between concentration of pollutant and meteorological parameters were expressed by multiple linear regression equations for both annual and seasonal conditions SPSS software. RMSE test showed that among different prediction models, stepwise model is the best option.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.931-931
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• 2012

제주지역의 강수자료는 최근에 이르러 69개 지점으로 증가하여 비교적 밀도있는 강수관측이 진행되고 있다. 그러나 기존의 자료 증설 내역과 이설 등으로 인해 과거로부터 현재에 이르는 자료를 기반으로 면적강수량을 산정할 경우 다소 어려움이 있다. 본 연구에서는 1992년부터 2010년까지의 강수자료를 바탕으로 관측소 개수를 기반으로 기간을 구분하여 각 기간별로 공간보간기법별로 면적강수량을 산정하고 이를 비교하였다. 사용한 공간보간기법은 PRISM(Parameter-elevation Regressions on Independent Slopes Model)기법과 티센(Thiessen)법으로 19년간의 일강수량 자료를 바탕으로 각각 면적강수량을 산정했다. PRISM기법을 이용한 경우는 고도, 관측점으로부터의 거리, 방향성 분석 및 해안가중치를 고려하여 계산하였고, 티센법의 경우는 기간별로 상이한 티센망을 구축하여 산정하였다. 지점 관측강수량에서 고도가 증가할수록 강수량이 증가하는 제주형 산악효과가 나타났으며 이는 보간기법에 의한 결과에서도 동일하게 나타나는 것으로 확인되었다. 또한 고도에 따른 상관성은 PRISM기법에 의한 결과에서 더 높게 산정되는 것으로 나타났다. 기법별 산정된 면적강수량은 근소한 차이를 보였으며 PRISM기법에 의한 값이 티센법에 비해 약 1%정도 크게 계산되었다.

• Korean Journal of Ecology and Environment
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• v.39 no.1 s.115
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• pp.13-20
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• 2006

The changing patterns of water temperature and turbidity in streams entering Imha Reservoir were studied. The turbidity variation near the intake tower in Imha Reservoir was investigated in relation with the variation of water temperature and turbidity in streams. Water temperature was estimated using multi-regression method with air temperature and dew point as independent variables. Peak turbidity was also estimated using non-linear regression method with rainfall intensity as an independent variable. Although more independent variables representing watershed characteristics seem to be needed to increase estimation accuracies, the methodology used in this study can be applied to estimate water temperature and peak turbidity in other streams.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.193-200
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• 2020

Hydrological drought is directly associated with lack of available water in rivers, reservoirs, and groundwater. It is important to analyze hydrological drought for efficient water resource management because most of rainfall is concentrated in wet seasons and water supply is highly dependent on dams and reservoirs in South Korea. Generally, a threshold level method is useful for defining hydrological droughts. However, this method causes interdependent problems between drought events which result in skewed results in further statistical analysis. Therefore, it is necessary to determine a proper threshold level to represent regional drought characteristics. In this study, applying 50~99 percentiles of daily flow-duration curve, hydrological drought events were extracted, and independence tests were conducted for 12 watersheds. The Poisson independence test showed that 87~99 percentiles were available for most stations except for Yeoju and Pyeongtaek. The generalized Pareto independence test showed that 80~90 percentiles were the most common. Mean excess plot showed that 80 ~ 90 percentiles were the most common. Therefore, the common ranges of the three independent tests were determined for each station and proper threshold levels were recommended for large river basins; 70~76 percentiles for the Han River basin, 87~91 percentiles for the Nakdong River basin, 86~98 percentiles for the Geum River basin, and 85~87 percentiles for the Youngsan and Seomjin River basin.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.2
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• pp.72-78
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• 2009

The demand for rainfall data in gridded digital formats has increased in recent years due to the close linkage between hydrological models and decision support systems using the geographic information system. One of the most widely used tools for digital rainfall mapping is the PRISM (parameter-elevation regressions on independent slopes model) which uses point data (rain gauge stations), a digital elevation model (DEM), and other spatial datasets to generate repeatable estimates of monthly and annual precipitation. In the PRISM, rain gauge stations are assigned with weights that account for other climatically important factors besides elevation, and aspects and the topographic exposure are simulated by dividing the terrain into topographic facets. The size of facet or grid cell resolution is determined by the density of rain gauge stations and a $5{\times}5km$ grid cell is considered as the lowest limit under the situation in Korea. The PRISM algorithms using a 270m DEM for South Korea were implemented in a script language environment (Python) and relevant weights for each 270m grid cell were derived from the monthly data from 432 official rain gauge stations. Weighted monthly precipitation data from at least 5 nearby stations for each grid cell were regressed to the elevation and the selected linear regression equations with the 270m DEM were used to generate a digital precipitation map of South Korea at 270m resolution. Among 1.25 million grid cells, precipitation estimates at 166 cells, where the measurements were made by the Korea Water Corporation rain gauge network, were extracted and the monthly estimation errors were evaluated. An average of 10% reduction in the root mean square error (RMSE) was found for any months with more than 100mm monthly precipitation compared to the RMSE associated with the original 5km PRISM estimates. This modified PRISM may be used for rainfall mapping in rainy season (May to September) at much higher spatial resolution than the original PRISM without losing the data accuracy.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.519-526
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• 2000

This study is to investigate the recurrence possibility of consecutive dry years such as the long dry period around 1900 in the annual rainfall data at Seoul station. The truncation levels, as the criterion for the dry years, are decided such as to make the occurrence of dry years follow the Poissonian distribution, which assures independent occurrence of dry years. For the truncation level of mean-0.5stdv, the occurrence of dry years is found to satisfy the Poissonian distribution weakly with 99% significance level, but for those of mean-0.75stdv and mean-stdv with 95% significance level. For these truncation levels, the long dry period around 1900 is divided into several short consecutive dry years. The Poisson process has then been applied to derive the occurrence probability of consecutive dry years. For the truncation level of mean-0.75stdv or below, the Poisson process was found to reproduce similar occurrence probabilities to the observed. Especially for the lowest truncation level used in the study (mean-stdv), we could see that the occurrence probability of consecutive dry years estimated for the data collected before the long dry period around 1900 was higher that those for the data collected after the long dry period, thus, it could be concluded that the possibility of long dry periods is decreasing recently.cently.