• Journal of Digital Convergence
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• v.12 no.6
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• pp.155-160
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• 2014

When we collect marketing data, we can only obtain the bimonthly or quarterly data but the monthly data be available. If we evaluate or predict monthly market condition or establish monthly marketing strategies, we need to disaggregate these bimonthly or quarterly data to the monthly data. In this paper, for bimonthly or quarterly data, we introduce some methods of disaggregation to monthly data. These disaggregation methods include the simple average method, the growth rate method, the weighting method by the judgment of experts, and variable decomposition method using 12 month moving cumulative sum. In this paper, we applied variable decomposition method to disaggregate for bimonthly data of sum of electronics sales in a European country. We, also, introduce how to use this method to predict the future data.

• Journal of Korea Water Resources Association
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• v.43 no.5
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• pp.471-482
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• 2010

Rainfall events in the hydrologic circulation are closely related with various meteorological factors. Therefore, in this research, correlation relationship was analyzed between sea surface temperature of typical meteorological factor and monthly rainfall on Korean peninsula. The cluster analysis was performed monthly average rainfall data, longitude and latitude observed by rainfall observatory in Korea. Results from cluster analysis using monthly rainfall data in South Korea were divided into 4 regions. The principal components of monthly rainfall data were extracted from rainfall stations separated cluster regions. A correlation analysis was performed with extracted principal components and sea surface temperatures. At the results of correlation analysis, positive correlation coefficients were larger than negative correlation coefficients. In addition, The 3 month of principal components on monthly rainfall predicted by locally weighted polynomial regression using observed data of sea surface temperature where biggest correlation coefficients have. The result of forecasting through the locally weighted polynomial regression was revealed differences in accuracy. But, this methods in the research can be analyzed for forecasting about monthly rainfall data. Therefore, continuous research need through hydrological meteorological factors like a sea surface temperature about forecasting of the rainfall events.

• Water for future
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• v.22 no.3
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• pp.331-339
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• 1989

The monthly flows with periodicity and trend were forecasted by multiplicative ARIMA model and then the applicability of the model was tested based on 23 years of the historical monthly flow data at Jindong river stage gauging station in the Nakdong River Basin. The parameter estimation was made with 21 years of data and the remaining two years of monthly data were used to compare the forecasted flows by ARIMA (2,0,0)$\times$$(0,1,1)_{12}$ with the observed. The results of forecast showed a good agreement with the observed, implying the applicability of multiplicative ARIMA model for forecasting monthly river flows at the Jindong site.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.4
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• pp.4232-4241
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• 1976

This study was carried out to clarify the stochastic characteristics of monthly rainfalls and to select a proper model for generating the sequential monthly rainfall amounts. The results abtained are as follows: 1. Log-Normal distribution function is the best fit theoretical distribution function to the empirical distribution of monthly rainfall amounts. 2. Seasonal and random components are found to exist in the time series of monthly rainfall amounts and non-stationarity is shown from the correlograms. 3. The Monte Carlo model shows a tendency to underestimate the mean values and standard deviations of monthly rainfall amounts. 4. The 1st order Markov model reproduces means, standard deviations, and coefficient of skewness with an error of ten percent or less. 5. A correlogram derived from the data generated by 1st order Markov model shows the charaterstics of historical data exactly. 6. It is concluded that the 1st order Markov model is superior to the Monte Carlo model in their reproducing ability of stochastic properties of monthly rainfall amounts.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.112-115
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Magazine of the Korean Society of Agricultural Engineers
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• v.24 no.4
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• pp.99-108
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• 1982

This thesis aims to estimate the rainfall runoff from paddy field in a small watershed during irrigation period. When the data observed at the proposed site are not available, the Monthly Runoff Equation of Korean Rivers which was derived from data observed under the following assumptions is used to study the water balance. a. Monthly base flow was assumed as 10. 2mm even if these is no mouthly rainmfall. b. Monthly comsumption of rainfall was ranged from 100 to 2OOmm without relation to the rainfall depth. However, the small watershed which consists mainly of paddy fields encounters severe droughts and accordingly the baseflow is negligible. Under the circumstances the author has developed the following equation called "Flood Irrigation Method for Rainfall Runoff "taking account of the evapotranspiration, precipitation, seepage, less of transportation, etc. R= __ A 7000(1 +F) -5n(n+1)+ (n+1)(Pr-S-Et)] where: R: runoff (ha-m) A: catchment area (ha) F: coefficient of loss (o.o-0. 20) Pr: rainfall (mm) S: seepage Er: evapotranspiration (mm) To verify the above equation, the annual runoff ratio for 28 years was estimated using the Monthly Runoff Equation of Korean Rivers the Flood Irrigation Method and the Complex Hydrograph Method based on meteorological data observed in the Dae Eyeog project area, and comparison was made with data observed in the Han River basin. Consequently, the auther has concluded that the Flood Irrigation Method is more consi- stent with the Complex Hydrograph Method and data observed than the Monthly Runoff Equation of Korean Rivers.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• Journal of Environmental Science International
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• v.7 no.4
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• pp.433-440
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• 1998

This study was carried out to develop the stream water quality model for the intaking station of Kongju waterworks in the Keum River system. The monthly water quality(total nitrogen and total phosphorus) with periodicity and trend were forecasted by multiplicative ARIU models and then the applicability of the models was tested based on 7 years of the historical monthly water quality data at Kongju intaking strate. The parameter estimation was made with the monthly observed data. The last one year data was used to compare the forecasted water Quality by ARU model with the observed one. The models are ARIMA(2,0,0)$\times$(0,1,1)l2 for total nitrogen, ARIMA(0,1,1)x(0,1,1)l2 for total phosphorus. The forecasting results showed a good agreement with the observed data. It is implying the applicability of multiplicative ARIMA model for forecasting monthly water quality at the Kongju site.

• Water for future
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• v.17 no.4
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• pp.281-291
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• 1984

In our Koreans river basins there are many of monthly rainfall data, but unfortrnately streamflow data needed are rare. Analysing monthly rainfall data of Somjin river basin, the stochastic theory model for calculation of monthly streamflow series of that region is determined. The model is composed of Box & Jenkins stansfer function plus ARIMA residual models. This linear stochastic differenced time series equation models can adapt themselves to the structure and variety of rainfall, streamflow data on the assumption of the stationary covarience. The fiexibility of Box-Jenkins method consists mainly in the iterative technique of building an AIRMA model from observations and by the use of autocorrelation functions. The best models for Somjin river basin belong to the general calss: $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ monthly streamflow, $X_t$ : monthly rainfall, $C_i$ :monthly run-off, $$\omega$o-$\omega$_1$ : transfer parameter, $$\varepsilon$_t$ : residual The streamflow series resulted from the proposed model is satisfactory comparing with the exsting streamflow data of Somjin gauging station site.

• Journal of Environmental Science International
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• v.28 no.6
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• pp.571-579
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• 2019

In this study, monthly average values of BOD, COD, and TOC observed for 10 years (2008-2017) in the Nam River were estimated, and monthly variations of BOD, COD, and TOC were analyzed. Monthly average COD was always higher than monthly average BOD; monthly average TOC was high from June to September when rainfall was high. Monthly correlation coefficients between BOD and COD ranged from 0.57 to 0.94, those between BOD and TOC from 0.45 to 0.93, and those between COD and TOC from 0.75 to 0.93. The correlation coefficients were high from November to February when rainfall was low. Regression analyses for monthly average water quality data of the Nam River classified into dry season (October to April) and wet season (May to September) were conducted. Correlation coefficients were higher in the dry season than those in the wet season, and the determination coefficients of linear regression functions for BOD and COD with TOC were also higher in the dry season than those in the wet season. From this study, it can be concluded that it is appropriate to use monthly data to analyze the correlations among BOD, COD, and TOC in the stream. To analyze the relationship between TOC flowing into the stream and BOD/COD, it was found that seasonal characteristics should be considered.