• Journal of Korean Society for Atmospheric Environment
• /
• v.34 no.5
• /
• pp.735-747
• /
• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• Spatial Information Research
• /
• v.15 no.2
• /
• pp.159-167
• /
• 2007

In this study, we investigated the application of extending the Huff's method to design discharge being used at present up to the event of concentrated rainfall. As our field study site, we selected Odae Cheon basin in Pheongchang, which was affected by concentrated rainfall in July 2006. Actual concentrated rainfall and design rainfall derived from the Huff's method were used to calculate the discharge and storm water levels, which were compared with the directly measured water-level marks of storm discharges. The results showed that the peak storm discharge from the torrential rainfall was twice higher than the design rainfall. The short term discharges from concentrated rainfall closely corresponded to the rainfall discharges of 150 years storm frequency.

• Environmental Engineering Research
• /
• v.22 no.2
• /
• pp.162-168
• /
• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.101-104
• /
• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• Journal of Environmental Science International
• /
• v.28 no.1
• /
• pp.125-135
• /
• 2019

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.166-166
• /
• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• 한국방재학회:학술대회논문집
• /
• 2007.02a
• /
• pp.575-578
• /
• 2007

The objective of this study is to analyze the regionalization of point rainfall by statistical methods for regional frequency analysis of the rainfall. The rainfall data used in this study are annual maximum rainfall at 57 stations during the period of more than 30 years for 12 durations(10min, 1, 2, 3, 4, 5, 6, 8, 10, 12, 18, 24hr) in Korea. The Mann-Whitney U test, Kruskal-Wallis one-way analysis of variance of nonparametric test the principal component and the cluster analysis have been performed to analyze the regionalization of rainfall. The results of this study are as follows; (1) The region which hydrological homogeneous is accepted does not exist for whole duration in Korea. (2) The result of nonpametric test shows that hydrological homogeneous regions of point rainfall are divided by 5 regions. (3) In case of cluster analysis hydrological homogeneous regions of point rainfall are divided by 6 regions and 4 other areas.

• Journal of Wetlands Research
• /
• v.2 no.1
• /
• pp.49-58
• /
• 2000

The frequency analysis of annual maximum rainfall data and the derivation of probable rainfall intensity formula at Masan station are performed in this study. Based on the eight different rainfall duration data from 10 minutes to 24 hours, eight types of probability distribution (Gamma, Lognormal, Log-Pearson type III, GEV, Gumbel, Log-Gumbel, Weibull, and Wakeby distributions), three types of parameter estimation scheme (moment, maximum likelihood and probability weighted methods) and three types of goodness-of-fit test (${\chi}^2$, Kolmogorov-Smirnov and Cramer von Mises tests) were considered to find an appropriate probability distribution at Masan station. The Lognormal-2 distribution was selected and the probable rainfall intensity formula was derived by regression analysis. The derived formula can be used for estimating rainfall quantiles of the Masan vicinity areas with convenience and reliability in practice.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.132-134
• /
• 1999

Rainfall characteristics for designing the optimum millimeter-wave communication systems from two rainfall data set was analyzed. Two rainfall data sets were compared; one-minute rainfall rate data, one-hour synoptic observation data. Each data set has different observation method, sampling frequency. We looked for tendency and quality confluence between two data sets. We showed several results using one-minute rainfall data by millimeter-wave attenuation model. A climatological one-minute rainfall rate data set over Korean Peninsula will be made after data quality control procedure

• Journal of The Korean Society of Agricultural Engineers
• /
• v.53 no.3
• /
• pp.53-58
• /
• 2011

This study is analyzed the characteristics of the soil erosion with the geotechnical conditions and rainfall conditions, such as the ground slope, the compaction ratio, rainfall intensity and duration of rainfall etc. To this ends, a series of model test are conducted on clayey sands. From the results, the variation of soil loss is analyzed with the geotechnical and the rainfall conditions. The amount of soil loss is decreased as the increase of compaction ratio and is increased as the ground slope, rainfall intensity and the duration of rainfall.