scholarly journals Topographical and subsurface geological characteristics of surface failures induced by heavy rainfall of the Typhoon Meari (No. 0421) at Miyagawa Village, Mie Prefecture, Japan

2009 ◽  
Vol 46 (3) ◽  
pp. 181-187 ◽  
Author(s):  
Hidehisa NAGATA ◽  
Tatsuo IINUMA ◽  
Yoichiro HAGIWARA
Keyword(s):  
Heavy Rainfall ◽  
Geological Characteristics ◽  
Typhoon Meari

The relationship between the boundary layer moisture transport from the South China Sea and heavy rainfall over Taiwan

2020 ◽  
Vol 31 (2) ◽  
pp. 159-176 ◽  
Author(s):  
Chuan-Chi Tu ◽  
Yi-Leng Chen ◽  
Pay-Liam Lin ◽  
Po-Hsiung Lin
Keyword(s):  
Boundary Layer ◽  
South China Sea ◽  
South China ◽  
Moisture Transport ◽  
Heavy Rainfall ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
The Relationship

SIMULATION OF HEAVY RAINFALL EVENT DURING 30/10 - 01/11/2008 OVER HANOI BY WRF MODEL

2019 ◽  
Author(s):  
Nguyen Van Hiep ◽  
Nguyen Xuan Anh ◽  
Nguyen Duc Nam ◽  
Dang Hong Nhu ◽  
Nguyen Tien Manh ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Wrf Model ◽  
Rainfall Event ◽  
Heavy Rainfall Event

Sedimentary Basins of the Republic of Yemen : Their Structural Evolution and Geological Characteristics

1996 ◽  
Vol 51 (6) ◽  
pp. 763-775 ◽  
Author(s):  
Z. R. Beydoun ◽  
M. L. As-Saruri ◽  
R. S. Baraba
Keyword(s):  
Structural Evolution ◽  
Sedimentary Basins ◽  
Geological Characteristics ◽  
The Republic

A Study on the Geological Characteristics and Slope Landform Changes of Mt. Umyeon

2013 ◽  
Vol 11 (1_2) ◽  
pp. 1-14 ◽  
Author(s):  
Joo-hwan KIM ◽  
Jeong-sik OH
Keyword(s):  
Geological Characteristics

Enteric viruses in drinking water supplies

2002 ◽  
Vol 2 (3) ◽  
pp. 17-22
Author(s):  
A.P. Wyn-Jones ◽  
J. Watkins ◽  
C. Francis ◽  
M. Laverick ◽  
J. Sellwood
Keyword(s):  
Drinking Water ◽  
Heavy Rainfall ◽  
Liquid Culture ◽  
Plaque Assay ◽  
Enteric Viruses ◽  
Enteric Virus ◽  
The Other ◽  
Raw Water ◽  
Rt Pcr ◽  
Water Supplies

Two rural spring drinking water supplies were studied for their enteric virus levels. In one, serving about 30 dwellings, the water was chlorinated before distribution; in the other, which served a dairy and six dwellings the water was not treated. Samples of treated (40 l) and untreated (20 l) water were taken under normal and heavy rainfall conditions over a six weeks period and concentrated by adsorption/elution and organic flocculation. Infectious enterovirus in concentrates was detected in liquid culture and enumerated by plaque assay, both in BGM cells, and concentrates were also analysed by RT-PCR. Viruses were found in both raw water supplies. Rural supplies need to be analysed for viruses as well as bacterial and protozoan pathogens if the full microbial hazard is to be determined.

Study on Manhole Internal Pressure Countermeasures during Heavy Rainfall

2017 ◽  
Vol 2017 (4) ◽  
pp. 5684-5698
Author(s):  
Yuki Kuwahara ◽  
Yoshiharu Itaya ◽  
Yuji Itou
Keyword(s):  
Internal Pressure ◽  
Heavy Rainfall

ISSUES ON SCHOOL SAFETY MANAGEMENT AT THE TIME OF FRONTAL CON CENTRATED HEAVY RAINFALL - CASE ANALYSIS OF NORTHERN KYUSHU HEAVY RAIN IN 2017 –

2018 ◽  
Vol 74 (2) ◽  
pp. I_77-I_84
Author(s):  
Susumu NAKANO ◽  
Junko KANAI ◽  
Mari TAKAHASHI ◽  
Kazuhito FUJIWARA ◽  
Shingo YAMASHIRO
Keyword(s):  
School Safety ◽  
Heavy Rainfall ◽  
Safety Management ◽  
Case Analysis ◽  
Heavy Rain

ROLE OF COMMUNITY JUDGEMENT STANDARDS IN NORTHERN KYUSHU HEAVY RAINFALL IN 2017 - FUNCTION OF DISASTER RESPONSE SWITCH IN DISASTERS -

2018 ◽  
Vol 74 (2) ◽  
pp. I_31-I_39 ◽  
Author(s):  
Kensuke TAKENOUCHI ◽  
Yasuyuki KANO ◽  
Katsuya YAMORI
Keyword(s):  
Disaster Response ◽  
Heavy Rainfall ◽  
Response Switch

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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