Report of the Monobe River Flood in July 2018

Kubo, Takayuki; Yamaji, Hideyuki; Okabayashi, Fukuyoshi; Shinkawa, Kazuyuki; Kakehi, Yasumasa

doi:10.2208/jscejhe.75.1_208

Cited by 2 publications

(3 citation statements)

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“…Figure 1 indicates that d R_13 < d R_08 < d R_01 . The relevance analyzed above suggests the arrival time of precipitation t R_n follows t R_13 (2-7) < t R_08 (8-9) ≤ t R_01 (8)(9)(10)(11). The t R_13 and t R_08 are clearly different, and at most about 6-7 h traveling time in between.…”

Section: Stage Estimation Sensitivity To the Rain Gaugesmentioning

confidence: 95%

“…For example, heavy rains in 2018 threatened flooding of the Monobe river in Kochi, Japan, triggering evacuation advice and orders. Yet most of the residents did not evacuate, presumably because flooding had not occurred there for over 100 years [9]. One way that might have helped to convince the residents to evacuate would have been to provide them with an accurate estimate of when a given heavy rainfall would produce a flood downstream.…”

Section: Introductionmentioning

confidence: 99%

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River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

Wakatsuki

Nakane

Hashino

2022

Water

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The increasing frequency of devastating floods from heavy rainfall—associated with climate change—has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is a long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with RMSE less than 67 cm for about 10 m of stage peaks, as well as accurately simulating stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision, we found that the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well-simulated, the physical concepts of runoff appear to be soundly embedded in the MLP.

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Section: Stage Estimation Sensitivity To the Rain Gaugesmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

Wakatsuki

Nakane

Hashino

2022

Water

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“…Kubo et al 27) focused on the Monobe River, which experienced record flooding during the July 2018 heavy rain event. They presented an overview of the flooding and flood forecasting, and so on, and analyzed the causes based on an opinion survey of the effectiveness of the backward displacement of the levee that had been conducted in recent years and the problematic low level of people evacuated.…”

Section: (4) Damage To River Structuresmentioning

confidence: 99%

<i>-Editorial-</i> SPECIAL ISSUE ON THE HEAVY RAIN EVENT OF JULY 2018 IN WESTERN JAPAN

et al. 2021

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The unprecedented heavy rainfall that occurred in western Japan in 2018 caused flooding, inundation, and sediment disasters extending over a wide area, resulting in various types of damage to lifelines, transport systems, diverse structures and so on, and exposing problems that extend across many sectors. One of the major roles of the Japan Society of Civil Engineers is to compile disaster survey data regarding the frequent occurrence of increasingly severe disasters in recent years and then share this information widely with the public. In order to contribute to the development of disaster prevention technologies and academic fields, this special issue is devoted to a collection of reports and preliminary research papers regarding the 2018 heavy rain event in western Japan. Some of the Japanese papers have been translated into English and posted on the Journal of JSCE.

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Report of the Monobe River Flood in July 2018

Cited by 2 publications

References 0 publications

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

<i>-Editorial-</i> SPECIAL ISSUE ON THE HEAVY RAIN EVENT OF JULY 2018 IN WESTERN JAPAN

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