Spatial Analysis of the Landslide Characteristics Caused by Heavy Rainfall in the Northern Kyushu District in July, 2017 Using Topography, Geology, and Rainfall Levels

Danjo, Toru; Storm, Flood; Ishizawa, Tomohiro; Kimura, Takashi

doi:10.20965/jdr.2018.p0832

Cited by 11 publications

(7 citation statements)

References 3 publications

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“…This is due to the larger value of slopes

θ

and profile curvatures

Χ_{p}

at the portion. The estimated coefficient for tangential curvature

α_{3}

was positive, thus agreeing with the fact that landslides likely occurred in ridge‐like terrains (Danjo et al, 2018).…”

Section: Predictive Simulation Employing Statistical Landslide Predic...supporting

confidence: 79%

“…This is due to the larger value of slopes θ and profile curvatures Χ p at the portion. The estimated coefficient for tangential curvature α 3 was positive, thus agreeing with the fact that landslides likely occurred in ridge-like terrains (Danjo et al, 2018). The receiver operating characteristic curve for this model can be obtained as shown in Figure 6, by plotting the sensitivity (TP/[TP + FN]) and 1-specificity (1-TN/ [TN + FP]) as the change in the cut-off probability.…”

Section: Statistical Landslide Prediction Model For Generating Artifi...supporting

confidence: 72%

“…According to radar observations, the maximum total rainfall exceeded 700 mm, and the area in which total rainfall exceeded 650 mm is roughly spatially comparable with the area of concentrated shallow landslides (Kyoto University, 2018). Danjo et al (2018) spatially analysed the relationship between the slope failure source and topographic characteristics and reported that the source areas of surface landslides are more likely to have occurred in ridge-like terrain than in valley-like terrain.…”

Section: Study Area and Target Eventmentioning

confidence: 99%

See 2 more Smart Citations

Predictive simulation of concurrent debris flows: How slope failure locations affect predicted damage

Yamanoi

Oishi

Kawaike

et al. 2021

J Flood Risk Management

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Predictive simulation of concurrent debris flows using only pre-disaster information has been difficult, partly because of problems faced in predicting debris-flow initiation locations (i.e., slope failure). However, because catchment topography has convergent characteristics with all channels in it joining each other as they flow downstream, damage to downstream areas could be predicted using relatively inaccurate initiation points. Based on this hypothesis, this study uses debris-flow initiation points generated randomly with statistical slope failure prediction and performs a many-case simulation across numerous initiation points to quantify the effect of slope-failure locations in terms of deviations in the predicted water level and topographic change. This paper presents the results of 2D simulations based on a conventional debris-flow model that was run on a supercomputer to realise simulations of many cases. The obtained relative standard deviation was found to decrease as the debris flow and sediment-laden flood approached the downstream area, indicating that the predictability of the inundation and topographic change can be decided from the terrain characteristics.

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“…This is due to the larger value of slopes

θ

and profile curvatures

Χ_{p}

at the portion. The estimated coefficient for tangential curvature

α_{3}

was positive, thus agreeing with the fact that landslides likely occurred in ridge‐like terrains (Danjo et al, 2018).…”

Section: Predictive Simulation Employing Statistical Landslide Predic...supporting

confidence: 79%

Section: Statistical Landslide Prediction Model For Generating Artifi...supporting

confidence: 72%

Section: Study Area and Target Eventmentioning

confidence: 99%

See 1 more Smart Citation

Predictive simulation of concurrent debris flows: How slope failure locations affect predicted damage

Yamanoi

Oishi

Kawaike

et al. 2021

J Flood Risk Management

View full text Add to dashboard Cite

show abstract

“…Landslides and non-landslide points are required to predict the probability of landslide occurrences in the logistic regression model and evaluating the model accuracy by trueor false-positive and negative rates. Landslide points at pixels with the highest elevation for each landslide were set [39,40]. Non-landslide points were randomly selected in areas without landslides using the "random points inside polygons" function in QGIS.…”

Section: Discussionmentioning

confidence: 99%

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

Lusiana

Shinohara

2022

Water

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Landslides often cause deaths and severe economic losses. In general, forests play an important role in reducing landslide probability because of the stabilizing effect of the tree roots. Although fruit groves consist of trees, which are similar to forests, practical land management, such as the frequent trampling of fields by laborers and compression of the terrain, may cause such land to become prone to landslides compared with forests. Fruit groves are widely distributed in hilly regions, but few studies have examined their role in landslide initiation. This study aims at filling this gap evaluating the predisposing and triggering conditions for rainfall-triggering landslides in part of Uwajima City, Japan. A large number of landslides occurred due to a heavy rainfall event in July 2018, where citrus groves occupied about 50% of the study area. In this study, we combined geodata with a regression model to assess the landslide hazard of fruit groves in hilly regions. We developed maps for five conditioning factors: slope gradient, slope aspect, normalized difference vegetation index (NDVI), land use, and geology. Based on these five maps and a landslide inventory map, we found that the landslide area density in citrus groves was larger than in forests for the categories of slope gradient, slope aspect, NDVI, and geology. Ten logistic regression models along with different rainfall indices (i.e., 1-h, 3-h, 12-h, 24-h maximum rainfall and total rainfall) and different land use (forests or citrus groves) in addition to the other four conditioning factors were produced. The result revealed that “citrus grove” was a significant factor with a positive coefficient for all models, whereas “forest” was a negative coefficient. These results suggest that citrus groves have a higher probability of landslide initiation than forests in this study area. Similar studies targeting different sites with various types of fruit groves and several rainfall events are crucial to generalize the analysis of landslide hazard in fruit groves.

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“…In recent years, the occurrence of severe disasters in Japan caused by localized and persistent heavy rainfall has increased (e.g., Danjo et al 2018;Tsuguti et al 2019;Tsuji et al 2020). For example, in the latter stage of the Baiu season in both 2017 and 2020, Kyushu experienced torrential rainfall of >200 mm (3h) −1 (Japan Meteorological Agency…”

Section: Introductionmentioning

confidence: 99%

Verification of Forecasted Three-Hour Accumulated Precipitation Associated with “Senjo-Kousuitai” from Very-Short-Range Forecasting Operated by the JMA

Hatsuzuka

Kato

Shimizu

et al. 2022

Journal of the Meteorological Society of Japan

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Spatial Analysis of the Landslide Characteristics Caused by Heavy Rainfall in the Northern Kyushu District in July, 2017 Using Topography, Geology, and Rainfall Levels

Cited by 11 publications

References 3 publications

Predictive simulation of concurrent debris flows: How slope failure locations affect predicted damage

Predictive simulation of concurrent debris flows: How slope failure locations affect predicted damage

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

Verification of Forecasted Three-Hour Accumulated Precipitation Associated with “Senjo-Kousuitai” from Very-Short-Range Forecasting Operated by the JMA

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