E3S Web of Conferences 415, 05017 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341505017

Topographical criteria for the occurrence of landslides causing
debris flows in the 2017 torrential rain in northern Kyushu,
Japan
Takehiro Ohta1*, Kanako Hamamoto1,2, and Seiya Eguchi1,3
1Yamaguchi University, Yoshida 1677-1, Yamaguchi, Japan
2NEXCO-West Consultants Company Limited, Higashi-ku Futaba-no-sato 3-5-7, Hiroshima, Japan
3Kyusyu Electric Engineering Consultants Inc., Chuo-ku Kiyokawa 2-13-6, Fukuoka, JapanAbstract. In the volcanic rock fields around Hita City, 19 landslides occurred in the 2017 torrential rains
in northern Kyushu, 11 of which triggered debris flows. In order to mitigate debris flows disasters causedby landslides, it is important to assess the points where landslides are most likely to occur. Therefore,
topographic profiles of landslide and non-landslide slopes were calculated from digital elevation models
obtained before the disaster. The results indicate that there is a topographic criterion for landslide occurrencein the relationship between the topographic position index (TPI) and the tangent Laplacian.

1 Introduction
In Japan, debris flows caused by landslides under hugeprecipitation amounts are common. About 200
landslides and debris flows occurred in Northern
Kyushu during heavy rain at Asakura City, Toho Village,and Hita City on July 5th to 6th, 2017. The source ofdebris flows at Asakura city, in which the geology
consists of metamorphic rocks and granitic rocks, wasmany small, shallow landslides with a few large

landslides [1]. In contrast, large landslides were thesource of debris flows at Hita and Toho districts [1], inwhich volcanic rocks are distributed.
The 2017 torrential rains in northern Kyushu caused
landslides in 37 locations in Hita and Toho districts,
where volcanic rocks are distributed, and 19 of theselandslides caused debris flows. Ohta and Eguchi [2]investigated these landslides and reported that thelandslides occurred at the caprock structure near theformer collapse site and that the geomorphological andFig. 1. Topographical map around the Hita, Toho, and Asakura area [2]. This map is based on the digital map published by theGeospatial ,nformation Authority of Japan. The red frame shows the study area. Red point shows the Hita AMeDAS point.
* Corresponding author: takohta@yamaguchi-u.ac.jp
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 05017 (2023)
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geological features that transitioned to debris flows were
not distinct. However, it is not established the strategy
to extract the slopes which have high potential to occur
landslide causing debris flows.
As climate change causes torrential rains, there isconcern that huge landslides accompanied by debris
flows may occur simultaneously in many catchments.
Therefore, it is important to clarify the criteria for theoccurrence of landslide in order to mitigate debris flows
hazards. In this report, a GIS analysis using a digital
elevation model was used to examine the topographic
criteria for landslide occurrence by comparing the

topographic profiles of landslide slopes and nonlandslide slopes.

2 Methods
2.1 Aerial photographic interpretation
Using aerial photographs taken after the disaster, thelocations of debris flows and landslides were extracted.
Aerial photographs taken before the occurrence of thedisaster were used to understand the characteristics ofthe topography before the disaster.

㻌㻌㻝㻝㻝㻠㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻝㻜㻝㻞㻝㻝㻥㻢㻤㻌㻡㻣㻢㻠㻡㻟㻠㻟㻞㻞㻝㻝㻜㻜㻜㻝㻜㻜㻞㻜㻜㻟㻜㻜㻠㻜㻜㻡㻜㻜㻌㼑㼘㼑㼢㼍㼠㼕㼛㼚㻝㻡㻝㻠㻝㻟㻝㻞㻝㻝㻝㻜㻥㻤㻣㻢㻡㻠㻟㻞㻝㻜㻢㻜㻜㻣㻜㻜㻜㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻝㻜㻞㻜㻞㻜㻌㼟㼘㼛㼜㼑㻔㼻㻕㻟㻜㻠㻜㻡㻜㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻝㻤㻝㻢㻝㻠㻝㻞㻌㻌㻯㼛㼡㼚㼠㼟㻝㻜㻤㻢㻠㻞㻜㻜㻝㻤㻝㻢㻠㻡㻥㻜㻝㻟㻡㻝㻤㻜㻞㻞㻡㻌㼍㼟㼜㼑㼏㼠㻞㻣㻜㻟㻝㻡㻟㻢㻜䌦㻠㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟䌦㻟䌦㻞䌦㻝㻜㻝㻞㻜㻚㻜㻞㻚㻜㼤㻝㻜㻤㻠㻚㻜㼤㻝㻜㻤㻜㻚㻜㻞㻚㻜㼤㻝㻜㻟㻌㼀㻼㻵㻟㻠㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻝㻞㻝㻜㻝㻠㻝㻞㻯㼛㼡㼚㼠㼟㻤㻌㻝㻜㻤㻢㻌㻯㼛㼡㼚㼠㼟㻌㻝㻜㻣㻯㼛㼡㼚㼠㼟㻯㼕㼡㼚㼠㻤㻯㼛㼡㼚㼠㼟㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻝㻟㻥㻢㻠㻠㻞㻞㻜㻜㻞㻜㻜㻚㻜㻞㻚㻜㼤㻝㻜㻤 㻠㻚㻜㼤㻝㻜㻤 㻢㻚㻜㼤㻝㻜㻤 㻤㻚㻜㼤㻝㻜㻤 㻝㻚㻜㼤㻝㻜㻥䌦㻢㻚㻜㼤㻝㻜㻤㻌㻳㼑㼚㼑㼞㼍㼘㻌㼏㼡㼞㼢㼍㼠㼡㼞㼑㻞㻞㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻝㻤㻝㻢㻯㼛㼡㼚㼠㼟㻝㻞㻌㻯㼛㼡㼚㼠㼟㻝㻠㻝㻜㻤㻢㻠㻞㻜䌦㻢㻚㻜㼤㻝㻜㻤䌦㻠㻚㻜㼤㻝㻜㻤䌦㻞㻚㻜㼤㻝㻜㻤㻜㻚㻜㻞㻚㻜㼤㻝㻜㻤㻠㻚㻜㼤㻝㻜㻤㻢㻚㻜㼤㻝㻜㻤㻼㼘㼍㼚㻌㻯㼡㼞㼢㼍㼠㼡㼞㼑㻟㻜㻞㻤㻞㻢㻞㻠㻞㻞㻞㻜㻝㻤㻝㻢㻝㻠㻝㻞㻝㻜㻤㻢㻠㻞㻜䌦㻤㻚㻜㼤㻝㻜㻟䌦㻠㻚㻜㼤㻝㻜㻤䌦㻞㻚㻜㼤㻝㻜㻤㻌㻼㼞㼛㼒㼕㼘㼑㻌㻯㼡㼞㼢㼍㼠㼡㼞㼑㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟䌦㻢㻚㻜㼤㻝㻜㻟䌦㻠㻚㻜㼤㻝㻜㻟䌦㻞㻚㻜㼤㻝㻜㻟㼠㼍㼚㼓㼑㼠㼕㼍㼘㻌㼏㼡㼞㼢㼍㼠㼡㼞㼑

Fig. 2. Histogram of each topographic quantity.

2㻢㻚㻜㼤㻝㻜㻤㻌䌦㻝㻚㻜㼤㻝㻜㻥 䌦㻤㻚㻜㼤㻝㻜㻤 䌦㻢㻚㻜㼤㻝㻜㻤 䌦㻠㻚㻜㼤㻝㻜㻤 䌦㻞㻚㻜㼤㻝㻜㻤㻠㻚㻜㼤㻝㻜㻟㻢㻚㻜㼤㻝㻜㻟㻤㻚㻜㼤㻝㻜㻟

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

2.2 Topographic measurement

3.2 Discriminant analysis

To determine the topographic factors of landslide
development that cause debris flows, topographic
measurements were taken using a 10-meter mesh digital
elevation model created by the Geospatial Information
AuthorityofJapanfromphotogrammetricmeasurements in 2016, before the disaster occurred.
Elevation, slope, slope direction, topographic positionindex (TPI) [3], general curvature, profile curvature,plan curvature [4], and tangential curvature [5] weredetermined for the landslide and non-landslide sites. TPI
is defined as the deference between the elevation of thetarget point (‫ݖ‬଴ ) and the average elevation (‫ݖ‬ҧ ) withinspecific radius around it [3]. Definitional equations areshown in Equation 1 and 2 [6].

Discriminant analysis was performed on several
combinations of measured topographic quantities inorder to establish criteria for landslide-induced
landslides. Since the statistic distribution of generalcurvature and profile curvature differ between landslideand non-landslide slopes, a discriminant analysis
conducted using these parameters as explanatory
variables. The results of the analysis are shown in Table
1, and the linear discriminant function (Eq. 3) is shownin the relationship diagram between general curvatureand profile curvature, along with data for landslide andnon-landslide areas (Fig. 3). The calculated error ratio
was 19.22%.

ܶܲ‫ ܫ‬ൌ ‫ݖ‬଴ െ ‫ݖ‬ҧଵ‫ݖ‬ҧ ൌ σ௜‫א‬ோ ‫ݖ‬௜௡ೃProf. cur.=-0.401*Gen. cur.+1.622*108

(1)

(3)

Table 1. Results of discriminant analysis using generalcurvature and profile curvature.

(2)

For each of the topographic quantities, the landslidearea was measured at the location of the scarp
occurrence, and the non-landslide area was measured at
the top of the slope. The non-landslide areas were set up
as follows based on Asada et al. [7] The slope unit wasdefined as the area bounded by the ridgelines andstreamlines extracted within the primary valley
catchment. The non-landslide slope top within the slope
unit was defined as the convex area above the depression
in the TPI map.

Estimated groupnonlandslide
31
683.78%
16.22%
1035
22.22%
77.78%
41
41
50.00%
50.00%

landslidelandslide
nonlandslide
Total㻢㻚㻜㼤㻝㻜㻤

3 Results and discussion㻤㻌 㻼㼞㼛㼒㻚㻯㼡㼞㻚㻩㻙㻠㻚㻜㼤㻝㻜㻼㼞㼛㼒㼕㼘㼑㻌㻯㼡㼞㼢㼍㼠㼡㼞㼑

From the aerial photographic interpretation, we detected
37 landslides induced by the 2017 northern Kyushu
rainstorms and measured the topographic quantities ineach. Forty-five non-landslide slopes adjacent tolandslide were picked up and their topography measured.

㻜㻚㻠㻜㻝㻖
㻳㼑

㼚㻚㻯
㼡㼞㻚㻗

㻞㻚㻜㼤㻝㻜㻤

㻝㻚㻢
㻞㻞㻖

Total
37
100.00%45
100.00%82
100.00%㻝㻜 㻤㻌㻜㻚㻜䌦㻞㻚㻜㼤㻝㻜㻤

3.1 Topographic measurement䌦㻠㻚㻜㼤㻝㻜㻤

Fig 2 shows the histograms of each topographic quantity.
There is no remarkable difference in height, slope, oraspect between landslide and non-landslide slopes.
There is no marked difference between landslide andnon-landslide slopes in elevation, slope, or aspect. The
TPI for landslide slopes shows a Gaussian normal
distribution centered at 0, whereas the TPI for most nonlandslide slopes is positive. On landslide slopes, thegeneral curvature and profile curvature show a Gaussian
distribution with negative mean and median, while on
non-landslide slopes, they have a Gaussian distribution
with positive mean and median. Thus, the TPI, generalcurvature, and profile curvature show statisticallydifferent distributions between landslide and nonlandslide slopes. For both landslide and non-landslideslopes, both plane curvature and tangential curvature are
Gaussian normally distributed around 0. For bothparameters, the distribution for the landslide slope iswider than that for the non-landslide slope.

䌦㻢㻚㻜㼤㻝㻜㻤

㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼘㼕㼚㼑㼍㼞㻌㼐㼕㼟㼏㼞㼕㼙㼕㼚㼍㼚㼠㻌㼒㼡㼚㼏㼠㼕㼛㼚䌦㻤㻚㻜㼤㻝㻜㻤

䌦㻠㻚㻜㼤㻝㻜㻤

㻜㻚㻜㻠㻚㻜㼤㻝㻜㻤

㻤㻚㻜㼤㻝㻜㻤

㻳㼑㼚㼑㼞㼍㼘㻌㼏㼡㼞㼢㼍㼠㼡㼞㼑

Fig. 3. Relationship diagram between general curvature andprofile curvature with the linear discriminant function.

The results of the discriminant analysis using TPI
and tangential curvature as explanatory variables areshown in Table 2 and Fig. 4. The linear discriminantfunction is as follows.
Tang. cur.=3775*TPI-1822(4)

In this analysis, the calculated error ratio was 12.07%,
the lowest value among the trials in this study. Therefore,
it can be concluded that the occurrence of landslides
causing debris flows can be evaluated using topographic
criteria based on TPI and tangential curvature at the topof the slope. If the tangential curvature of the slope is3

E3S Web of Conferences 415, 05017 (2023)
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References

not less than the value of the right-hand side of Equation
4, then landslide causing debris flow are likely to occur.

1.

Table 2. Results of discriminant analysis using TPI andtangential curvature.

Estimated groupnonlandslide33
489.19%
10.81%
639
13.33%
86.67%
39
43
47.56%
52.44%

landslidelandslide
nonlandslide
Total

Total

2.

37
100.00%45
100.00%82
100.00%3.

4.

㻝㻜㻜㻜㻜㻌㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼚㼛㼚㻙㼘㼍㼚㼐㼟㼘㼕㼐㼑㼟㻌㼘㼕㼚㼑㼍㼞㻌㼐㼕㼟㼏㼞㼕㼙㼕㼚㼍㼚㼠㻌㼒㼡㼚㼏㼠㼕㼛㼚㻣㻡㻜㻜

5.

㻞㻡㻜㻜
㻜

6.
㻤㻞㻞㻌䌦㻞㻡㻜㻜㼀㻼㻵 㻙㻝㼠㼍㼚㼓㼑㼠㼕㼍㼘㻌㼏㼡㼞㼢㼍㼠㼡㼞㼑㻡㻜㻜㻜㼢㻘㻩㻟㻣㻣㻡㻖䌦㻡㻜㻜㻜㼀㼍㼚㼓㻚㻯㼡㼞䌦㻣㻡㻜㻜䌦㻝㻜㻜㻜㻜䌦㻠䌦㻟䌦㻞䌦㻝㻜㻝㻞㻟㻠

7.

㼀㻼㻵Fig. 4. Relationship diagram between general curvature andprofile curvature with the linear discriminant function.

4 Conclusions
In order to establish a strategy for identifying slopes
with a high potential for debris flow-causing landslide,
GIS analysis using a digital elevation model was used toexamine the criteria for the landslide-causing terrain.
The results from these investigations suggest that;
1) There is no marked difference between
landslide and non-landslide slopes in elevation,slope, or aspect.
2) TPI, general curvature, and profile curvatureshow statistically different distributions
between landslide and non-landslide slopes.
3) From the results of the discriminant analysis
using TPI and tangential curvature, which
difference between landslides and nonlandslides, It was not possible to obtain a linear
discriminant function with good accuracy.
4) When the explanatory variables were TPI andtangent curvature, the most accurate linear
discriminant function was obtained, and its
error rate was 12.07%, indicating that it could
discriminate with sufficient accuracy.

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