R5-17
A Dynamic Rainfall-based Warning Model for Predicting Landslides Using Radar Rainfall
Data○Chen-Yu Chen (Soil and Water Conservation Bureau, Taiwan)
1. INTRODUCTION
The Rainfall Triggering Index (RTI) model is adopted to set up the critical rainfall for each township since2005 in Taiwan, and the debris-flow warning system based on the RTI model is successful in reducing casualties.
However, the antecedent rainfall calculation using the deduction coefficient of "t" days in the RTI model causes thefalse alert rate higher under some rainfall patterns (e.g., long-term duration and lower rainfall intensity). Besides,
the RTI model simplifies each rainfall event as a represent point assembled by short-term and long-term rainfall
indexes, nevertheless, the different definitions of rainfall events would lead to the diverse results on demarcating
critical line. To improve above-mentioned problems, this study suggests a modified method to calculate theeffective accumulated rainfall and proposes the new model which uses the continuous rainfall data to determine thewarning rainfall (i.e., it’s not necessary to define the rainfall event.).

2. METHOD
The Rainfall Triggering Index (RTI) model isadopted to set up the critical rainfall for eachtownship since 2005 in Taiwan. Its method ofcalculation is summarized as Eq.(1) and Eq.(2).
RTI

(1)
∑(2)

where I is 60 minutes of rainfall intensity; Rt is
the effective accumulated rainfall which is theamount of rainfall for the antecedent i days (Ri); α isa weighting factor and is set to be 0.7 (modified since2014). However, the antecedent rainfall calculation
using the deduction coefficient of "t" days in the RTI
model causes the false alert rate higher under somerainfall patterns (e.g., long-term duration and lower
rainfall intensity). To solve the problem, this study
suggests a modified method, which uses the same
deduction coefficient and half-life but deducting eachan hour, to calculate the effective precipitation
rainfall as Eq.(3).

0.7

(3)

where It is the current hourly rainfall, Rt-1 is theeffective accumulated rainfall an hour ago. Comparedto the Eq.(2), Eq.(3) offers higher computingperformance, and it is appropriate to a large numberof grid computing.
Using the Eq.(3), the new landslide warningmodel is established by using the radar rainfall data,
which is called QPE (Quantitative PrecipitationEstimation) and displays by meshes (1.3x1.3 km)
each 10 minute since 2005. We also collect theremote-sensing images and disaster records in thepast decade to identify the results of landslideprediction. The study area is located at Wulai Districtin north of Taiwan. The flow chart of the study
process is shown in Fig.1. This warning mode alsouses each QPE mesh as warning unit (237 meshes instudy area), and the adjustment rule of landslidedisaster warning rainfall (Rw) is shown as Fig.2.

Fig. 2 Adjustment rules of landslide disaster warning rainfall (Rw)for each QPE meshFig. 1 Flow chart of the study process.

- 292 -3. RESULTS AND DISCUSSIONS
To evaluate the spatial accuracy of the warningmodel, the positive alert rate (PAR), false alert rate(FAR) and missing alert rate (MAR) are defined asTable1.

alert six hours before the disaster during typhoon
Soudelor in 2015. Moreover, the waring model didn’t
issue an alert during typhoon Duluan which did notlead to disaster.

Table 1 The definition of positive alert rate, false positive rate
and missing rate for warning system of sediment disaster
Alert (Y)Alert (N)A
B
Collapse (Y)CD
Collapse (N)Positive alert rate
False alert rate
Missing alert(PAR) (%)(FAR) (%)
rate (MAR) (%)(A+D)/(A+B+C+D)
C/(A+B+C+D)
B/(A+B+C+D)

The prediction result of the warning model forthe past decade is shown as Table 2. The average
results are PAR=69.5%, FAR= 24.1%, MAR=6.4%.
Table 2 The results of positive alert rate, false alert rate andmissing alert rate for warning system during 2006~2015
2006
Collapse (Y)Collapse (N)PAR (%)
84.8
2008
Collapse (Y)Collapse (N)PAR (%)
29.5
2010
Collapse (Y)Collapse (N)PAR (%)
86.5
2012
Collapse (Y)Collapse (N)PAR (%)
35.0
2014
Collapse (Y)Collapse (N)PAR (%)
91.6

Alert(Y)01
FAR(%)
0.4
Alert(Y)35162
FAR(%)
68.4
Alert(Y)1525
FAR(%)
10.5
Alert(Y)21151
FAR(%)
63.7
Alert(Y)20
FAR(%)
0.0

Alert(N)35201
MAR(%)
14.8
Alert(N)535
MAR(%)
2.1
Alert(N)7190
MAR(%)
3.0
Alert(N)362
MAR(%)
1.3
Alert(N)20215
MAR(%)
8.4

2007
Collapse (Y)Collapse (N)PAR (%)
42.6
2009
Collapse (Y)Collapse (N)PAR (%)
89.0
2011
Collapse (Y)Collapse (N)PAR (%)
87.3
2013
Collapse (Y)Collapse (N)PAR (%)
92.8
2015
Collapse (Y)Collapse (N)PAR (%)
56.1

Alert(Y)27
127
FAR(%)
53.6
Alert(Y)128
FAR(%)
3.4
Alert(Y)116
FAR(%)
2.5
Alert(Y)49
FAR(%)
3.8
Alert(Y)7181
FAR(%)
34.2

Alert(N)974
MAR(%)
3.8
Alert(N)18199
MAR(%)
7.6
Alert(N)24196
MAR(%)
10.1
Alert(N)8216
MAR(%)
3.4
Alert
(N)
2362
MAR(%)
9.7

A good warning model should predict not only
locations of landslides but also the occurring time.
Figure 3 presents that the warning model issued anFig.3 The occurring time of the landslide in Wulai Waterfall Parkand the change of effective accumulated rainfall (EAR)

While the PAR is adopted as evaluation index inmany studies, its result is very susceptible to annual
rainfall. Generally, PAR can reflect whether thewarning model is over-predicting (i.e., rainfall is not
so heavy but alert is issued.). Table 3 shows PAR ishigher in 2006, 2009, 2010~2014 because the annual
rainfall is less. The results present the warning modelis reliable. However, to verify the warning model canissue alerts before landslide occurring, this study
defines the waring hit rate (WHR) =A/(A+B). Here A,
B see Table 1. As shown in Table 3, WHR is higher
than 75% during the high annual rainfall in 2007,2008, 2012, and 2015. That is, the warning model
offers good prediction of times and locations ofrainfall-induced landslides.
Table 3 The results of average warning rainfall, max EAR andwarning hit rate
Year

Number ofmeshes(collapse)

Average ofRw for all
meshes(mm)

2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Average

35
3640
30
2235
24
12
22
94
37

441.8
401.8
463.8
512.2
492.7
497.0
477.3
549.2
544.1
520.7
490.1

Average ofmax EAR
for allmeshes
(mm)
195.8
497.1
577.7
282.0
303.9
241.5
569.4
309.8
192.0
531.2
377.8

Positiverate (%)

Warning
hit rate(%)

84.8
42.6
29.5
89.0
86.5
87.3
35
92.8
91.6
56.1
69.5

0
75.0
87.5
40
68.2
31.4
87.5
33.3
9.1
75.5
50.8

4. CONCLUSIONSThis study establishes an operable warning model, which uses the radar rainfall data, the identified results ofremote-sensing image, and the disaster records, to predict landslides. It offers well-predicting for occurring timeand locations of landslides. This study also proposes a new platform which displays the rainfall-induced disasterhot zones. These findings can help government officials to make appropriate decisions during emergency response.

Keywords: Landslide, debris flow, warning system, QPESUMS, effective accumulated rainfall.

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