Prediction of the Periglacial Debris Flow in Southeast Tibet Based on Imbalanced Small Sample Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Method
2.2.1. Borderline SMOTE
2.2.2. Random Forest (RF)
2.2.3. Support Vector Machine (SVM)
2.3. Data and Indicators
2.3.1. Data Sources
2.3.2. Basic Indicators
- -
- Meteorology
- -
- Topography
- -
- Geology
- -
- Underlying surface
3. Results and Discussion
3.1. Selection of Prediction Indicators
3.2. Modeling and Comparative Analysis
3.2.1. Training and Validation
3.2.2. Testing
3.2.3. Comparison
I-D Model and I-D-A Model
Resampling Model and Non-Resampling Model
SMOTE Model and Borderline SMOTE Model
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Gully No. | Gully |
Area (km2) |
Event No. | Date | Time |
Possible Causes |
Monitoring Station |
---|---|---|---|---|---|---|---|
1 | Bitong | 24 | DF1 | 2016-09-05 | 06:30 | Snowmelt, Precipitation | PEILONG |
DF2 | 2018-07-11 | 03:00 | Snowmelt, Precipitation | GUXIANG | |||
2 | Chaobu | 16 | DF3 | 2017-08-03 | 17:00 | Ice and Snow melt | GUXIANG |
3 | Chidan | 28 | DF4 | 2016-09-05 | 11:00 | Snowmelt, Precipitation | PEILONG |
4 | Dada | 3 | DF5 | 2020-07-10 | 16:10 | Snowmelt, Precipitation | TIANMO |
5 | East Lapu | 4 | DF6 * | 2013-07-05 | 21:00 | Snowmelt, Precipitation | MIDUI/ SONGZONG |
DF7 * | 2014-07-24 | 20:00 | Snowmelt, Precipitation | ||||
DF8 | 2018-05-22 | 17:00 | Snowmelt, Precipitation | ||||
6 | Guxiang | 25 | DF9 | 2020-07-09 | 21:00 | Ice and Snow melt | GUXIANG-2 |
7 | Jiaolong | 22 | DF10 | 2016-09-05 | —— | Snowmelt, Precipitation | PEILONG |
8 | Jiurong | 7 | DF11 * | 2014-08.18 | 23:00 | Snowmelt, Precipitation | SONGZONG |
DF12 * | 2014-08-23 | 02:00 | Snowmelt, Precipitation | ||||
DF13 * | 2015-08-04 | 22:30 | Snowmelt, Precipitation | ||||
DF14 * | 2015-08-20 | 07:40 | Snowmelt, Precipitation | ||||
9 | Midui | 1 | DF15 * | 2015-08-19 | 23:30 | Snowmelt, Precipitation | MIDUI/ SONGZONG |
10 | Motuo | 3 | DF16 * | 2015-08-19 | 22:30 | Snowmelt, Precipitation | BOMI/ SONGZONG |
11 | Rongqian | 5 | DF17 * | 2015-08-19 | 18:30 | Snowmelt, Precipitation | BOMI/ SONGZONG |
12 | Shalong | 15 | DF18 * | 2015-08-19 | 21:00 | Snowmelt, Precipitation | SONGZONG |
13 | Tianmo | 18 | DF19 | 2018-07-11 | 03:00 | Snowmelt, Precipitation | GUXIANG |
14 | West Jiazong | 2 | DF20 * | 2012-09-22 | 09:00 | Snowmelt, Precipitation | MIDUI |
DF21 * | 2013-07-05 | 21:00 | Snowmelt, Precipitation | ||||
DF22 * | 2013-07-31 | 18:00 | Snowmelt, Precipitation | ||||
15 | Zhuonong | 5 | DF23 * | 2015-08-19 | 19:20 | Snowmelt, Precipitation | BOMI/ SONGZONG |
Type | Content | Sources |
---|---|---|
Precipitation | Hourly data of the stations of PEIlONG, BOMI, GUXIANG, SONGZONG, MIDUI on the day and the adjacent days of debris flow events in 2012–2018, and daily data of the 10 days before the event day; hourly data of GUXIANG-2 and TIANMO stations on the day and the adjacent days of debris flow events in 2020, and daily data of the 7 days before the event day | Data from 2012 to 2018 collected from the Bomi Geologic Hazard Observation Station of the Institute of Mountain Hazards and Environment, CAS; data of 2020 sourced from the field observation |
Temperature | Daily maximum, minimum, and average data of Bomi station in 2012–2020 and Tianmo station in 2020 on the event day and the 15 days before the event day | Data of the Bomi station collected from the China Meteorological Data Service Centre; data of the Tianmo station sourced from the field observation |
Landform | DEM of the Parlong Zangbo Basin | SRTM 90 m DEM and ASTER 30 m GDEMv2 |
Geology | National 1:2.5 million geological map of China | National Geological Archives Data Center, China |
Vegetation Cover | NDVI of the study area in typical months from 2012 to 2020 | MODIS 500 m monthly synthetic product |
Snow Cover | Snow cover products from 2012 to 2015 | Maximum_Snow_Extent MOD_Grid_Snow_500 m products of MOD10A2 |
No. |
Precipitation Station | Year | Date |
Event No. * | D (h) |
I
a (mm/h) |
I
m
(mm/h) |
A
3 (mm) |
A
5 (mm) |
A
10 (mm) |
---|---|---|---|---|---|---|---|---|---|---|
1 | BOMI | 2012 | 09-21 | 1 | 2 | 0.8 | 1.0 | 10.0 | 14.2 | 21.3 |
2 | 2012 | 09-22 | 1 | 1 | 1.0 | 1.0 | 5.8 | 9.4 | 16.2 | |
3 | 2012 | 09-22 | 2 | 3 | 2.0 | 5.0 | 5.8 | 9.4 | 16.2 | |
4 | 2012 | 09-22 | 3 | 13 | 1.4 | 4.0 | 5.8 | 9.4 | 16.2 | |
5 | 2013 | 07-05 | 1 | 2 | 0.8 | 1.0 | 0.1 | 0.3 | 2.8 | |
6 | 2013 | 07-05 | 2 | 9 | 2.1 | 4.5 | 0.1 | 0.3 | 2.8 | |
7 | 2014 | 07-23 | 1 | 4 | 1.9 | 4.5 | 2.9 | 3.7 | 6.9 | |
8 | 2014 | 07-24 | 1 | 1 | 1.5 | 1.5 | 5.7 | 7.2 | 10.7 | |
9 | 2014 | 07-24 | 2 | 4 | 1.0 | 2.0 | 5.7 | 7.2 | 10.7 | |
10 | 2014 | 07-24 | 3 | 4 | 1.8 | 3.5 | 5.7 | 7.2 | 10.7 | |
11 | 2014 | 07-25 | 1 | 3 | 0.8 | 1.5 | 6.5 | 8.8 | 12.6 | |
12 | 2014 | 08-18 | 1 | 1 | 1.0 | 1.0 | 4.4 | 8.5 | 14.7 | |
13 | 2014 | 08-22 | 1 | 3 | 0.8 | 1.0 | 4.5 | 5.8 | 10.4 | |
14 | 2014 | 08-22 | 2 | 1 | 1.0 | 1.0 | 4.5 | 5.8 | 10.4 | |
15 | 2014 | 08-22 | 3 | 3 | 0.7 | 1.0 | 4.5 | 5.8 | 10.4 | |
16 | 2014 | 08-22 | 4 | 2 | 1.3 | 1.5 | 4.5 | 5.8 | 10.4 | |
17 | 2014 | 08-22 | 5 | 2 | 1.8 | 2.0 | 4.5 | 5.8 | 10.4 | |
18 | 2014 | 08-23 | 1 | 14 | 1.0 | 3.5 | 7.3 | 9.4 | 14.1 | |
19 | SONGZONG | 2012 | 09-21 | 1 | 3 | 1.2 | 1.5 | 10.8 | 15.5 | 22.5 |
20 | 2012 | 09-21 | 2 | 4 | 1.9 | 2.5 | 10.8 | 15.5 | 22.5 | |
21 | 2012 | 09-22 | 1 | 11 | 2.9 | 7.0 | 12.5 | 17.7 | 25.8 | |
22 | 2012 | 09-23 | 1 | 10 | 1.6 | 2.5 | 28.4 | 36.7 | 49.0 | |
23 | 2013 | 07-05 | 1 | 5 | 1.3 | 3.0 | 0.8 | 0.9 | 2.8 | |
24 | 2013 | 07-05 | 2 | 1 | 1.5 | 1.5 | 0.8 | 0.9 | 2.8 | |
25 | 2013 | 07-06 | 1 | 4 | 1.8 | 3.0 | 6.4 | 7.7 | 10.3 | |
26 | 2014 | 07-23 | 1 | 1 | 1.5 | 1.5 | 0.4 | 0.6 | 2.4 | |
27 | 2014 | 07-24 | 1 | 3 | 1.0 | 1.5 | 0.9 | 1.2 | 2.7 | |
28 | 2014 | 07-24 | 2 | 2 | 3.0 | 2.5 | 0.9 | 1.2 | 2.7 | |
29 | 2014 | 08-18 | 1 | 2 | 5.0 | 2.5 | 2.7 | 6.4 | 11.7 | |
30 | 2014 | 08-22 | 1 | 4 | 0.6 | 2.5 | 3.9 | 5.3 | 9.4 | |
31 | 2014 | 08-23 | 1 | 9 | 2.4 | 5.0 | 8.6 | 11.5 | 16.2 | |
32 | 2015 | 08-03 | 1 | 2 | 4.0 | 2.5 | 0.0 | 0.0 | 0.2 | |
33 | 2015 | 08-04 | 1 | 3 | 3.2 | 6.0 | 6.5 | 7.8 | 9.1 | |
34 | 2015 | 08-04 | 2 | 2 | 3.8 | 5.0 | 6.5 | 7.8 | 9.1 | |
35 | 2015 | 08-17 | 1 | 2 | 1.0 | 1.0 | 1.0 | 1.3 | 2.1 | |
36 | 2015 | 08-17 | 2 | 3 | 1.2 | 1.5 | 1.0 | 1.3 | 2.1 | |
37 | 2015 | 08-17 | 3 | 4 | 0.6 | 1.0 | 1.0 | 1.3 | 2.1 | |
38 | 2015 | 08-18 | 1 | 1 | 1.0 | 1.0 | 7.0 | 8.6 | 10.3 | |
39 | 2015 | 08-19 | 1 | 9 | 1.1 | 2.0 | 11.4 | 14.7 | 18.3 | |
40 | 2015 | 08-19 | 2 | 10 | 2.5 | 6.0 | 11.4 | 14.7 | 18.3 | |
41 | MIDUI | 2012 | 09-22 | 1 | 17 | 1.3 | 3.0 | 3.9 | 5.9 | 10.3 |
42 | 2012 | 09-22 | 2 | 13 | 1.2 | 3.0 | 3.9 | 5.9 | 10.3 | |
43 | 2012 | 09-23 | 1 | 1 | 1.5 | 1.5 | 15.6 | 19.9 | 26.3 | |
44 | 2012 | 09-23 | 2 | 9 | 1.5 | 5.5 | 15.6 | 19.9 | 26.3 | |
45 | 2013 | 07-04 | 1 | 4 | 1.8 | 2.5 | 0.0 | 0.0 | 1.4 | |
46 | 2013 | 07-05 | 1 | 2 | 1.5 | 1.5 | 2.8 | 3.3 | 4.8 | |
47 | 2014 | 07-23 | 1 | 1 | 1.0 | 1.0 | 1.6 | 2.3 | 3.7 | |
48 | 2014 | 07-23 | 2 | 2 | 1.0 | 1.0 | 1.6 | 2.3 | 3.7 | |
49 | 2014 | 07-23 | 3 | 2 | 1.3 | 1.5 | 1.6 | 2.3 | 3.7 | |
50 | 2014 | 07-24 | 1 | 6 | 2.1 | 3.5 | 3.4 | 4.7 | 6.4 | |
51 | 2014 | 08-22 | 1 | 4 | 0.8 | 1.0 | 0.9 | 2.1 | 5.4 | |
52 | 2014 | 08-23 | 1 | 9 | 1.7 | 2.5 | 3.1 | 4.5 | 7.3 | |
53 | 2014 | 08-24 | 1 | 8 | 1.6 | 3.5 | 8.6 | 11.2 | 15.1 |
Year | Date | C v 5 | C v 10 | C v 15 | T 5 | T 10 | T 15 | T a |
---|---|---|---|---|---|---|---|---|
2012 | 09-22 | 0.29 | 0.37 | 0.37 | 14.1 | 13.7 | 14.4 | 43.3 |
2012 | 09-21 | 0.30 | 0.38 | 0.38 | 13.8 | 13.8 | 14.6 | 44.0 |
2012 | 09-23 | 0.40 | 0.44 | 0.41 | 13.9 | 13.4 | 14.1 | 39.6 |
2013 | 07-06 | 0.11 | 0.26 | 0.29 | 18.7 | 17.5 | 17.5 | 56.8 |
2013 | 07-04 | 0.14 | 0.33 | 0.30 | 17.9 | 16.8 | 17.5 | 55.8 |
2013 | 07-05 | 0.11 | 0.29 | 0.30 | 18.5 | 17.2 | 17.6 | 56.4 |
2013 | 07-31 | 0.26 | 0.30 | 0.33 | 18.5 | 18.0 | 17.8 | 54.3 |
2014 | 07-23 | 0.23 | 0.34 | 0.29 | 18.1 | 17.2 | 17.6 | 56.6 |
2014 | 07-25 | 0.23 | 0.24 | 0.30 | 18.2 | 17.2 | 17.4 | 53.6 |
2014 | 07-24 | 0.20 | 0.36 | 0.30 | 18.4 | 17.1 | 17.5 | 55.1 |
2014 | 08-18 | 0.55 | 0.39 | 0.32 | 14.6 | 16.3 | 17.2 | 42.0 |
2014 | 08-22 | 0.26 | 0.42 | 0.35 | 16.3 | 15.6 | 16.5 | 48.9 |
2014 | 08-23 | 0.30 | 0.41 | 0.35 | 16.0 | 15.3 | 16.2 | 45.8 |
2014 | 08-24 | 0.35 | 0.42 | 0.36 | 15.6 | 15.0 | 16.0 | 44.2 |
2015 | 08-04 | 0.15 | 0.19 | 0.25 | 17.3 | 17.2 | 16.6 | 54.6 |
2015 | 08-03 | 0.14 | 0.19 | 0.25 | 17.0 | 16.8 | 16.5 | 53.4 |
2015 | 08-17 | 0.31 | 0.37 | 0.31 | 18.8 | 17.9 | 17.9 | 54.2 |
2015 | 08-18 | 0.46 | 0.41 | 0.38 | 18.0 | 18.0 | 17.7 | 50.6 |
2015 | 08-19 | 0.46 | 0.49 | 0.43 | 17.0 | 17.9 | 17.6 | 47.8 |
2015 | 08-20 | 0.38 | 0.53 | 0.48 | 15.9 | 17.5 | 17.3 | 44.4 |
Gully | Hd (m) | Sa (°) | Gg (°) | As | P (%) | F | L |
---|---|---|---|---|---|---|---|
Zhuonong | 1832.62 | 32 | 11.4 | East–Northwest | −65.5 | 0.33 | Extremely hard |
West Jiazong | 1858.96 | 37 | 34.0 | South–Southwest | −12.6 | 0.00 | Extremely hard |
Shalong | 1508.94 | 32 | 13.4 | Southwest–West | −3.8 | 0.31 | Secondary hard |
Rongqian | 1993.82 | 32 | 29.4 | South–Southwest | −4.9 | 0.10 | Extremely hard |
Motuo | 1510.14 | 27 | 28.6 | East–Southeast | −2.2 | 0.25 | Extremely hard |
Midui | 1262.14 | 34 | 31.8 | East–Northeast | 4.2 | 0.33 | Secondary hard |
Jiurong | 1751.91 | 36 | 23.5 | West–Northwest | −28.0 | 0.21 | Secondary hard |
East Lapu | 1563.89 | 36 | 30.1 | South–Southwest | 3.1 | 0.10 | Extremely hard |
Gully | NDVI | ||||||||
---|---|---|---|---|---|---|---|---|---|
Training/Validation Phase | Testing Phase | ||||||||
2012-08 | 2013-06 | 2014-07 | 2015-07 | 2016-08 | 2017-07 | 2018-04 | 2018-06 | 2020-07 | |
Tianmo | 0.12 | 0.18 | 0.20 | 0.70 | 0.40 | 0.51 | |||
Chidan | 0.23 | 0.37 | 0.31 | 0.75 | 0.69 | ||||
Jiaolong | 0.12 | 0.16 | 0.17 | 0.51 | 0.37 | ||||
Chaobu | 0.13 | 0.20 | 0.52 | 0.83 | 0.64 | ||||
Bitong | 0.12 | 0.18 | 0.20 | 0.51 | 0.44 | 0.29 | 0.39 | ||
Dada | 0.16 | 0.19 | 0.43 | 0.61 | 0.39 | ||||
Guxiang | 0.12 | 0.17 | 0.19 | 0.42 | 0.30 | 0.28 | |||
Zhuonong | 0.24 | 0.48 | 0.31 | 0.69 | |||||
West Jiazong | 0.16 | 0.42 | 0.28 | 0.43 | |||||
Shalong | 0.18 | 0.27 | 0.23 | 0.38 | |||||
Rongqian | 0.24 | 0.34 | 0.22 | 0.51 | |||||
Motuo | 0.29 | 0.47 | 0.38 | 0.62 | |||||
Midui | 0.06 | 0.11 | 0.08 | 0.16 | |||||
Jiurong | 0.34 | 0.41 | 0.42 | 0.57 | 0.22 | ||||
East Lapu | 0.26 | 0.30 | 0.23 | 0.32 | 0.04 |
Gully | ASCd | MSCa | |||
---|---|---|---|---|---|
2012 | 2013 | 2014 | 2015 | ||
Zhuonong | 89.2 | 93.6 | 86.0 | 93.0 | 86.5 |
West Jiazong | 84.3 | 91.6 | 100.0 | 80.0 | 98.5 |
Shalong | 43.8 | 100.0 | 80.1 | 94.5 | 93.1 |
Rongqian | 93.7 | 100.0 | 83.9 | 79.5 | 77.5 |
Motuo | 90.9 | 100.0 | 86.3 | 100.0 | 100.0 |
Midui | 80.0 | 90.0 | 80.0 | 100.0 | 100.0 |
Jiurong | 100.0 | 100.0 | 82.3 | 100.0 | 92.4 |
East Lapu | 91.4 | 91.4 | 100.0 | 96.0 | 90.9 |
Rank | Correlation Analysis | SVM-RFE | GainRatio | |||
---|---|---|---|---|---|---|
Coefficient | Indicator | Average Merit | Indicator | Score | Indicator | |
1 | 0.42 ** | NDVIt | 23 ± 2.7 | D | 0.47 | Cv10 |
2 | −0.36 ** | Tr | 22.5 ± 3.0 | T10 | 0.47 | Cv15 |
3 | 0.34 ** | D | 21.4 ± 2.6 | A5 | 0.26 | Cv5 |
4 | 0.30 ** | Cv15 | 21.2 ± 1.5 | T15 | 0.24 | A3 |
5 | 0.27 ** | T10 | 20.3 ± 4.1 | Cv15 | 0.24 | Im |
6 | 0.26 ** | Im | 20.3 ± 1.1 | A10 | 0.16 | NDVIt |
7 | 0.25 ** | A3 | 19.8 ± 1.9 | Im | 0.11 | T10 |
8 | 0.24 ** | Cv5 | 19.3 ± 2.1 | Cv10 | 0.11 | T15 |
9 | 0.24 ** | Cv10 | 18.4 ± 1.4 | NDVIt | 0.11 | D |
10 | 0.23 ** | A5 | 16.9 ± 5.2 | Ia | 0.06 | Ia |
11 | 0.22 ** | Ia | 15.1 ± 2.7 | A3 | 0 | A10 |
12 | 0.21 ** | T15 | 14.1 ± 0.3 | Sa | 0 | T5 |
13 | 0.19 * | A10 | 12.4 ± 1.0 | Hd | 0 | A5 |
14 | 0.11 | Sa | 8.8 ± 3.2 | ASCd | 0 | MSCa |
15 | 0.08 | T5 | 8.8 ± 4.3 | Cv5 | 0 | ASCd |
16 | 0.06 | ASCd | 8.3 ± 2.8 | T5 | 0 | F |
17 | −0.06 | L | 8.1 ± 4.7 | MSCa | 0 | P |
18 | 0.039 | A | 6.8 ± 3.6 | Gg | 0 | As |
19 | 0.03 | As | 6.7 ± 1.9 | As | 0 | NDVIs |
20 | −0.03 | F | 6.4 ± 2.65 | P | 0 | Gg |
21 | −0.02 | Ta | 6.3 ± 3.55 | NDVIs | 0 | A |
22 | −0.02 | Hd | 6.2 ± 3.25 | Ta | 0 | Hd |
23 | 0.02 | P | 5.7 ± 3.44 | L | 0 | L |
24 | 0.01 | MSCa | 4.1 ± 2.74 | F | 0 | Sa |
25 | 0.01 | Gg | 4.1 ± 3.05 | A | 0 | Ta |
26 | 0.01 | NDVIs |
True Class | Predicted Class | |
---|---|---|
+ | − | |
+ | TP | FN |
– | FP | TN |
Phase | Model | Class | TPR | FPR | Precision | F-Measure | MCC | AUC |
---|---|---|---|---|---|---|---|---|
Training and Validation | RF | No | 0.966 | 0.013 | 0.986 | 0.976 | 0.953 | 0.997 |
Yes | 0.987 | 0.034 | 0.967 | 0.977 | ||||
WA | 0.977 | 0.023 | 0.977 | 0.977 | ||||
SVM | No | 0.886 | 0.034 | 0.964 | 0.923 | 0.855 | 0.926 | |
Yes | 0.966 | 0.114 | 0.894 | 0.929 | ||||
WA | 0.926 | 0.074 | 0.929 | 0.926 | ||||
LSVM | No | 0.765 | 0.342 | 0.691 | 0.726 | 0.425 | 0.711 | |
Yes | 0.658 | 0.235 | 0.737 | 0.695 | ||||
WA | 0.711 | 0.289 | 0.714 | 0.711 | ||||
Testing | RF | No | 0.750 | 0.222 | 0.750 | 0.750 | 0.528 | 0.778 |
Yes | 0.778 | 0.250 | 0.778 | 0.778 | ||||
WA | 0.765 | 0.237 | 0.765 | 0.765 | ||||
SVM | No | 0.500 | 0.222 | 0.667 | 0.571 | 0.290 | 0.639 | |
Yes | 0.778 | 0.500 | 0.636 | 0.700 | ||||
WA | 0.647 | 0.369 | 0.651 | 0.639 | ||||
LSVM | No | 0.375 | 0.111 | 0.750 | 0.500 | 0.311 | 0.632 | |
Yes | 0.889 | 0.625 | 0.615 | 0.727 | ||||
WA | 0.647 | 0.383 | 0.679 | 0.620 |
Phase | Model | RF | SVM | LSVM | |||
---|---|---|---|---|---|---|---|
Class | No | Yes | No | Yes | No | Yes | |
Training and Validation | No | 144 | 5 | 132 | 17 | 114 | 35 |
Yes | 2 | 147 | 5 | 144 | 51 | 98 | |
Testing | No | 6 | 2 | 4 | 4 | 3 | 5 |
Yes | 2 | 7 | 2 | 7 | 1 | 8 |
No. | Gully | Date | Station | A 3 | D | I m | NDVI t | S a | C v 15 | T 10 | Debris Flow | RF | SVM |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Chidan | 2016-09-05 | PEILONG | 21.75 | 15.00 | 4.70 | 0.69 | 30.70 | 0.43 | 16.80 | Yes | √ | √ |
2 | Jiaolong | 2016-09-05 | PEILONG | 21.75 | 15.00 | 4.70 | 0.37 | 36.20 | 0.43 | 16.80 | Yes | √ | √ |
3 | Bitong | 2016-09-05 | PEILONG | 21.75 | 15.00 | 4.70 | 0.44 | 34.50 | 0.43 | 16.80 | Yes | √ | √ |
4 | Chaobu | 2017-08-03 | GUXIANG | 3.10 | 1.00 | 0.20 | 0.64 | 35.00 | 0.17 | 18.30 | Yes | √ | × |
5 | Chaobu | 2017-08-02 | GUXIANG | 0.09 | 6.00 | 1.60 | 0.64 | 35.00 | 0.17 | 18.40 | No | √ | √ |
6 | Guxiang | 2017-08-03 | GUXIANG | 3.10 | 1.00 | 0.20 | 0.30 | 35.60 | 0.17 | 18.30 | No | √ | √ |
7 | East Lapu | 2018-05-22 | SONGZONG | 0.00 | 0.00 | 0.00 | 0.04 | 36.00 | 0.21 | 14.00 | Yes | × | × |
8 | East Lapu | 2018-05-21 | SONGZONG | 0.00 | 0.00 | 0.00 | 0.01 | 36.00 | 0.21 | 13.10 | No | √ | √ |
9 | Jiurong | 2018-05-22 | SONGZONG | 0.00 | 0.00 | 0.00 | 0.22 | 36.00 | 0.21 | 14.00 | No | √ | √ |
10 | Tianmo | 2018-07-11 | GUXIANG | 12.94 | 10.00 | 3.10 | 0.40 | 37.70 | 0.24 | 18.20 | Yes | √ | √ |
11 | Bitong | 2018-07-11 | GUXIANG | 12.94 | 10.00 | 3.10 | 0.29 | 34.50 | 0.24 | 18.20 | Yes | √ | √ |
12 | Guxiang | 2018-07-11 | GUXIANG | 12.94 | 10.00 | 3.10 | 0.28 | 35.60 | 0.24 | 18.20 | No | × | × |
13 | Dada | 2020-07-09 | TIANMO | 0.65 | 2.35 | 6.00 | 0.39 | 33.00 | 0.37 | 16.26 | No | √ | × |
14 | Dada | 2020-07-10 | TIANMO | 6.50 | 3.00 | 7.60 | 0.39 | 33.00 | 0.35 | 16.19 | Yes | √ | √ |
15 | Tianmo | 2020-07-10 | TIANMO | 6.50 | 3.00 | 7.60 | 0.51 | 37.70 | 0.35 | 16.19 | No | × | × |
16 | Guxiang | 2020-07-08 | GUXIANG-2 | 0.20 | 0.00 | 0.00 | 0.39 | 35.60 | 0.37 | 15.98 | No | √ | × |
17 | Guxiang | 2020-07-09 | GUXIANG-2 | 0.35 | 3.80 | 2.33 | 0.39 | 35.60 | 0.37 | 16.26 | Yes | × | √ |
Dependent Variable | Independent Variable | Standardized Coefficients | t-Value | p-Value | F-Value | Adjusted R2 |
---|---|---|---|---|---|---|
RF outputs | A3 | 0.790 | 4.986 | 0.000 | 24.862 | 0.599 |
SVM outputs | Cv15 | 0.818 | 5.891 | 0.000 | 19.167 | 0.694 |
T10 | 0.338 | 2.433 | 0.029 |
Phase | Model | Class | TPR | FPR | MCC | ROC |
---|---|---|---|---|---|---|
Training and Validation | Original | No | 0.966 | 0.013 | 0.953 | 0.997 |
Yes | 0.987 | 0.034 | ||||
WA | 0.977 | 0.023 | ||||
Without A3 | No | 0.946 | 0.020 | 0.927 | 0.995 | |
Yes | 0.980 | 0.054 | ||||
WA | 0.963 | 0.037 | ||||
Non-resample | No | 0.980 | 0.375 | 0.664 | 0.947 | |
Yes | 0.625 | 0.020 | ||||
WA | 0.945 | 0.341 | ||||
SMOTE | No | 0.966 | 0.028 | 0.939 | 0.995 | |
Yes | 0.972 | 0.034 | ||||
WA | 0.969 | 0.031 | ||||
Testing | Original | No | 0.750 | 0.222 | 0.528 | 0.778 |
Yes | 0.778 | 0.250 | ||||
WA | 0.765 | 0.237 | ||||
Without A3 | No | 0.500 | 0.333 | 0.169 | 0.736 | |
Yes | 0.667 | 0.500 | ||||
WA | 0.588 | 0.422 | ||||
Non-resample | No | 0.750 | 0.667 | 0.091 | 0.729 | |
Yes | 0.333 | 0.250 | ||||
WA | 0.529 | 0.446 | ||||
SMOTE | No | 0.750 | 0.556 | 0.203 | 0.750 | |
Yes | 0.444 | 0.250 | ||||
WA | 0.588 | 0.394 |
Model | Phase | Training & Validation | Testing | ||
---|---|---|---|---|---|
Class | No | Yes | No | Yes | |
Original | No | 144 | 5 | 6 | 2 |
Yes | 2 | 147 | 2 | 7 | |
Without A3 | No | 141 | 8 | 4 | 4 |
Yes | 3 | 146 | 3 | 6 | |
Non-resample | No | 146 | 3 | 6 | 2 |
Yes | 6 | 10 | 6 | 3 | |
SMOTE | No | 144 | 5 | 6 | 2 |
Yes | 4 | 140 | 5 | 4 |
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Du, J.; Zhang, H.-y.; Hu, K.-h.; Wang, L.; Dong, L.-y. Prediction of the Periglacial Debris Flow in Southeast Tibet Based on Imbalanced Small Sample Data. Water 2023, 15, 310. https://doi.org/10.3390/w15020310
Du J, Zhang H-y, Hu K-h, Wang L, Dong L-y. Prediction of the Periglacial Debris Flow in Southeast Tibet Based on Imbalanced Small Sample Data. Water. 2023; 15(2):310. https://doi.org/10.3390/w15020310
Chicago/Turabian StyleDu, Jun, Hong-ya Zhang, Kai-heng Hu, Lin Wang, and Lin-yao Dong. 2023. "Prediction of the Periglacial Debris Flow in Southeast Tibet Based on Imbalanced Small Sample Data" Water 15, no. 2: 310. https://doi.org/10.3390/w15020310