Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms
Abstract
:1. Introduction
2. Study Area
3. Data and Information
4. Methods
- At the first stage, to find the correlation of the landslides with the causative factors and calculate the initial weights [9,100], the bivariate statistical method of FR is applied. In this method, the weight (Fri) of each class (i = 1, 2, 3, …, n) of a factor is equal to the percentage of its landslides divided by the percentage of its area as a ratio of the whole map;
- At the second stage, the Fris (the sixth column of Table 2) should be normalized in the standard interval of (0.1, 0.9) that results in the µi values (the last column of Table 2) as follows [101,102]:
- At the third stage, the unique condition units of the study area are created by overlaying all the factors with the µi values in GIS software (e.g., using the Combine tool in ArcGIS® 10);
- The last stage involves creating a calibration dataset which is comprised of the µi values of both landslide and stable pixels extracted from the unique condition units. Both landslide and stable pixels are necessary for training some of the data mining models [57,81,93,103,104], such as BLR, BPANN, SVM, and C5DT (except for FG which was applied directly using the unique condition raster). The calibration dataset consisted of 80% landslide pixels (344 pixels in the modeling dataset) and 344 randomly selected stable pixels. A buffer distance of 100 m around the landslides was considered when randomly picking out the stable pixels to provide relative assurance of the insensitivity of these stable pixels, which in turn helped to increase the accuracy of the models. A low-volume text file (such as DBF, database file) of the calibration dataset was used in the training process of the machine learning models in statistical software (SPSS® Statistics 19 and SPSS® Modeler 18 in this study).
4.1. FG
4.2. BLR
4.3. BPANN
4.4. SVM
4.5. C5DT
4.6. Validation of the Built Models
5. Results and Discussion
5.1. The Relationship between the Landslides and Causative Factors
5.2. Application of the NFUC Method
5.3. Application of Different Data Mining Methods
5.4. Validation of the Data Mining Models
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Age | Code | Formation Name | Lithology |
---|---|---|---|
Cenozoic | E1m | _ | Marl, gypsiferous marl, and limestone |
Ekh | Khangiran | Olive-green shale and sandstone | |
Murm | _ | Light red to brown marl and gypsiferous marl with sandstone intercalations | |
Murmg | _ | Gypsiferous marl | |
Plc | _ | Polymictic conglomerate and sandstone | |
PlQc | _ | Fluvial conglomerate, piedmont conglomerate, and sandstone | |
Qal | _ | Stream channel, braided channel, and floodplain deposits | |
Qft1 | _ | High-level piedmont fan and valley terrace deposits | |
Qft2 | _ | Low-level pediment fan and valley terrace deposits | |
Qm | _ | Swamp and marsh | |
Qsd | _ | Unconsolidated wind-blown sand deposit including sand dunes | |
Qsw | _ | Swamp | |
Mesozoic | Jch | Chaman bid | Dark gray argillaceous limestone and marl |
Jd | Dalichai | Well-bedded to thin-bedded, greenish-gray argillaceous limestone with intercalations of calcareous shale | |
Jl | Lar | Light gray, thin bedded to massive limestone | |
Jmz | Mozduran | Grey thick-bedded limestone and dolomite | |
Jsc | _ | Conglomerate | |
K | _ | Cretaceous rocks in general, include limestone, marly limestone, Inoceramus bearing | |
Kad-ab | _ | Undifferentiated unit including argillaceous limestone, marl, and shale | |
Kat | Aitamir | Olive green glauconitic sandstone and shale | |
Kl | _ | Lower cretaceous undifferentiated rocks (Argillite, limestone, massive dolomite, sandstone) | |
Ksn | Sanganeh | Grey to black shale and thin layers of siltstone and sandstone | |
Ksr | Sarcheshmeh | Ammonite bearing shale with interaction of orbitolina limestone | |
Ktr | Tirgan | Grey oolitic and bioclastic orbitolina limestone | |
Ku | _ | Upper cretaceous, undifferentiated rocks | |
TRe | Elikah | Thick bedded gray oolitic limestone; thin-platy, yellow to pinkish shale-limestone with worm tracks and well to thick-bedded dolomite and dolomitic limestone | |
TRe2 | _ | Thick bedded dolomite | |
TRJs | Shemshak | Dark-gray shale and sandstone | |
Paleozoic | Cl | Lalun | Dark red medium-grained arkosic to sub arkosic sandstone and micaceous siltstone |
Cm | Mobarak | Dark gray to black fossiliferous limestone with subordinate black shale | |
DCkh | _ | Yellowish, thin to thick-bedded, fossiliferous argillaceous limestone, dark gray limestone, greenish marl, and shale, locally including gypsum | |
Dp | Pabdeh | Light red to white, thick bedded quartzarenite with dolomite intercalations and gypsum | |
P | _ | Undifferentiated Permian rocks | |
Pd | Dorud | Red sandstone and shale with subordinate sandy limestone | |
Pr | Ruteh | Dark-gray medium-bedded to massive limestone | |
Pz | _ | Undifferentiated lower Paleozoic rocks | |
Pz1a.bv | _ | Andesitic basaltic volcanic | |
Sn | Niur | Greenish gray, shale, sandstone, sandy lime, coral limestone, and dolomite | |
Proterozoic | PCC | _ | Late Proterozoic to early Cambrian undifferentiated rocks |
PCmt2 | Greenschist facies | Low-grade, regional metamorphic rocks |
Factor | Class | Class Area % | No. of Landslide | Landslide % | Fri | µi |
---|---|---|---|---|---|---|
Elevation (m above m.s.l.) | <100 | 32.29 | 6 | 1.74 | 0.050 | 0.118 |
100–300 | 12.16 | 63 | 18.31 | 1.500 | 0.648 | |
300–600 | 13.01 | 98 | 28.49 | 2.190 | 0.900 | |
600–1000 | 11.82 | 89 | 25.87 | 2.190 | 0.900 | |
1000–1300 | 9.33 | 49 | 14.24 | 1.520 | 0.655 | |
1300–1700 | 10.34 | 32 | 9.30 | 0.900 | 0.429 | |
1700–2500 | 9.68 | 7 | 2.03 | 0.210 | 0.177 | |
2500< | 1.33 | 0 | 0.00 | 0.000 | 0.100 | |
Slope degree | 0–6 | 40.83 | 89 | 25.87 | 0.630 | 0.226 |
6-12 | 22.55 | 76 | 22.09 | 0.980 | 0.457 | |
12–18 | 15.40 | 76 | 22.09 | 1.430 | 0.755 | |
18–24 | 10.35 | 59 | 17.15 | 1.650 | 0.900 | |
24–30 | 6.17 | 27 | 7.85 | 1.270 | 0.649 | |
30–40 | 4.03 | 16 | 4.65 | 1.150 | 0.569 | |
40< | 0.65 | 1 | 0.29 | 0.440 | 0.100 | |
Slope aspect | Flat | 0.23 | 0 | 0.00 | 0.000 | 0.100 |
North | 17.42 | 60 | 17.44 | 1.000 | 0.740 | |
Northeast | 12.00 | 32 | 9.30 | 0.770 | 0.593 | |
East | 8.61 | 33 | 9.59 | 1.110 | 0.810 | |
Southeast | 10.51 | 29 | 8.43 | 0.800 | 0.612 | |
South | 13.32 | 52 | 15.11 | 1.130 | 0.823 | |
Southwest | 11.18 | 48 | 13.95 | 1.250 | 0.900 | |
West | 10.59 | 38 | 11.04 | 1.040 | 0.766 | |
Northwest | 16.13 | 52 | 15.11 | 0.930 | 0.695 | |
STI-V | 0–10 | 59.43 | 160 | 46.51 | 0.780 | 0.108 |
10–20 | 17.07 | 68 | 19.76 | 1.150 | 0.185 | |
20–30 | 7.41 | 19 | 5.52 | 0.740 | 0.100 | |
30–40 | 4.35 | 21 | 6.10 | 1.400 | 0.236 | |
40–50 | 3.06 | 10 | 2.90 | 0.950 | 0.143 | |
50–60 | 2.41 | 10 | 2.90 | 1.200 | 0.195 | |
60–70 | 2.10 | 8 | 2.32 | 1.100 | 0.174 | |
70–80 | 2.01 | 14 | 4.06 | 2.020 | 0.365 | |
80–90 | 2.14 | 34 | 9.88 | 4.610 | 0.900 | |
SPI | <1 | 15.24 | 22 | 6.39 | 0.420 | 0.100 |
1–2 | 19.29 | 49 | 14.24 | 0.740 | 0.388 | |
2–3 | 21.29 | 90 | 26.16 | 1.230 | 0.828 | |
3–5 | 30.43 | 121 | 35.17 | 1.150 | 0.756 | |
5< | 13.73 | 62 | 18.02 | 1.310 | 0.900 | |
Lithology (Code) | E1m | 0.35 | 0 | 0.00 | 0.000 | 0.100 |
Ekh | 0.22 | 0 | 0.00 | 0.000 | 0.100 | |
Murm | 0.70 | 3 | 0.87 | 1.250 | 0.239 | |
Murmg | 0.00 | 0 | 0.00 | 0.000 | 0.100 | |
Plc | 0.37 | 0 | 0.00 | 0.000 | 0.100 | |
PlQc | 0.50 | 0 | 0.00 | 0.000 | 0.100 | |
Qal | 0.21 | 0 | 0.00 | 0.000 | 0.100 | |
Qft1 | 0.89 | 0 | 0.00 | 0.000 | 0.100 | |
Qft2 | 0.87 | 6 | 1.74 | 1.990 | 0.321 | |
Qm | 40.80 | 14 | 4.07 | 0.100 | 0.111 | |
Qsd | 3.44 | 31 | 9.01 | 2.620 | 0.391 | |
Qsw | 12.35 | 100 | 29.06 | 2.350 | 0.361 | |
Jch | 0.51 | 6 | 1.74 | 3.400 | 0.477 | |
Jd | 3.52 | 10 | 2.90 | 0.820 | 0.191 | |
Jl | 5.19 | 20 | 5.81 | 1.120 | 0.224 | |
Jmz | 2.17 | 6 | 1.74 | 0.800 | 0.189 | |
Jsc | 0.09 | 0 | 0.00 | 0.000 | 0.100 | |
K | 0.08 | 2 | 0.58 | 7.210 | 0.900 | |
Kad-ab | 0.09 | 0 | 0.00 | 0.000 | 0.100 | |
Kat | 0.99 | 0 | 0.00 | 0.000 | 0.100 | |
Kl | 0.14 | 3 | 0.87 | 6.170 | 0.785 | |
Ksn | 1.51 | 1 | 0.29 | 0.190 | 0.121 | |
Ksr | 1.08 | 1 | 0.29 | 0.260 | 0.129 | |
Ktr | 0.00 | 0 | 0.00 | 0.000 | 0.100 | |
Ku | 2.20 | 3 | 0.87 | 0.390 | 0.143 | |
TRe | 0.33 | 7 | 2.03 | 6.130 | 0.780 | |
TRe2 | 0.30 | 0 | 0.00 | 0.000 | 0.100 | |
TRJs | 4.12 | 24 | 6.97 | 1.690 | 0.288 | |
Cl | 0.13 | 1 | 0.29 | 2.130 | 0.336 | |
Cm | 3.89 | 35 | 10.17 | 2.610 | 0.390 | |
DCkh | 6.24 | 31 | 9.01 | 1.440 | 0.260 | |
Dp | 0.07 | 0 | 0.00 | 0.000 | 0.100 | |
P | 0.47 | 1 | 0.29 | 0.610 | 0.168 | |
Pd | 1.93 | 7 | 2.03 | 1.050 | 0.217 | |
Pr | 0.12 | 0 | 0.00 | 0.000 | 0.100 | |
Pz | 0.05 | 0 | 0.00 | 0.000 | 0.100 | |
Pz1a.bv | 0.68 | 2 | 0.58 | 0.850 | 0.194 | |
Sn | 0.04 | 0 | 0.00 | 0.000 | 0.100 | |
PCC | 0.01 | 0 | 0.00 | 0.000 | 0.100 | |
PCmt2 | 3.19 | 30 | 8.72 | 2.730 | 0.403 | |
Landcover (ID) | 1 | 12.90 | 2 | 0.58 | 0.040 | 0.107 |
2 | 3.12 | 52 | 15.11 | 4.840 | 0.900 | |
3 | 6.69 | 93 | 27.03 | 4.040 | 0.768 | |
4 | 8.45 | 9 | 2.61 | 0.300 | 0.150 | |
5 | 7.48 | 6 | 1.74 | 0.230 | 0.138 | |
6 | 5.16 | 3 | 0.87 | 0.170 | 0.128 | |
7 | 6.22 | 2 | 0.58 | 0.090 | 0.115 | |
8 | 2.62 | 4 | 1.16 | 0.440 | 0.173 | |
9 | 5.73 | 12 | 3.49 | 0.600 | 0.199 | |
10 | 24.16 | 147 | 42.73 | 1.770 | 0.393 | |
11 | 9.20 | 13 | 3.78 | 0.410 | 0.168 | |
12 | 3.74 | 0 | 0.00 | 0.000 | 0.100 | |
13 | 1.71 | 0 | 0.00 | 0.000 | 0.100 | |
14 | 1.52 | 1 | 0.29 | 0.190 | 0.131 | |
15 | 1.25 | 0 | 0.00 | 0.000 | 0.100 | |
Distance to roads (m) | 0–100 | 4.51 | 72 | 20.93 | 4.640 | 0.900 |
100–200 | 3.76 | 38 | 11.04 | 2.930 | 0.568 | |
200–300 | 4.06 | 30 | 8.72 | 2.140 | 0.415 | |
300–400 | 3.40 | 29 | 8.43 | 2.470 | 0.479 | |
400–500 | 3.58 | 31 | 9.01 | 2.510 | 0.486 | |
500< | 80.67 | 144 | 41.86 | 0.520 | 0.100 | |
Distance to rivers network (m) | 0–100 | 5.41 | 83 | 24.12 | 4.450 | 0.900 |
100–200 | 4.59 | 45 | 13.08 | 2.850 | 0.549 | |
200–400 | 9.38 | 37 | 10.75 | 1.140 | 0.175 | |
400–700 | 13.08 | 36 | 10.46 | 0.800 | 0.100 | |
700–1000 | 11.74 | 39 | 11.33 | 0.960 | 0.135 | |
1000–1500 | 16.85 | 54 | 15.69 | 0.930 | 0.128 | |
1500< | 38.93 | 50 | 14.53 | 0.370 | 0.100 | |
Distance to faults (m) | 0–200 | 9.30 | 61 | 17.73 | 1.900 | 0.900 |
200–400 | 7.85 | 43 | 12.50 | 1.590 | 0.738 | |
400–600 | 6.81 | 47 | 13.66 | 2.000 | 0.952 | |
600–1000 | 10.54 | 55 | 15.10 | 1.510 | 0.696 | |
1000< | 65.49 | 138 | 40.11 | 0.610 | 0.225 | |
Climate (type) | Very humid | 15.32 | 97 | 28.19 | 1.840 | 0.900 |
Humid | 19.95 | 83 | 24.12 | 1.210 | 0.626 | |
Semi humid | 13.37 | 25 | 7.26 | 0.540 | 0.335 | |
Mediterranean | 15.24 | 85 | 24.71 | 1.620 | 0.804 | |
Semiarid | 35.55 | 54 | 15.69 | 0.440 | 0.291 | |
Arid | 0.02 | 0 | 0.00 | 0.000 | 0.100 | |
Annual average rainfall (mm) | 150 | 0.01 | 0 | 0.00 | 0.000 | 0.100 |
200 | 0.15 | 0 | 0.00 | 0.000 | 0.100 | |
250 | 2.11 | 0 | 0.00 | 0.000 | 0.100 | |
300 | 9.73 | 1 | 0.29 | 0.030 | 0.109 | |
400 | 20.36 | 31 | 9.01 | 0.440 | 0.225 | |
500 | 19.40 | 99 | 28.78 | 1.480 | 0.521 | |
600 | 16.48 | 31 | 9.01 | 0.540 | 0.254 | |
700 | 13.03 | 41 | 11.92 | 0.910 | 0.359 | |
800 | 13.92 | 97 | 28.19 | 2.020 | 0.675 | |
900 | 4.13 | 40 | 11.62 | 2.810 | 0.900 | |
1000 | 0.64 | 4 | 1.16 | 1.820 | 0.618 | |
Annual average temperature (°C) | 4 | 0.34 | 0 | 0.00 | 0.000 | 0.100 |
6 | 1.42 | 2 | 0.58 | 0.400 | 0.269 | |
8 | 2.36 | 3 | 0.87 | 0.370 | 0.257 | |
10 | 8.88 | 23 | 6.68 | 0.750 | 0.417 | |
12 | 7.14 | 21 | 6.10 | 0.850 | 0.460 | |
14 | 34.92 | 227 | 65.99 | 1.890 | 0.900 | |
16 | 32.01 | 64 | 18.60 | 0.580 | 0.346 | |
18 | 12.90 | 4 | 1.16 | 0.090 | 0.138 |
γ Value | The Lowest Output Susceptibility | The Highest Output Susceptibility |
---|---|---|
0.5 | 0 | 0.294 |
0.6 | 0 | 0.376 |
0.7 | 0 | 0.480 |
0.8 | 0.003 | 0.613 |
0.9 | 0.056 | 0.873 |
0.95 | 0.218 | 0.885 |
0.975 | 0.429 | 0.940 |
BPANN Models | Training Accuracy (%) | Test Accuracy (%) | Overall Incorrect Predictions (%) |
---|---|---|---|
1 | 85.7 | 81.8 | 15.08 |
2 | 86.4 | 84.3 | 14.02 |
3 | 84.9 | 85.5 | 14.98 |
4 | 87.7 | 84.8 | 12.88 |
5 | 80.7 | 82.1 | 19.02 |
6 | 85.9 | 80.9 | 15.01 |
7 | 87.9 | 83.3 | 13.02 |
8 | 91.4 | 88.0 | 9.28 |
9 | 82.6 | 85.1 | 16.09 |
10 | 87.2 | 80.7 | 14.10 |
Susceptibility Zone | Zones Area % | Landslides % | ||||
---|---|---|---|---|---|---|
FG | BLR | C5DT | SVM | BPANN | ||
Very low | 20 | 1.62 | 0.93 | 0.70 | 0.93 | 1.39 |
Low | 20 | 1.39 | 1.39 | 1.86 | 1.86 | 0.93 |
Medium | 20 | 6.26 | 3.94 | 3.25 | 2.78 | 1.16 |
High | 20 | 14.85 | 11.60 | 11.37 | 13.46 | 9.51 |
Very high | 20 | 75.87 | 82.13 | 82.83 | 80.97 | 87.01 |
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Vakhshoori, V.; Pourghasemi, H.R.; Zare, M.; Blaschke, T. Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms. Water 2019, 11, 2292. https://doi.org/10.3390/w11112292
Vakhshoori V, Pourghasemi HR, Zare M, Blaschke T. Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms. Water. 2019; 11(11):2292. https://doi.org/10.3390/w11112292
Chicago/Turabian StyleVakhshoori, Vali, Hamid Reza Pourghasemi, Mohammad Zare, and Thomas Blaschke. 2019. "Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms" Water 11, no. 11: 2292. https://doi.org/10.3390/w11112292