# Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

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## Abstract

**:**

^{2}, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR–RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR–RF integrated model showed that the prediction accuracy of FR–RF–PALSAR (0.917) was higher than FR–RF–ASTER (0.865) and FR–RF–SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}and lies between longitudes of 53° 03′ 52″ E and 53° 31′ 39″ E and latitudes of 36° 11′ 48″ N and 36° 30′ 28″ N (Figure 1). The elevation averages 704 m, varying from 72 m in the northeast to 1681 m in the southwest and east of the study area. The mean annual temperature and rainfall are 12.8 °C and 900 mm, respectively [69]. The area is characterised by a mountainous landscape with an average slope gradient of 15.8° and a maximum of 67.8°. The normalised difference vegetation index (NDVI), which varies from −0.132 to 0.639 with a mean of 0.458, indicates a high vegetation density. Convex and concave plan curvature occur on 49.43% and 46.06% of the study area, respectively, whereas only 3.14% has flat topography. The site is characterised by seven land use/land cover (LU/LC) classes, including agricultural lands (0.08%), dense forest (78.9%), low forest (1.5%), agriculture-orchard (7.6%), agricultural forest (0.02%), dry farming forest (11.5%) and water (0.3%). The dominant lithological units in the area [70] are marl, calcareous sandstone, sandy limestone and minor conglomerate (Mm, s, l); hyporite-bearing limestone (K2l1); medium- to thick-bedded limestone (Pel); polymictic conglomerate and sandstone (Plc); and thick-bedded to massive limestone (K2l2).

#### 2.2. Data Collection (Landslide Inventory Map and Landslide Conditioning Factors [LCFs])

^{2}. Analysis of landslide size showed that the smallest was 135.63 m

^{2}and the largest was 976,423 m

^{2}, with a mean size of 108,215 m

^{2}. The models developed by [75,76] were used to classify all landslides using landslide points located in the centre of the landslide scar.

_{s}is specific upstream contributing area, and β is the slope gradient. The values of H, G, p, q, r, s and t are obtained as follows:

_{1}–z

_{9}are altitude values in 3 × 3 cellular networks and ${\Delta}_{s}$ denotes the cell size.

#### 2.3. Methodology

#### 2.4. Models

#### 2.4.1. FR

#### 2.4.2. RF

#### 2.4.3. Ensemble of FR and RF (FR–RF)

#### 2.5. Validation of models

## 3. Results

#### 3.1. Multicollinearity Test

#### 3.2. Application of FR Model

^{−1}). As expected, the lowest distance to stream class (<227 m) had a strong relationship with the triggering of landslides, whereas landslides were less frequent as the distance to streams increased. River erosion and saturation at the foot of slopes had a negative effect on the stability and increased the probability of landslide occurrence [102]. The FR analysis of distance to roads showed that areas close to roads had higher susceptibility to landslides. The results of lithology showed that Plc units had a strong relation with landslides and if water penetrates into them, then landslide is more likely to occur. The results of the rainfall parameter showed that the areas that receive the highest amount of rainfall (>1196 mm) with the highest value of FR (1.9542) had the highest potential for landslide occurrence. On the basis of the LU/LC results, a class of agriculture-orchard with FR = 5.6107 had a strong correlation with landslides. These results indicated that human activities could cause the instability of domains and increase landslide occurrence. On the basis of the NDVI factor, the sensitivity of the areas to landslide decreased with the increase in the value of the indicator, indicating the importance of vegetation in reducing mass movements. After determining the weights of the classes of parameters using the FR method, an LSM map was produced using Equation (5) in ArcGIS 10.5 with the weighted sum tool.

#### 3.3. Application of RF Model

#### 3.4. Application of FR–RF Integrated Model

#### 3.5. Model Validation

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 1.**Study area: (

**a**) Location of the study area in Iran; (

**b**) location of the study area in Mazandran Province; (

**c**) location of training and validation landslides in the study area.

**Figure 2.**

**Landslide conditioning factors**(LCFs): (

**a**) elevation; (

**b**) slope degree; (

**c**) slope aspect; (

**d**) convergence index; (

**e**) slope length (SL); (

**f**) plan curvature; (

**g**) profile curvature; (

**h**) drainage density; (

**i**) distance to stream; (

**j**) distance to road; (

**k**) distance to fault; (

**l**) lithology; (

**m**) rainfall; (

**n**) land use/land cover(LU/LC); (

**o**) normalized difference vegetation index (NDVI).

**Figure 4.**Classification of resulting map using FR, random forest (RF) and ensemble model: (

**a**) Equal interval; (

**b**) quantile; (

**c**) natural break; (

**d**) geometrical interval.

**Table 1.**Lithology of the study area [70].

Code | Description | Formation | Age |
---|---|---|---|

Qm | Swamp and shale | - | Quaternary |

Plc | Polymictic conglomerate and sandstone | - | Pliocene |

Mm, s, l | Marl, calcareous sandstone, sandy limestone and minor conglomerate | - | Miocene |

PeEm | Marl and gypsiferous marl locally gypsiferous mudstone | - | Paleocene–Eocene |

K2l2 | Thick-bedded to massive limestone | - | Late Cretaceous |

K2l1 | Hyporite-bearing limestone | - | Late Cretaceous |

DCkh | Yellowish, thin to thick-bedded, fossiliferous argillaceous limestone, dark grey limestone, greenish marl and shale, locally including gypsum | - | Devonian |

Pel | Medium to thick-bedded limestone | - | Paleocene–Eocene |

Jk | Conglomerate, sandstone and shale with plant remains and coal seams | Kashafrud | Middle Jurassic |

Jl | Light grey, thin-bedded to massive limestone | Lar | Jurassic–Cretaceous |

TRJs | Dark grey shale and sandstone | Shemshad | Triassic–Jurassic |

Ktzl | Thick-bedded to massive, white to pinkish orbitolina bearing limestone | Tizkuh | Early Cretaceous |

Factor | Source | Resolution | Classes | Method | Reference |
---|---|---|---|---|---|

Elevation | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) <338 m; (2) 338–536 m; (3) 536–721 m; (4) 721–909 m; (5) 909–1136 m; (6) >1136 m | Natural break | [4] |

Slope | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) <7.1°; (2) 7.1°–13°; (3) 13°–18.8°; (4) 18.8°–25.2°; (5) 25.2°–32.6°; (6) >32.6° | Natural break | [78] |

Aspect | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) Flat (−1°); (2) North (337.5°–360°, 0°–22.5°); (3) Northeast (22.5°–67.5°); (4) East (67.5°–112.5°; (5) Southeast (112.5°–157.5°); (6) South (157.5°–202.5°); (7) Southwest (202.5°–247.5°); (8) West (247.4°–292.5°); (9) Northwest (292.5°–337.5°) | Equal interval | [12] |

Convergence index | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) <−29.41); (2) −29.41 to −9.01; (3) −9.01 to 7.45; (4) 7.45–27.84; (5) >27.84 | Natural break | [4] |

SL | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) <22.06 m; (2) 22.06–50.3 m; (3) 50.3–77.9 m; (4) 77.9–103.4 m; (5) >103.4 m | Natural break | [79] |

Plan curvature | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) Concave (<0); (2) Flat (0); (3) Convex (>0) | Natural break | [45] |

Profile curvature | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) <−1.13; (2) −1.13 to −0.35; (3) −0.35 to 0.28; (4) 0.28–1.12; (5) >1.12 | Natural break | [80] |

Drainage density | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) <0.38 km/km^{2}; (2) 0.38–0.74 km/km^{2}; (3) 0.74–1.12 k m/km^{2}; (4) >1.12 km/km^{2} | Natural break | [60] |

Distance to stream | PALSAR, ASTER and SRTM DEMs | 12.5, 30 and 90 m | (1) < 227 m; (2) 227–483 m; (3) 483–745 m; (4) 745–1035 m; (5) >1035 m | Natural break | [81] |

Distance to road | Topographic map | 1:50,000 | (1) <1,204 m; (2) 1204–2564 m; (3) 2564–4041 m; (4) 4041–5712 m; (5) >5712 m | Natural break | [45] |

Distance to fault | Geological map | 1:100,000 | (1) <649 m; (2) 649–1492 m; )3) 1492–2575 m; (4) 2575–3924 m; (5) >3924 m | Natural break | [82] |

Lithology | Geology map | 1:100,000 | (1) DCkh; (2) Jk; (3) Jl; (4) K2l1; (5) K2l2; (6) Ktzl; (7) Mm, s, l; (8) PeEm; (9) Pel; (10) Plc; (11) Qm; (12) TRJs | Lithological units | - |

Rainfall | Raining data | - | (1) <798.3 mm; (2) 798.3–911 mm; (3) 911–1041.3 mm; (4) 1041–1196 mm; (5) >1196 m m | Natural break | [45] |

LU/LC | Landsat-8 image | 30 m | (1) Agriculture; (2) Dense forest; (3) Low forest; (4) Agri-orchard; (5) Agri-forest; (6); Dry farming forest; (7) Water | Supervisedclassification | - |

NDVI | Landsat-8 image | 30 m | (1) <0.3; (2) 0.3–0.43; (3) >0.43 | Natural break | [12] |

Model | Unstandardised Coefficients | Standardised Coefficients | t | Sig. | Collinearity Statistics | ||
---|---|---|---|---|---|---|---|

B | Std. Error | Beta | Tolerance (TOL) | Variance Inflation Factor (VIF) | |||

(Constant) | 1.163 | 0.483 | 2.408 | 0.017 | |||

Slope | −0.010 | 0.009 | −0.157 | −1.070 | 0.286 | 0.154 | 6.510 |

Stream power index (SPI) | 0.034 | 0.057 | 0.125 | 0.590 | 0.556 | 0.074 | 13.476 |

Topographic wetness index (TWI) | −0.059 | 0.052 | −0.262 | −1.143 | 0.255 | 0.063 | 15.948 |

LS | 0.001 | 0.001 | 0.062 | 1.039 | 0.300 | 0.939 | 1.064 |

NDVI | −1.063 | 0.349 | −0.220 | −3.048 | 0.003 | 0.632 | 1.582 |

Plan | 0.057 | 0.060 | 0.082 | 0.944 | 0.346 | 0.442 | 2.264 |

Rainfall | 0.000 | 0.000 | −0.091 | −1.001 | 0.318 | 0.401 | 2.494 |

Convergence | −0.005 | 0.002 | −0.178 | −2.315 | 0.022 | 0.558 | 1.791 |

Elevation | 0.000 | 0.000 | −0.185 | −1.611 | 0.109 | 0.251 | 3.980 |

Dis. to fault | −8.000 × 10^{−5} | 0.000 | −0.201 | −3.201 | 0.002 | 0.836 | 1.197 |

Dis. to road | −9.013 × 10^{−6} | 0.000 | −0.036 | −0.514 | 0.608 | 0.691 | 1.447 |

Dis. to stream | 0.000 | 0.000 | 0.129 | 1.557 | 0.121 | 0.479 | 2.087 |

Aspect | −1.151 × 10^{−5} | 0.000 | −0.002 | −0.040 | 0.968 | 0.955 | 1.047 |

Lithology | 0.024 | 0.045 | 0.042 | 0.547 | 0.585 | 0.565 | 1.769 |

LU/LC | 0.059 | 0.020 | 0.243 | 2.985 | 0.003 | 0.499 | 2.003 |

Profile | −0.0011 | 0.051 | −0.015 | −0.215 | 0.830 | 0.689 | 1.452 |

Drainage | 0.217 | 0.107 | 0.176 | 2.020 | 0.045 | 0.437 | 2.286 |

**Table 4.**Spatial relationship between conditioning factors and landslide locations with frequency ratio (FR).

Factor | Classes | Pixels in Domain | Landslides | FR | |||
---|---|---|---|---|---|---|---|

No. | % | No. | % | ||||

Elevation (m) | <338 | 137,911 | 11.5753 | 33 | 37.0787 | 3.2032 | |

338–536 | 234,710 | 19.7000 | 22 | 24.7191 | 1.2548 | ||

536–721 | 259,859 | 21.8108 | 21 | 23.5955 | 1.0818 | ||

721–909 | 263,398 | 22.1079 | 8 | 8.9888 | 0.4066 | ||

909–1136 | 190,367 | 15.9781 | 4 | 4.4944 | 0.2813 | ||

>1136 | 105,177 | 8.8279 | 1 | 1.1236 | 0.1273 | ||

Slope (°) | <7.1 | 182,669 | 15.3320 | 13 | 14.6067 | 0.9527 | |

7.1–13 | 291,575 | 24.4729 | 32 | 35.9551 | 1.4692 | ||

13–18.8 | 278,341 | 23.3621 | 23 | 25.8427 | 1.1062 | ||

18.8–25.2 | 219,564 | 18.4287 | 11 | 12.3596 | 0.6707 | ||

25.2–32.6 | 153,604 | 12.8925 | 5 | 5.6180 | 0.4358 | ||

>32.6 | 65,669 | 5.5118 | 5 | 5.6180 | 1.0193 | ||

Aspect | F | 132,288 | 9.8337 | 6 | 6.1856 | 0.6290 | |

N | 255,671 | 19.0054 | 10 | 10.3093 | 0.5424 | ||

NE | 92,803 | 6.8986 | 8 | 8.2474 | 1.1955 | ||

E | 103,340 | 7.6818 | 14 | 14.4330 | 1.8788 | ||

SE | 123,821 | 9.2043 | 9 | 9.2784 | 1.0080 | ||

SE | 125,536 | 9.3318 | 12 | 12.3711 | 1.3257 | ||

SW | 106,245 | 7.8978 | 12 | 12.3711 | 1.5664 | ||

W | 115,393 | 8.5778 | 10 | 10.3093 | 1.2019 | ||

NW | 136,325 | 10.1338 | 8 | 8.2474 | 0.8139 | ||

Convergence (100/m) | <−29.41 | 85,228 | 7.1535 | 9 | 10.1124 | 1.4136 | |

−29.41 to −9.01 | 254,471 | 21.3586 | 14 | 15.7303 | 0.7365 | ||

−9.01 to 7.45 | 471,169 | 39.5469 | 45 | 50.5618 | 1.2785 | ||

7.45–27.84 | 290,876 | 24.4142 | 18 | 20.2247 | 0.8284 | ||

>27.84 | 89,675 | 7.5267 | 3 | 3.3708 | 0.4478 | ||

SL (100/m) | <22.06 | 108,816 | 18.2882 | 12 | 13.4831 | 0.7373 | |

22.06–50.3 | 96,539 | 16.2249 | 9 | 10.1124 | 0.6233 | ||

50.3–77.9 | 107,115 | 18.0023 | 8 | 8.9888 | 0.4993 | ||

77.9–103.4 | 105,363 | 17.7079 | 44 | 49.4382 | 2.7919 | ||

103.4–128.2 | 106,179 | 17.8450 | 16 | 17.9775 | 1.0074 | ||

>128.2 | 70,994 | 11.9316 | 0 | 0.0000 | 0.0000 | ||

Plan curvature (100/m) | Concave | 564,975 | 47.4202 | 41 | 46.0674 | 0.9715 | |

Flat | 37,478 | 3.1457 | 0 | 0.0000 | 0.0000 | ||

Convex | 588,969 | 49.4341 | 48 | 53.9326 | 1.0910 | ||

Profile curvature (100/m) | <−1.13 | 68,989 | 5.7905 | 7 | 7.8652 | 1.3583 | |

−1.13 to −0.35 | 250,549 | 21.0294 | 16 | 17.9775 | 0.8549 | ||

−0.35 to 0.28 | 502,333 | 42.1625 | 42 | 47.1910 | 1.1193 | ||

0.28–1.12 | 292,982 | 24.5910 | 20 | 22.4719 | 0.9138 | ||

>1.12 | 76,569 | 6.4267 | 4 | 4.4944 | 0.6993 | ||

Drainage density (km/km^{2}) | <0.38 | 330,170 | 27.7123 | 5 | 5.6180 | 0.2027 | |

0.38–0.74 | 365,514 | 30.6788 | 18 | 20.2247 | 0.6592 | ||

0.74–1.12 | 320,115 | 26.8683 | 37 | 41.5730 | 1.5473 | ||

>1.12 | 175,623 | 14.7406 | 29 | 32.5843 | 2.2105 | ||

Distance to stream (m) | <227 | 340,196 | 28.5538 | 45 | 50.5618 | 1.7708 | |

227–483 | 304,400 | 25.5493 | 22 | 24.7191 | 0.9675 | ||

483–745 | 260,354 | 21.8524 | 15 | 16.8539 | 0.7713 | ||

745–1035 | 196,123 | 16.4613 | 4 | 4.4944 | 0.2730 | ||

>1035 | 90,349 | 7.5833 | 3 | 3.3708 | 0.4445 | ||

Distance to road (m) | <1204 | 320,797.2 | 26.9256 | 50 | 56.1798 | 2.0865 | |

1204–2564 | 276,105.2 | 23.1744 | 12 | 13.4831 | 0.5818 | ||

2564–4041 | 238,790.2 | 20.0425 | 12 | 13.4831 | 0.6727 | ||

4041–5712 | 199,267.2 | 16.7252 | 6 | 6.7416 | 0.4031 | ||

>5712 | 1,564,62.2 | 13.1324 | 9 | 10.1124 | 0.7700 | ||

Distance to fault (m) | <649 | 494,487 | 41.5039 | 34 | 38.2022 | 0.9204 | |

649–1492 | 329,948 | 27.6936 | 32 | 35.9551 | 1.2983 | ||

1492–2575 | 196,680 | 16.5080 | 19 | 21.3483 | 1.2932 | ||

2575–3924 | 107,320 | 9.0077 | 3 | 3.3708 | 0.3742 | ||

>3924 | 62,987 | 5.2867 | 1 | 1.1236 | 0.2125 | ||

Lithology | DCkh | 112 | 0.0094 | 0 | 0.0000 | 0.0000 | |

Jk | 15,756 | 1.3225 | 1 | 1.1236 | 0.8496 | ||

Jl | 14,742 | 1.2373 | 1 | 1.1236 | 0.9081 | ||

K2l1 | 135,059 | 11.3359 | 3 | 3.3708 | 0.2974 | ||

K2l2 | 45,114 | 3.7866 | 0 | 0.0000 | 0.0000 | ||

Ktzl | 1253 | 0.1052 | 0 | 0.0000 | 0.0000 | ||

Mm, s, l | 803,209 | 67.4160 | 61 | 68.5393 | 1.0167 | ||

PeEm | 9010 | 0.7562 | 0 | 0.0000 | 0.0000 | ||

Pel | 71,877 | 6.0329 | 2 | 2.2472 | 0.3725 | ||

Plc | 56,750 | 4.7632 | 15 | 16.8539 | 3.5384 | ||

Qm | 34,118 | 2.8636 | 6 | 6.7416 | 2.3542 | ||

TRJs | 4422 | 0.3712 | 0 | 0.0000 | 0.0000 | ||

Rainfall (mm) | <798.3 | 448,287 | 37.6262 | 45 | 50.5618 | 1.3438 | |

798.3911 | 305,254 | 25.6210 | 12 | 13.4831 | 0.5263 | ||

911–1041.3 | 195,091 | 16.3746 | 9 | 10.1124 | 0.6176 | ||

1041–1196 | 98,934 | 8.3039 | 2 | 2.2472 | 0.2706 | ||

>1196 | 143,855 | 12.0742 | 21 | 23.5955 | 1.9542 | ||

LU/LC | Agriculture | 976 | 0.0819 | 0 | 0.0000 | 0.0000 | |

Dense forest | 941,148 | 78.9937 | 39 | 43.8202 | 0.5547 | ||

Low forest | 17,995 | 1.5104 | 0 | 0.0000 | 0.0000 | ||

Agri-orchard | 90,665 | 7.6098 | 38 | 42.6966 | 5.6107 | ||

Agri-forest | 183 | 0.0154 | 0 | 0.0000 | 0.0000 | ||

Dry farming forest | 136,585 | 11.4640 | 12 | 13.4831 | 1.1761 | ||

Water | 3870 | 0.3248 | 0 | 0.0000 | 0.0000 | ||

NDVI | <0.3 | 116,202 | 9.7532 | 26 | 29.2135 | 2.9953 | |

0.3–0.43 | 177,729 | 14.9174 | 29 | 32.5843 | 2.1843 | ||

>0.43 | 897,490 | 75.3294 | 34 | 38.2022 | 0.5071 |

**Table 5.**Confusion matrix from random forest (RF) model (0 = non-landslide or negative, 1 = landslide or positive).

Observed | Predicted | |
---|---|---|

0 | 1 | |

0 | 54 | 10 |

1 | 17 | 44 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Arabameri, A.; Pradhan, B.; Rezaei, K.; Lee, C.-W. Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs. *Remote Sens.* **2019**, *11*, 999.
https://doi.org/10.3390/rs11090999

**AMA Style**

Arabameri A, Pradhan B, Rezaei K, Lee C-W. Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs. *Remote Sensing*. 2019; 11(9):999.
https://doi.org/10.3390/rs11090999

**Chicago/Turabian Style**

Arabameri, Alireza, Biswajeet Pradhan, Khalil Rezaei, and Chang-Wook Lee. 2019. "Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs" *Remote Sensing* 11, no. 9: 999.
https://doi.org/10.3390/rs11090999