Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methodology
2.2.1. Gully Erosion Inventory Mapping
2.2.2. Gully Erosion Predictor Variables (GEPV)
2.3. Multi-Collinearity Test
2.4. Multivariate Adaptive Regression Splines (MARS Model)
Evaluation of the Model
3. Results
3.1. Gully Erosion Susceptibility Model
3.2. Evaluation of the Susceptibility in Gully Erosion
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Group | Code | Explanation | Formation |
---|---|---|---|
1 | Ksr | Shale containing Ammonite with interaction of orbitolin limestone | Sarcheshmeh |
2 | PlQc | Fluvial conglomerate, Piedmont conglomerate, and sandstone. | - |
3 | Jmz | Grey thick-fluvial limestone and dolomite | Mozduran |
4 | Ksn | Brown to block shale and thin layers of siltstone and sandstone | Sanganeh |
4 | Murm | Light-red to brown marl and gyps marl with sandstone intercalations | - |
4 | Murmg | Gypsiferous marl | - |
4 | E1m | Marl, gypsiferous marl and limestone | - |
5 | Mur | Red marl, gypsiferous marl, sandstone and conglomerate | Dalichai |
5 | Kad-ab | Usual unit comprising argillaceous limestone, marl and shale | - |
5 | Jd | Well-bedded to thin-bedded, greenish-grey argillaceous limestone with intercalations of calcareous shale | - |
6 | Qft1 | Concentrated piedmont fan and valley terrace deposits | - |
6 | Qft2 | Low level piedment fan and valley terrace sedimentation | - |
6 | Qal | River channel, braided drainage and flood plain sedimentation | - |
6 | Qs,d | Loose loess sand sedimentation such as dunes | - |
7 | Jl | Light brown, thin-bedded to massive limestone | Lar |
8 | Ekh | Olive-green shale and sandstone | Khangiran |
9 | Kat | Green glauconitic sandstone and shale | Aitamir |
10 | Qsw | Swamp | - |
10 | Qm | Swamp and marsh | - |
Observed | Predicted | |
---|---|---|
%Gully (+) | %Non-Gully (−) | |
Gully (+) Non-gully (−) | (+|+) True positive (TP) | (−|+) False negative (FN) |
(+|−) False positive (FP) | (−|−) True negative (TN) |
Relative Distributions of the Gully Susceptibility Classes | |||||
---|---|---|---|---|---|
MARS Model | 70%/30% | 80%/20% | 90%/10% | ||
5 rep * | 10 rep | 15 rep | 10 rep | 10 rep | |
Low | 47.14 | 44.72 | 45.86 | 47.55 | 47.65 |
Medium | 22.83 | 23.63 | 22.85 | 22.16 | 21.94 |
High | 15.70 | 17.10 | 16.27 | 15.20 | 16.17 |
Very high | 14.34 | 14.55 | 15.02 | 15.09 | 14.25 |
MARS Model | Probabilistic Prediction Values | ||||
---|---|---|---|---|---|
70%/30% | 80%/20% | 90%/10% | |||
5 rep | 10 rep | 15 rep | 10 rep | 10 rep | |
Mean | 0.277 | 0.279 | 0.283 | 0.277 | 0.275 |
SD | 0.281 | 0.270 | 0.280 | 0.285 | 0.273 |
Minimum | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Maximum | 0.999 | 0.997 | 0.996 | 0.999 | 0.998 |
MARS Model | 70%/30% | 80%/20% | 90%/10% | ||
---|---|---|---|---|---|
5 rep | 10 rep | 15 rep | 10 rep | 10 rep | |
Sensitivity | 0.86 | 0.79 | 0.85 | 0.88 | 0.86 |
Specificity | 0.72 | 0.81 | 0.66 | 0.83 | 0.72 |
(Negative predictive value) | 0.70 | 0.78 | 0.72 | 0.74 | 0.75 |
(Positive predictive value) | 0.83 | 0.73 | 0.82 | 0.85 | 0.84 |
Efficiency (%) | 79.0 | 76.0 | 76.0 | 79.0 | 77.9 |
Kappa | 0.58 | 0.51 | 0.52 | 0.58 | 0.58 |
AUC Mean | 0.80 | 0.82 | 0.83 | 0.84 | 0.83 |
Robustness | 0.03 | 0.08 | 0.11 | 0.01 | 0.15 |
Observed | Predicted | |
---|---|---|
%Gully (+) | %Non-gully (−) | |
Gully (+) Non-gully (−) | (+|+) 40% (TP) | (−|+) 10% (FN) |
(+|−) 14% (FP) | (−|−) 36% (TN) |
© 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/).
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Javidan, N.; Kavian, A.; Pourghasemi, H.R.; Conoscenti, C.; Jafarian, Z. Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios. Water 2019, 11, 2319. https://doi.org/10.3390/w11112319
Javidan N, Kavian A, Pourghasemi HR, Conoscenti C, Jafarian Z. Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios. Water. 2019; 11(11):2319. https://doi.org/10.3390/w11112319
Chicago/Turabian StyleJavidan, Narges, Ataollah Kavian, Hamid Reza Pourghasemi, Christian Conoscenti, and Zeinab Jafarian. 2019. "Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios" Water 11, no. 11: 2319. https://doi.org/10.3390/w11112319