Research Article
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Landslide Susceptibility Assessment of Forest Roads*

Year 2016, Volume: 2 Issue: 2, 54 - 60, 18.11.2016

Abstract



In last few decades, there has been an increasing
interest in using Landslide Susceptibility Maps (LSMs) especially in planning
and decision making stages of landslide prevention and mitigation activities,
as well as in landslide related studies. In forested areas, inappropriately
located roads potentially cause slope instability problems such as landslides
which then result in serious destructions on road networks and deformations on
road platforms. Thus, one of the further usages of LSM may involve overlapping
analysis with forest roads in order to obtain information about how road networks
should be planned and located considering land sliding potential. Statistical
approaches such as Logistic Regression (LR) method are well integrated with GIS
based evaluation of landslide probability of slopes in larger regions. In this
study, LSMs of two forest districts (Gölyaka and Kardüz) in Gölyaka Forest
Directorate (Düzce, Turkey) was generated by using LR method based on an
inventory of 52 landslides and eight conditioning parameters. These parameters
include elevation, slope, land-use, lithology, aspect, distance to faults,
distance to streams, and distance to roads. For overlapping analysis, forest
road layer was obtained from Bolu Regional Directorate of Forestry (RDF) in
vector data format. It was found that landslide susceptibilities obtained in
study area were between 0 and 0.57 with 0.85 AUC (Area Under the Curve) value.
The results indicated that all of the selected parameters had positive effects
on landslide occurrences. After normalization of generated susceptibility
values between 0 and 1, LSM was classified into following five classes: very
low (0-0.2), low (0.2-0.4), moderate (0.4-0.6), high (0.6-0.8), and very high
(0.8-1.0). Then, classified LSM was overlapped with forest road layer which
includes the total of 380.8 km road. According to classified susceptibility
map, more than 95% of total area is located in very low and low susceptibility
classes, 3% of the area has moderate landslide susceptibility, while remains
have high and very high susceptibilities. According to overlapping analysis,
1.3 km of roads is located within very high susceptibility and 5.1 km of roads
is located within high susceptibility classes. The rest of the roads (i.e. more
than 95%) are located in other susceptibility classes.



References

  • Akgün, A., 2011. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides, 9: 93–106.
  • Ayalew, L., Yamasgishi, H., 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorpholgy, 65:15-31.
  • Bai, S,B,, Wang. J,, Zhang, F.Y., Pozdnoukhov, A., Kanevski, M., 2008. Prediction of landslide susceptibility using logistic regression: a case study in Bailongjiang River Basin, China. Fifth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 647–651.
  • Begueria, S. 2006. Validation and evaluation of predictive models in hazard assessment and risk management. Natural Hazards, 37 (2006), 315–329.
  • Eker, R. and Aydın, A., 2014. Assessment of forest road conditions in terms of landslide susceptibility: a case study in Yığılca Forest Directorate (Turkey). Turk J Agric For, 38(2): 281-290.
  • Ercanoğlu, M. and Gökceoğlu, C., 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol, 41: 720–730.
  • Ercanoğlu, M. and Temiz, F.A., 2011. Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci, 64: 949–964.
  • Erener, A. and Düzgün, H.S.B., 2010. Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides, 7: 55–68.
  • Görcelioğlu, E., 2004. Forest Road Erosion Relations. Istanbul University, Faculty of Forestry Publication No: 4460/476, 184 p.
  • Hosseini, S.A., Mazrae, M.R., Lotfalian, M., Parshakhoo, A., 2012. Designing an optimal road network by consideration of environmental impacts in GIS. J Environ Eng Landsc, 20: 58–66.
  • Kıncal, C., Akgün, A., Koca, M.Y., 2009. Landslide susceptibility assessment in the İzmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci, 59: 745–756.
  • Lee, S. and Talib, J.A., 2005. Probabilistic landslide susceptibility and factor effect analysis. Environ Geol, 47: 982–990.
  • Nefeslioğlu, H.A., Gökçeoğlu, C., Sönmez, H., 2008. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol, 97: 171–191.
  • Reichenbach, P., Ardizzone, F., Cardinali, M., Galli, M., Guzetti, F., Salvati, P., 2002. Landslide events and their impact on the transportation network in the Umbria region, central Italy. Proceedings of the 4th EGS Plinius Conference held at Mallorca, Spain.
  • Süzen, M.L. and Kaya, B.Ş., 2011. Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digit Earth, 5: 1–18.
  • Vahidnia, M.H., Alesheikh, A.A., Alimohammadi, A., Hosseinali, F., 2010. A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci, 36: 1101–1114.
  • Van Den Eeckhaut, M., Marre, A., Poessen, J., 2010. Comparison of two landslide susceptibility assessments in the Champagne–Ardenne region (France). Geomorphology, 115: 141–155.
  • Yılmaz, I., 2009. Landslide susceptibility MAPPING using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey). Comput Geosci, 35: 1125–1138.
Year 2016, Volume: 2 Issue: 2, 54 - 60, 18.11.2016

Abstract

References

  • Akgün, A., 2011. A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides, 9: 93–106.
  • Ayalew, L., Yamasgishi, H., 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorpholgy, 65:15-31.
  • Bai, S,B,, Wang. J,, Zhang, F.Y., Pozdnoukhov, A., Kanevski, M., 2008. Prediction of landslide susceptibility using logistic regression: a case study in Bailongjiang River Basin, China. Fifth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 647–651.
  • Begueria, S. 2006. Validation and evaluation of predictive models in hazard assessment and risk management. Natural Hazards, 37 (2006), 315–329.
  • Eker, R. and Aydın, A., 2014. Assessment of forest road conditions in terms of landslide susceptibility: a case study in Yığılca Forest Directorate (Turkey). Turk J Agric For, 38(2): 281-290.
  • Ercanoğlu, M. and Gökceoğlu, C., 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol, 41: 720–730.
  • Ercanoğlu, M. and Temiz, F.A., 2011. Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci, 64: 949–964.
  • Erener, A. and Düzgün, H.S.B., 2010. Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides, 7: 55–68.
  • Görcelioğlu, E., 2004. Forest Road Erosion Relations. Istanbul University, Faculty of Forestry Publication No: 4460/476, 184 p.
  • Hosseini, S.A., Mazrae, M.R., Lotfalian, M., Parshakhoo, A., 2012. Designing an optimal road network by consideration of environmental impacts in GIS. J Environ Eng Landsc, 20: 58–66.
  • Kıncal, C., Akgün, A., Koca, M.Y., 2009. Landslide susceptibility assessment in the İzmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environ Earth Sci, 59: 745–756.
  • Lee, S. and Talib, J.A., 2005. Probabilistic landslide susceptibility and factor effect analysis. Environ Geol, 47: 982–990.
  • Nefeslioğlu, H.A., Gökçeoğlu, C., Sönmez, H., 2008. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol, 97: 171–191.
  • Reichenbach, P., Ardizzone, F., Cardinali, M., Galli, M., Guzetti, F., Salvati, P., 2002. Landslide events and their impact on the transportation network in the Umbria region, central Italy. Proceedings of the 4th EGS Plinius Conference held at Mallorca, Spain.
  • Süzen, M.L. and Kaya, B.Ş., 2011. Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digit Earth, 5: 1–18.
  • Vahidnia, M.H., Alesheikh, A.A., Alimohammadi, A., Hosseinali, F., 2010. A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci, 36: 1101–1114.
  • Van Den Eeckhaut, M., Marre, A., Poessen, J., 2010. Comparison of two landslide susceptibility assessments in the Champagne–Ardenne region (France). Geomorphology, 115: 141–155.
  • Yılmaz, I., 2009. Landslide susceptibility MAPPING using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey). Comput Geosci, 35: 1125–1138.
There are 18 citations in total.

Details

Subjects Engineering
Journal Section Research Articles
Authors

Remzi Eker

Abdurrahim Aydin

Publication Date November 18, 2016
Published in Issue Year 2016 Volume: 2 Issue: 2

Cite

APA Eker, R., & Aydin, A. (2016). Landslide Susceptibility Assessment of Forest Roads*. European Journal of Forest Engineering, 2(2), 54-60.

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