Landslide Hazard Mapping Using AHP and Shannon Entropy Models in Cherikabad of Urmia Watershed

Hanifinia, Abdulaziz; Nazarnejad, Habib; Najafi, Saeed; Kornejady, Aiding Kornejady

Document Type : Original Article

Authors

¹ PhD. Student of Watershed Management, Urmia University

² Urmia University

³ Faculty of Natural Resources, Urmia University

⁴ Ph.D. of Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources

Abstract

Understanding the factors, conditions of occurrence, and development of landslides in order to assess and zoning the risk of its occurrence provides the possibility of achieving methods by which the dangers and damage caused by landslides can be prevented. The purpose of this study is to prepare a landslide risk potential map with AHP and Shannon entropy models in the Cherikabad watershed of Urmia. Initially, ninety-five landslide points were recorded using GPS through field visits and Google Earth. Layers of mean annual rainfall, slope degree, slope Aspect, elevation classes, land use, Lithology, distance to River, distance to road, distance to fault, distance to village, and normalized index of vegetation difference (NDVI) As factors affecting the occurrence of landslides in the basin were selected and maps of these layers were prepared in the GIS environment and digitized. After combining the layers of factors affecting the occurrence of landslide, the landslide risk potential map was prepared for two models of Analytic hierarchy process (AHP) and Shannon entropy. The results of preparing risk maps using the Areas Under the Receiver Operating Characteristic Curve with the area below the 99% significance curve showed that both models have good performance in evaluation. Shannon entropy model with 87.9% AUC and very good performance compared to AHP model with 70.6% AUC and a good performance class, has a higher accuracy in preparing the maps. According to Shannon's entropy model, 32% of the basin is located in high and very high-risk classes, which should be considered by the relevant authorities to implement relief measures.

Keywords

References

Abedini M., and Tulabi S. 2018. Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province, Iran. Environmental earth sciences, 77(11): 405- 418.

Amir Ahmadi A., Naemi Tabar M., and Gholkar ostadi B. 2017. Prioritize and zoning factors affecting the landslide model entropy (Case study: Bajgiran, Ghochan. Hydrogeomorphology, 3(11): 105-125. (In Persian)

Ariapour M., Bashiri M., and Golkarian A. 2019. Modeling of Mass Movements Using Data Mining Methods in the Southeast of Neyshabur City, Razavi Khorasan Province. Hydrogeomorphology, 5(19): 57-77. (In Persian)

Basu S.R., and Ghatowar L. 1988. Landslides in the Lish Basin of the eastern Himalayas and their control. Geomorphology and Environment. Allababad Geographical Society, Allahabad: 428-443.

Capitani M., Ribolini A., and Federici P.R. 2011. Influence of deep-seated gravitational slope deformations on landslide distributions: a statistical approach. Geomorphology, 201: 127-134.

Chen, W., Zhang, S., Li, R., & Shahabi, H. 2018. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Science of the Total Environment, 644: 1006-1018.

Gholami M., soleymani K., and Nekoee E. 2017. Landslide susceptibility mapping by use of Weight of Evidence (WofE) and Frequency Ratio (FR) and Dempster-Shafer (DSH) models: A case study of Sari-Kiasar region, Northern Iran. Journal of Range and Watershed Management, 70(3): 735-750 (In Persian)

Guha-Sapir D., Below R., and Hoyois P. 2020. EM-DAT: international disaster database. Brussels, Belgium: Université Catholique de Louvain.

HatamiFard R., Mousavi S., and Alimoradi M. 2012. Landslide hazard zonation using AHP model and GIS technique in Khoram Abad City. Geography and Environmental Planning, 23(3): 43-60. (In Persian)

Kornejady A., Ownegh M., and Bahremand, A. 2017. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 152: 144-162.

Lau NN. 2018. Determination of ground displacement of 25 April 2015 Nepal earthquake by GNSS precise point positioning Vietnam. J. Earth. Sci, 40:17–25.

Pham BT., Jaafari A., Prakash I., and Bui, DT. 2019. A novel hybrid intelligent model of support vector machines and the Multiboot ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment, 78(4): 2865-2886.

Pourghasemi HR., Moradi HR., and Aghda SF. 2013. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural hazards, 69(1): 749-779.

Pradhan B. 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51: 350-365.

Regmi NR., Giardino JR., Vitek JD., and Dangol V. 2010. Mapping landslide hazards in western Nepal: Comparing qualitative and quantitative approaches. Environmental & Engineering Geoscience, 16(2), 127-142.

Samodra G., Chen G., Sartohadi J., and Kasama K. 2018. Generating landslide inventory by participatory mapping: an example in Purwosari Area, Yogyakarta, Java. Geomorphology, 30(6): 306-313.

Sharma L.P., Patel N., Ghose M.K., and Debnath, P. 2012. Influence of Shannon’s entropy on landslide-causing parameters for vulnerability study and zonation—a case study in Sikkim, India. Arabian Journal of Geosciences,5(5): 421-431.

Song Y., Gong J., Gao S., Wang D., Cui T., Li Y., and Wei B., 2012. Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Computers & Geosciences, 42: 189-199.

Tay L.T., Lateh H., Hossain M.K., and Kamil A.A. 2014. Landslide hazard mapping using a Poisson distribution: a case study in Penang Island, Malaysia. In Landslide Science for a Safer Geoenvironment. Springer, Cham. (521-525 pp.). ‏

Wang H.B., Li J.M., Zhou B., Zhou Y., Yuan Z.Q., and Chen Y.P. 2017. Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility. Geoenvironmental Disasters, 4)15(: 1-12.

Wang Q., Li W., Wu Y., Pei Y., and Xie P. 2016. Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China). Environmental Earth Sciences, 75(7): 1-13.

Yalcin A. 2011. A geotechnical study on the landslides in the Trabzon Province, NE, Turkey. Applied clay science, 52: 11-29.

Yufeng S., and Fengxiang J. 2009. Landslide stability analysis based on generalized information entropy in 2009. international conference on environmental science and information application technology, 2: 83-85. IEEE.

Zhang TY., Han L., Zhang H., Zhao YH., Li XA., and Zhao L. 2019. GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine. Journal of Mountain Science, 16(6): 1275-1288.

Zhao H., Yao L., Mei G., Liu T., and Ning Y. 2017. A fuzzy comprehensive evaluation method based on AHP and entropy for a landslide susceptibility map. Entropy, 19(8):1-16. ‏

Zhuang J., Peng J., Wang G., Javed I., Wang Y., and Li W. 2018. Distribution and characteristics of landslide in Loess Plateau: A case study in Shaanxi province. Engineering Geology, 236: 89-96.

Applied Soil Research

Landslide Hazard Mapping Using AHP and Shannon Entropy Models in Cherikabad of Urmia Watershed

References

References

Volume 10, Issue 3 - Serial Number 3
August 2022
Pages 130-144

Landslide Hazard Mapping Using AHP and Shannon Entropy Models in Cherikabad of Urmia Watershed

References

References

Volume 10, Issue 3 - Serial Number 3August 2022Pages 130-144

Volume 10, Issue 3 - Serial Number 3
August 2022
Pages 130-144