Comparing the Validity of Statistical and Knowledge-Based Methods for Landslide Susceptibility Mapping

Document Type : Original Research

Authors
J. Mosaffaie
A. Salehpour Jam
M. Tabatabaei
Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Islamic Republic of Iran.
10.22034/JAST.26.3.695
Abstract
In the Shahroud Watershed, there has been an increasing occurrence of landslides that have caused a lot of human and financial losses. Therefore, landslide susceptibility zonation is crucial for reducing landslide risk. The aim of this study was to compare the Landslide Susceptibility Maps (LSMs) of different methods. Therefore, thematic layers of the ten causal factors were prepared. Then, a landslide inventory map consisting of 104 landslides covering 1401 hectares was compiled and partitioned into two subsets including 70% for training and 30% for testing purposes. Three landslide susceptibility maps were prepared using the Frequency ratio (Fr), Statistical index (Si), and Analytic Hierarchy Process (AHP) methods. The validation process showed that the Si [Area Under the Curve (AUC)= 0.732] and Fr (AUC= 0.707) models presented a more valid LSM than AHP (AUC= 0.651) method. The Qs (Quality sum) index values also confirmed the results of the ROC (Receiver Operating Characteristic) curve such that the Qs index values of 1.71, 1.43, and 0.62 for, respectively, Fr, Si, and AHP models implied a more accurate LSMs of the Fr and Si models than the one from the AHP. The results of this study can be used as a basic step for landslide risk management in the study area.

Keywords

Subjects

Abay, A., Barbieri, G., & Woldearegay, K. (2019). GIS-based landslide susceptibility evaluation using analytical hierarchy process (AHP) approach: The case of Tarmaber District, Ethiopia. Momona Ethiopian Journal of Science, 11(1), 14-36.
Achu, A., & Reghunath, R. (2017). Application of analytical hierarchy process (AHP) for Landslide Susceptibility Mapping: A study from southern Western Ghats, Kerala, India. Paper presented at the Disaster, Risk and Vulnerability Conference 2017.
Armin, M., Mosaffaie, J., Ghorbannia Kheybari, V., & Khairi, A. (2019). Landslide zoning and its risk management plan in Kohgiluyeh and Boyerahmad province using Haeri-Sami model. Quantitative Geomorphological Research, 7(4), 176-196.
Chen, W., Han, H., Huang, B., Huang, Q., & Fu, X. (2017). Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China. ISPRS International Journal of Geo-Information, 6(11), 347.
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.
Dou, J., Yamagishi, H., Pourghasemi, H. R., Yunus, A. P., Song, X., Xu, Y., & Zhu, Z. (2015). An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards, 78(3), 1749-1776.
El Jazouli, A., Barakat, A., & Khellouk, R. (2019). GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters, 6(1), 1-12.
Gholami, M., Ghachkanlu, E. N., Khosravi, K., & Pirasteh, S. (2019). Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. Journal of Earth System Science, 128(2), 1-22.
He, H., Hu, D., Sun, Q., Zhu, L., & Liu, Y. (2019). A landslide susceptibility assessment method based on GIS technology and an AHP-weighted information content method: A case study of southern Anhui, China. ISPRS International Journal of Geo-Information, 8(6), 266.
Joybari, J., Kavian, A. A., & Mosaffaie, J. (2017). Effect of Land Use on Landslide Movement in the Tavan District, Qazvin.
Kanungo, D., Arora, M., Sarkar, S., & Gupta, R. (2006). A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85(3-4), 347-366.
Karimi Sangchini, E., Salehpour Jam, A., & Mosaffaie, J. (2022). Flood risk management in Khorramabad watershed using the DPSIR framework. Natural Hazards, 114(3), 3101-3121. doi: 10.1007/s11069-022-05507-4
Khan, H., Shafique, M., Khan, M. A., Bacha, M. A., Shah, S. U., & Calligaris, C. (2019). Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 22(1), 11-24. doi: https://doi.org/10.1016/j.ejrs.2018.03.004
Lee, S. (2007). Application and verification of fuzzy algebraic operators to landslide susceptibility mapping. Environmental Geology, 52(4), 615-623. doi: 10.1007/s00254-006-0491-y
Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33-41.
Morady, H. R., Pourghasemi, H. R., Mohammdi, M., & Mahdavifar, M. R. (2010). Landslide hazard zoning using gamma fuzzy operator, with a case study of haraz watershed. ENVIRONMENTAL SCIENCES, 7(4), 129-142.
Mosaffaie, J. (2016). Application of artificial neural network, multiple-regression and index-flood techniques in regional flood frequency estimation. International Journal of Water, 10(4), 328-342.
Mosaffaie, J., Ekhtesasi, M. R., Dastorani, M. T., Azimzadeh, H. R., & Chahuki, M. A. Z. (2015). Temporal and spatial variations of the water erosion rate. Arabian journal of Geosciences, 8(8), 5971-5979.
Mosaffaie, J., & Salehpour Jam, A. (2018). Economic assessment of the investment in soil and water conservation projects of watershed management. Arabian journal of Geosciences, 11(14), 368.
Mosaffaie, J., & Salehpour Jam, A. (2021). Prioritization of factors preventing participation of rural people in soil & water conservation projects (The case of Vers watershed). Journal of Agricultural Science and Technology, 23(5), 975-986.
Mosaffaie, J., Salehpour Jam, A., & Sarfaraz, F. (2023). Landslide risk assessment based on susceptibility and vulnerability. Environment, Development and Sustainability, 1-19.
Mosaffaie, J., Salehpour Jam, A., Sarfaraz, F., & Shadfar, S. (2020). Evaluation of Landslide Susceptibility Zonation applying Fuzzy Gamma Operators in Taleghanroud Watershed of Qazvin Province. Geography and.
Nguyen, T. T. N., & Liu, C.-C. (2019). A new approach using AHP to generate landslide susceptibility maps in the Chen-Yu-Lan Watershed, Taiwan. Sensors, 19(3), 505.
Oh, H.-J., & Lee, S. (2017). Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree. Applied Sciences, 7(10), 1000.
Ozdemir, A., & Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180-197.
Peyrowan, H. R., & Shariat Jafari, M. (2013). Presentation of a comprehensive method for determining erodibility rate of rock units with a review on Iranian geology. Journal of Watershed Engineering and Management.
Pourghasemi, H. R., Moradi, H. R., & Fatemi Aghda, S. M. (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. doi: 10.1007/s11069-013-0728-5
Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63(2), 965-996.
Pradhan, B., Lee, S., & Buchroithner, M. F. (2009). Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping. Applied Geomatics, 1(1), 3-15.
Pradhan, B., Youssef, A., & Varathrajoo, R. (2010). Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geo-spatial Information Science, 13(2), 93-102.
Pradhan, B., & Youssef, A. M. (2010). Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian journal of Geosciences, 3(3), 319-326.
Ruff, M., & Czurda, K. (2008). Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria). Geomorphology, 94(3-4), 314-324.
Saaty, T. L. (1980). The analytical hierarchy process, planning, priority. Resource allocation. RWS publications, USA.
Salehpour Jam, A., Mosaffaie, J., Sarfaraz, F., Shadfar, S., & Akhtari, R. (2021). GIS-based landslide susceptibility mapping using hybrid MCDM models. Natural Hazards, 1-22.
Salehpour Jam, A., Mosaffaie, J., & Tabatabaei, M. (2021). Management Responses for Chehel-Chay Watershed Health Improvement Using the DPSIR Framework. Journal of Agricultural Science and Technology, 23, 0-0.
Salehpour Jam, A., Mosaffaie, J., & Tabatabaei, M. R. (2021). Assessment of comprehensiveness of soil conservation measures using the DPSIR framework. Environmental Monitoring and Assessment, 193(1), 1-19.
Salehpour Jam, A., Mosaffaie, J., & Tabatabaei, M. R. (2023). Raster-based landslide susceptibility mapping using compensatory MADM methods. Environmental Modelling & Software, 159, 105567. doi: https://doi.org/10.1016/j.envsoft.2022.105567
Sangchini, E. K., Emami, S. N., Tahmasebipour, N., Pourghasemi, H. R., Naghibi, S. A., Arami, S. A., & Pradhan, B. (2016). Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran. Arabian journal of Geosciences, 9(3), 201.
Santos, J. G. (2015). GIS-based hazard and risk maps of the Douro river basin (north-eastern Portugal). Geomatics, Natural Hazards and Risk, 6(2), 90-114.
Saygin, F., Şişman, Y., Dengiz, O., & Şişman, A. (2023). Spatial assessment of landslide susceptibility mapping generated by fuzzy-AHP and decision tree approaches. Advances in Space Research. doi: https://doi.org/10.1016/j.asr.2023.01.057
Soltani, M. J., Motamedvaziri, B., Mosaffaei, J., Noroozi, A. A., & Ahmadi, H. (2023). Cause and effect analysis of the trend of dust storms using the DPSIR framework in the Hendijan region. International Journal of Environmental Science and Technology. doi: 10.1007/s13762-023-04882-0
TJPrl, F. (2006). An introduction to ROC analysis. Pattern Recogn Lett, 27(8), 861-874.
Turan, İ. D., Özkan, B., Türkeş, M., & Dengiz, O. (2020). Landslide susceptibility mapping for the Black Sea Region with spatial fuzzy multi-criteria decision analysis under semi-humid and humid terrestrial ecosystems. Theoretical and Applied Climatology, 140(3), 1233-1246. doi: 10.1007/s00704-020-03126-2
Van Westen, C. (1997). Statistical landslide hazard analysis. Ilwis, 2, 73-84.
Wang, M., Liu, M., Yang, S., & Shi, P. (2014). Incorporating triggering and environmental factors in the analysis of earthquake-induced landslide hazards. International Journal of Disaster Risk Science, 5(2), 125-135.
Yalcin, A., Reis, S., Aydinoglu, A. C., & Yomralioglu, T. (2011). A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA, 85(3), 274-287. doi: https://doi.org/10.1016/j.catena.2011.01.014
Journal of Agricultural Science and Technology
Volume 26, Issue 3
May and June 2024
Pages 695-709