Landslide hazard zonation based on fuzzy-analytical hierarchy process (FAHP) and Multi-criteria decision analysis (Case study: Marbar river basin)

Subject Areas : Spatial data infrastructures and standardisation

Mohammad Reza  Sajjadi ¹ (MSc. Student of Hydraulic Structures, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran)
Ahmad  Ahmadi ² (Associate Professor, Department of Water and Environment, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran)
Behnaz  Bigdeli ³ (Assistant Professor, Department of Geotechnic, Road and Surveying, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran)

Received: 2020-01-04 Accepted : 2020-08-05 Published : 2020-12-21

Keywords: Marbar river basin, zoning, landslide hazard, Fuzzy analytical hierarchy process (FAHP),

Abstract :

Background and ObjectiveLandslide as a terrifying disaster can cause human and economic losses and the destruction of cultural and natural heritage. While the need for a method to directly predict the location of landslides has vital importance but currently, the prediction is not possible. The zoning of landslide hazard can be an efficient indirect approach. This paper proposes a method for landslide hazard zoning based on the decision fusion and Analytical Hierarchy Process (AHP) in the Semirom of Isfahan province. Materials and Methods In the first step of the proposed methodology of this research, GIS information layers of the study area are collected. Then by using of fuzzy and non-fuzzy hierarchical analysis method and based on expert knowledge, the layers and sub-layers were weighted. In addition, two different overlay methods including weighted overlay and fuzzy overlay are applied for zoning of the AHP and fuzzy AHP results. Combination of both AHP and fuzzy AHP methods with two overlay methods create four zoning maps for the area. The Fuzzy Overlay tool makes it possible for the analysis of the possibility of a phenomenon belonging to multiple sets in a multi-criteria overlay analysis. Not only the fuzzy overlay determines the influential members in the occurrence of a phenomenon but also analyzes the relationships between the memberships of several sets. Weight overlapping is one of the most effective methods used to overlay analysis to address multiple-criteria questions such as location selection and appropriate models. This method will adopt the values in the input raster to a common evaluation criterion for suitability or priority, risk, or appropriate scale. The cell values of each row of inputs increase with the increase of importance of the raster. It also combines the resultant cells to produce the output raster. After obtaining four zoning maps, a decision fusion strategy is applied for the fusion of these maps. Decision fusion systems or in general data fusion or combination strategies combines various decisions made from different methods or data to ultimately make decisions that are more precise and reliable than the result obtained from a single decision. One of the most important and effective methods for integrating decisions is based on the concept of voting. In this method, one vote is assigned to each decision. The simplest form of this method is known as the majority voting. In this method, if all decision-making methods have the same weight and accuracy, the decision of all strategies for an input sample is considered to be the same weight, and the decision with the highest score will be introduced as the winning class for the input sample. Results and Discussion The study area is located approximately 60 kilometers from Semirom city. Also, this area is located in Marbur River watershed. Generally, different factors can be effective in slope instability and landslide, which in this research, slope, aspect, distance to fault, distance to roads, distance to drainages, distance to residential areas, lithology and rainfall were selected for assessing the landslide phenomenon. These effective layers are obtained from information data such as Digital Elevation Model (DEM), fault lines, rivers location, streams location, residential areas, roads location, lithology and synoptic stations. The digital elevation model (DEM) of the region is prepared with 30 meters pixel size from the USGS website. By using DEM in GIS, slope and aspect maps in five classes are created. Faults map of the studied area is obtained from 1:100000 geology map of the Geology organization center of the country. Also, by using Euclidean distance in GIS, distance to faults layer is created in five classes. For preparation of rainfall map, the rainfall content of the studied area has been used from the average rainfall data of the Iran Meteorological Organization in the last 10 years of 19 meteoroidal stations. Based on the rainfall information, the area is divided into five classes. Roads map of the area is obtained from 1:25000 map of National Cartographic Center.  The distance to road layer is created from roads map of the area and divided into five classes. For drainage and residential area maps, a 1:25000 map from NCC is applied. Also, distance to residential area layer is created by this map in five classes. For assessment of the lithology in this area, a 1:100000 geology map is applied. Conclusion Results showed that the zoning methods provide satisfactory results, but eventually the results were improved with the decision fusion strategy. For validation our finding the results were compared with historical landslides. Based on the results, it was concluded that zoning by four different combinations: hierarchical analysis and overweight analysis, hierarchical analysis and fuzzy overlay, fuzzy hierarchical analysis and weighted overlay, and fuzzy hierarchical analysis and fuzzy overlaying, have a precision of 80%, 86%, 75% and 88% respectively. After integrating the results of these four methods, the accuracy of the zoning increased to 90%.

References:

Abedini M, 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. doi:https://doi.org/10.1007/s12665-018-7524-1.

Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA. 2017. Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arabian Journal of Geosciences, 10(8): 194. doi:https://doi.org/10.1007/s12517-017-2980-6.

Ahmed B. 2015. Landslide susceptibility modelling applying user-defined weighting and data-driven statistical techniques in Cox’s Bazar Municipality, Bangladesh. Natural Hazards, 79(3): 1707-1737. doi:https://doi.org/10.1007/s11069-015-1922-4.

Anbalagan R, Singh B. 1996. Landslide hazard and risk assessment mapping of mountainous terrains - a case study from Kumaun Himalaya, India. Engineering Geology, 43(4): 237-246. doi:https://doi.org/10.1016/S0013-7952(96)00033-6.

Asgharizadeh MJ. 2018. Multi-criteria decision making techniques. First Edition. University of Tehran Press. 562 p. (In Persian).

Baharvand S, Soori S. 2016. Landslide hazard zonation using artificial neural network (Case study: Sepiddasht-Lorestan, iran). Journal of RS and GIS for Natural Resources (Journal of Applied RS & GIS Techniques in Natural Resource Science), 6(4): 15-31. (In Persian).

Bera A, Mukhopadhyay BP, Das D. 2019. Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: a case study from Eastern Himalayas, Namchi, South Sikkim. Natural Hazards, 96(2): 935-959. doi:https://doi.org/10.1007/s11069-019-03580-w.

Chandra DK, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1): 135-165. doi:10.1007/s11069-012-0347-6.

Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P. 2008. Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology, 102(3): 496-510. doi:https://doi.org/10.1016/j.geomorph.2008.05.041.

Dai FC, Lee CF. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3): 213-228. doi:https://doi.org/10.1016/S0169-555X(01)00087-3.

Fauvel M, Chanussot J, Benediktsson J. 2006. A combined support vector machines classification based on decision fusion. In: 2006 IEEE International Symposium on Geoscience and Remote Sensing, Citeseer. 2494-2497.

Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage W. 2008. on behalf of the JTC-1 Joint Technical Committee on Landslides and Engineered Slopes (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol, 102(3-4): 85-98. doi:https://doi.org/10.1016/j.enggeo.2008.03.014.

Jiang W, Rao P, Cao R, Tang Z, Chen K. 2017. Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation. Journal of Geographical Sciences, 27(4): 439-462. doi:10.1007/s11442-017-1386-4.

Kanungo DP, Arora MK, Sarkar S, Gupta RP. 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): 347-366. doi:https://doi.org/10.1016/j.enggeo.2006.03.004.

Kayastha P, Dhital MR, De Smedt F. 2012. Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Natural Hazards, 63(2): 479-498. doi:https://doi.org/10.1007/s11069-012-0163-z.

Kumar R, Anbalagan R. 2016. Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. Journal of the Geological Society of India, 87(3): 271-286. doi:https://doi.org/10.1007/s12594-016-0395-8.

Kuncheva LI, Whitaker CJ. 2003. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning, 51(2): 181-207. doi:https://doi.org/10.1023/A:1022859003006.

Lynn H, Bobrowsky PT. 2008. The landslide handbook: a guide to understanding landslides. US Geological Survey Reston, 129 p.

Rahmati M, Zand F. 2018. Landslide hazard zonation using geographic information system landslide (Case study: Robat-Siahpoush rural district, lorestan province). Journal of RS and GIS for Natural Resources (Journal of Applied RS & GIS Techniques in Natural Resource Science), 8(4): 63-75. (In Persian).

Ruta D, Gabrys B. 2000. An overview of classifier fusion methods. Computing and Information systems, 7(1): 1-10.

Sharma S, Mahajan AK. 2018. Comparative evaluation of GIS-based landslide susceptibility mapping using statistical and heuristic approach for Dharamshala region of Kangra Valley, India. Geoenvironmental Disasters, 5(1): 4. doi:https://doi.org/10.1186/s40677-018-0097-1.

Soeters R, Van Westen C. 1996. Slope instability recognition, analysis and zonation. In:  Landslides: investigation and mitigation. Transportation Research Board, National Research Council, Special Report 247. National Academy Press, Washington D.C., U.S.A. 129-177 p.

Subedi P, Subedi K, Thapa B, Subedi P. 2019. Sinkhole susceptibility mapping in Marion County, Florida: Evaluation and comparison between analytical hierarchy process and logistic regression based approaches. Scientific reports, 9(1): 1-18. doi:https://doi.org/10.1038/s41598-019-43705-6.

Vojteková J, Vojtek M. 2020. Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia. Geomatics, Natural Hazards and Risk, 11(1): 131-148. doi:https://doi.org/10.1080/19475705.2020.1713233.

Wu Y, Li W, Liu P, Bai H, Wang Q, He J, Liu Y, Sun S. 2016. Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environmental Earth Sciences, 75(5): 422. doi:https://doi.org/10.1007/s12665-015-5194-9.