GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam

Dang Quang Thanh; Duy Huu Nguyen; Indra Prakash; Abolfazl Jaafari; Viet -Tien Nguyen; Tran Van Phong; Binh Thai Pham

doi:10.15625/0866-7187/42/1/14758

Author affiliations

Authors

Dang Quang Thanh University of Transport Technology, Hanoi, Vietnam
Duy Huu Nguyen Faculty of Geography, VNU University of Science, Hanoi, Vietnam
Indra Prakash Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India
Abolfazl Jaafari Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran
Viet -Tien Nguyen 1) Institute of Geological Sciences, VAST, Hanoi, Vietnam 2) Graduate University of Science and Technology, VAST, Hanoi, Vietnam
Tran Van Phong Institute of Geological Sciences, VAST, Hanoi, Vietnam
Binh Thai Pham University of Transport Technology, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/0866-7187/42/1/14758

Keywords:

Landslides, Frequency Ratio, GIS, Da Lat City, Vietnam

Abstract

Landslide susceptibility mapping of the city of Da Lat, which is located in the landslide prone area of Lam Dong province of Central Vietnam region, was carried out using GIS based frequency ratio (FR) method. There are number of methods available but FR method is simple and widely used method for landslide susceptibility mapping. In the present study, eight topographical and geo-environmental landslide-conditioning factors were used including slope, elevation, land use, weathering crust, soil, lithology, distance to geology features, and stream density in conjunction with 70 past landslide locations. The results show that 6.27% of the area is in the very low susceptibility area, 21.03% in the low susceptibility area, 27.09% in the moderate susceptibility area and 27.41% of the area is in the high susceptibility zone and 18.21% in the very high susceptibility zone. The landslide susceptibility map produced in this study helps to assist decision makers in proper land use management and planning.

Downloads

Download data is not yet available.

References

Abedini M., Ghasemian B., Shirzadi A., Shahabi H., Chapi K., Pham B.T., Bin Ahmad B., Tien Bui D., 2018. A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment. Geocarto International, 1–31.

Akgun A., Dag S., Bulut F., 2008. Landslide susceptibility mapping for a landslide-prone area (findikli, ne of turkey) by likelihood-frequency ratio and weighted linear combination models. Environmental Geology, 54 (6), 1127–1143.

Alkhasawneh M.S., Ngah U.K., Tay L.T., Isa M., Ashidi N., Al-Batah M.S., 2014. Modeling and testing landslide hazard using decision tree. Journal of Applied Mathematics, 2014, 1-9.

Althuwaynee O.F., Pradhan B., Lee S., 2012. Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences, 44, 120–135.

Ayalew L., Yamagishi H., 2005. The application of gis-based logistic regression for landslide susceptibility mapping in the kakuda-yahiko mountains, central japan. Geomorphology, 65 (1–2), 15–31.

Barlow J., Martin Y., Franklin S., 2003. Detecting translational landslide scars using segmentation of landsat etm+ and dem data in the northern cascade mountains, british columbia. Canadian Journal of Resmote Sensing, 29, 510-517.

Chang K.-T., Merghadi A., Yunus A.P., Pham B.T., Dou J., 2019. Evaluating scale effects of topographic variables in landslide susceptibility models using gis-based machine learning techniques. Scientific reports, 9(1), 1–21.

Chen W., Pourghasemi H.R., Naghibi S., 2017. A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in china. Bulletin of Engineering Geology and the Environment, 1–18.

Constantin, M., Bednarik, M., Jurchescu, M.C. & Vlaicu, M., 2011. Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the sibiciu basin (romania). Environmental earth sciences, 63(2), 397–406.

Dou J., Tien Bui D., Yunus A.P., Jia K., Song X., Revhaug I., Huan X., Zhu Z., 2015. Optimization of causative factors for landslide susceptibility evaluation using remote sensing and gis data in parts of niigata, japan. PLoS ONE, 10, e0133262.

Dou J., Yunus A.P., Tien Bui D., Sahana M., Chen C.-W., Zhu Z., Wang W., Pham B.T., 2019a. Evaluating gis-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the lidar dem. Remote Sensing, 11(6), 638.

Dou J., Yunus A.P., Xu Y., Zhu Z., Chen C.-W., Sahana M., Khosravi K., Yang Y., Pham B.T., 2019b. Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the dongjiang reservoir watershed, china. Natural Hazards, 97(2), 579–609.

Duc D., 2013. Rainfall-triggered large landslides on 15 december 2005 in Van Canh district, Binh Dinh province, Vietnam. Landslides, 10.

Duman T.Y., Can T., Gokceoglu C., Nefeslioglu H.A., Sonmez H., 2006. Application of logistic regression for landslide susceptibility zoning of cekmece area, istanbul, turkey. Environmental Geology, 51(2), 241–256.

Ermini L., Catani F., Casagli N., 2005. Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66(1–4), 327–343.

Froude M., Petley D., 2018. Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18, 2161–2181.

Gholami M., Nekouei Ghachkanlu E., Khosravi K., Pirasteh S., 2018. Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. Journal of Earth System Science, 128(2), 42.

Gorsevski P.V., Jankowski P., 2010. An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and kalman filter. Computers & Geosciences, 36(8), 1005–1020. http://www.sciencedirect.com/science/article/pii/S0098300410001226.

Hasekioğulları G.D., Ercanoglu M., 2012. A new approach to use ahp in landslide susceptibility mapping: A case study at yenice (karabuk, nw turkey). Natural Hazards, 63(2), 1157–1179.

He Q., Xu Z., Li S., Li R., Zhang S., Wang N., Pham B.T., Chen W., 2019. Novel entropy and rotation forest-based credal decision tree classifier for landslide susceptibility modeling. Entropy, 21(2), 106.

Jaafari A., Najafi A., Pourghasemi H., Rezaeian J., Sattarian A., 2014. Gis-based frequency ratio and index of entropy models for landslide susceptibility assessment in the caspian forest, northern iran. International Journal of Environmental Science and Technology, 11(4), 909–926.

Kayastha P., Dhital M.R., De Smedt F., 2013. Application of the analytical hierarchy process (ahp) for landslide susceptibility mapping: A case study from the tinau watershed, west nepal. Computers & Geosciences, 52, 398–408.

Lee S., Choi J., 2004. Landslide susceptibility mapping using gis and the weight-of-evidence model. International Journal of Geographical Information Science, 18(8), 789–814.

Lee S., Oh H.-J., 2012. Ensemble-based landslide susceptibility maps in jinbu area, korea. Terrigenous mass movements. Springer, 193–220.

Lee S., Pradhan B., 2007. Landslide hazard mapping at selangor, malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33–41.

Lee S., Ryu J.-H., Lee M.-J., Won J.-S., 2006. The application of artificial neural networks to landslide susceptibility mapping at janghung, korea. Mathematical Geology, 38(2), 199–220.

Moosavi V., Niazi Y., 2016. Development of hybrid wavelet packet-statistical models (wp-sm) for landslide susceptibility mapping. Landslides, 13(1), 97–114.

Nguyen P.T., Tuyen T.T., Shirzadi A., Pham B.T., Shahabi H., Omidvar E., Amini A., Entezami H., Prakash I., Phong T.V., 2019. Development of a novel hybrid intelligence approach for landslide spatial prediction. Applied Sciences, 9(14), 2824.

Nguyen T.T.B., Satir M., Siebel W., Chen F., 2004. Granitoids in the dalat zone, southern vietnam: Age constraints on magmatism and regional geological implications. International Journal of Earth Sciences, 93(3), 329–340.

Nohani E., Moharrami M., Sharafi S., Khosravi K., Pradhan B., Pham B.T., Lee S.M., Melesse A., 2019. Landslide susceptibility mapping using different gis-based bivariate models. Water, 11(7), 1402.

Pham B.T., Bui D.T., Prakash I., Dholakia M., 2017. Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at himalayan area (india) using gis. Catena, 149, 52–63.

Pham B.T., Jaafari A., Prakash I., Bui D.T., 2019. A novel hybrid intelligent model of support vector machines and the multiboost ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment, 78(4), 2865–2886.

Pham B.T., Prakash I., Tien Bui D., 2018. Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology, 303, 256–270 Available from: http://www.sciencedirect.com/science/article/pii/S0169555X16309060.

Pham B.T., Tien Bui D., Indra P., Dholakia M., 2015. Landslide susceptibility assessment at a part of uttarakhand himalaya, india using gis–based statistical approach of frequency ratio method. Int J. Eng. Res. Technol., 4(11), 338–344.

Phong T.V., Phan T.T., Prakash I., Singh S.K., Shirzadi A., Chapi K., Ly H.-B., Ho L.S., Quoc N.K., Pham B.T., 2019. Landslide susceptibility modeling using different artificial intelligence methods: A case study at muong lay district, vietnam. Geocarto International, 1–24.

Pourghasemi H.R., Kerle N., 2016. Random forests and evidential belief function-based landslide susceptibility assessment in western mazandaran province, iran. Environmental earth sciences, 75(3), 185.

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.

Shirzadi A., Bui D.T., Pham B.T., Solaimani K., Chapi K., Kavian A., Shahabi H., Revhaug I., 2017a. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environmental Earth Sciences, 76(2), 60.

Shirzadi A., Tien Bui D., Pham B., Solaimani K., Chapi K., Kavian A., Shahabi H., Revhaug I., 2017c. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environmental Earth Sciences, 76.

Shirzadi, A., Chapi, K., Shahabi, H., Solaimani, K., Kavian, A. & Ahmad, B.B., 2017b. Rock fall susceptibility assessment along a mountainous road: An evaluation of bivariate statistic, analytical hierarchy process and frequency ratio. Environmental Earth Sciences, 76 (4), 152.

Thai Pham B., Shirzadi A., Shahabi H., Omidvar E., Singh S.K., Sahana M., Talebpour Asl D., Bin Ahmad B., Kim Quoc N., Lee S., 2019. Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability, 11(16), 4386.

Thai Pham B., Tien Bui D., Prakash I., 2018. Landslide susceptibility modelling using different advanced decision trees methods. Civil Engineering and Environmental Systems, 35(1–4), 139–157.

Tien Bui D., Pradhan B., Lofman O., Revhaug I., 2012a. Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and naive bayes models. Mathematical problems in Engineering, 2012.

Tien Bui D., Pradhan B., Löfman O., Revhaug I., 2012b. Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and naive bayes models. Mathematical Problems in Engineering, 2012.

Tien Bui D., Tuan T., Klempe H., Pradhan B., Revhaug I., 2015. Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 1–18.

Tran P., Trinh P., Prakash I., Singh S., Shirzadi A., Chapi K., Ly H.-B., Quoc N., Pham B., 2019. Landslide susceptibility modeling using different artificial intelligence methods: A case study at Muong Lay district, Vietnam. Geocarto International.

Xu C., Dai F., Xu X., Lee Y.H., 2012. Gis-based support vector machine modeling of earthquake-triggered landslide susceptibility in the jianjiang river watershed, china. Geomorphology, 145, 70–80.

Yao X., Tham L., Dai F., 2008. Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of hong kong, china. Geomorphology, 101(4), 572–582.

Yeon Y.-K., Han J.-G., Ryu K.H., 2010. Landslide susceptibility mapping in injae, korea, using a decision tree. Engineering Geology, 116(3–4), 274–283.