Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-loe watershed in South Sulawesi

被引:11
作者
Soma, Andang Suryana [1 ]
Kubota, Tetsuya [2 ]
机构
[1] Hasanuddin Univ, Fac Forest, Makassar, Indonesia
[2] Kyushu Univ, Fac Agr, Fukuoka, Fukuoka, Japan
关键词
Landslide; certainty factor; mitigation; South Sulawesi;
D O I
10.24259/fs.v2i1.3594
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
This study aims to build a landslide susceptibility map (LSM) by using certainty factor (CF) models for mitigation of landslide hazards and mitigation for people who live near to the forest. In the study area, the mountainous area of the Ujung-loe watersheds of South Sulawesi, Indonesia, information on landslides were derived from aerial photography using time series data images from Google Earth Pro((c)) from 2012 to 2016 and field surveys. The LSM was built by using a CF model with eleven causative factors. The results indicated that the causative factor with the highest impact on the probability of landslide occurrence is the class of change from dense vegetation to sparse vegetation (4-1), with CF value 0.95. The CF method proved to be an excellent method for producing a landslide susceptibility map for mitigation with an area under curve (AUC) success rate of 0.831, and AUC predictive rate 0.830 and 85.28% of landslides validation fell into the high to very high class. In conclusion, correlations between landslide occurrence with causative factors shows an overall highest LUC causative factor related to the class of change from dense vegetation to sparse vegetation, resulting in the highest probability of landslide occurrence. Thus, forest areas uses at these locations should prioritize maintaining dense vegetation and involving the community in protection measures to reduce the occurrence of landslide risk. LSM models that apply certainty factors can serve as guidelines for mitigation of people living in this area to pay attention to landslide hazards with high and very high landslide vulnerability and to be careful to avoid productive activities at those locations.
引用
收藏
页码:79 / 91
页数:13
相关论文
共 23 条
[1]  
Aditian A, 2017, INT J ECOL DEV, V32, P66
[2]   An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm [J].
Akgun, A. ;
Sezer, E. A. ;
Nefeslioglu, H. A. ;
Gokceoglu, C. ;
Pradhan, B. .
COMPUTERS & GEOSCIENCES, 2012, 38 (01) :23-34
[3]  
[Anonymous], 1975, MATH BIOSCI, DOI [DOI 10.1016/0025-5564(75)90047-4, 10.1016/0025-5564(75)90047-4]
[4]   Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications [J].
Ayalew, L ;
Yamagishi, H ;
Marui, H ;
Kanno, T .
ENGINEERING GEOLOGY, 2005, 81 (04) :432-445
[5]   The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan [J].
Ayalew, L ;
Yamagishi, H .
GEOMORPHOLOGY, 2005, 65 (1-2) :15-31
[6]   Slope failures in the Blue Nile basin, as seen from landscape evolution perspective [J].
Ayalew, L ;
Yamagishi, H .
GEOMORPHOLOGY, 2004, 57 (1-2) :95-116
[7]   Slope instability zonation: A comparison between certainty factor and fuzzy Dempster-Shafer approaches [J].
Binaghi, E ;
Luzi, L ;
Madella, P ;
Pergalani, F ;
Rampini, A .
NATURAL HAZARDS, 1998, 17 (01) :77-97
[8]   Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island [J].
Chau, KT ;
Chan, JE .
LANDSLIDES, 2005, 2 (04) :280-290
[9]   Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network [J].
Chauhan, Shivani ;
Sharma, Mukta ;
Arora, M. K. ;
Gupta, N. K. .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2010, 12 (05) :340-350
[10]   Validation of spatial prediction models for landslide hazard mapping [J].
Chung, CJF ;
Fabbri, AG .
NATURAL HAZARDS, 2003, 30 (03) :451-472