GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

被引:5
|
作者
Makonyo, Michael [1 ]
Zahor, Zahor [2 ]
机构
[1] Univ Dodoma, Coll Earth Sci & Engn, Dept Geol, Dodoma, Tanzania
[2] Univ Dar Es Salaam, Dept Geog, Dar Es Salaam, Tanzania
关键词
Landslides; GIS; MCDA; Lushoto; Tanzania; ANALYTIC HIERARCHY PROCESS; LOGISTIC-REGRESSION; DECISION TREE; RANDOM FOREST; MOUNT ELGON; MODELS; EARTHQUAKE; PREDICTION; SECTION; AFRICA;
D O I
10.1007/s11069-023-06038-2
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Landslides are becoming increasingly widespread, claiming tens of thousands of fatalities, hundreds of thousands of injuries, and billions of dollars in economic losses each year. Thus, studies for geographically locating landslides, vulnerable areas have been increasingly relevant in recent decades. This research is aimed at integrating Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate landslides susceptibility areas of Lushoto district, Tanzania. RS assisted in providing remote datasets including; Digital Elevation Models (DEMs), Landsat 8 OLI imageries, and past spatially distributed landslides coordinate with the use of a handheld Global Position System (GPS) receiver, while various GIS analysis techniques were used in the preparation and analysis of landslides influencing factors hence, generating landslides susceptibility areas index values. However, rainfall, slope angle, elevation, soil type, lithology, proximity to roads, rivers, faults, and Normalized Difference Vegetation Index (NDVI) factors were found to have a direct influence on the occurrence of landslides in the study area. These factors were evaluated, weighted, and ranked using Analytical Hierarchy Process (AHP) technique in which a 0.086 (8.6%) Consistency Ratio (CR) was attained (highly accepted). Findings reveal that rainfall (29.97%), slopes' angle (21.72%), elevation (15.68%), and soil types (11.77%) were found to have high influence on the occurrence of landslides, while proximity to faults (8.35%), lithology (4.94%), proximity to roads (3.41%), rivers (2.48%), and NDVI (1.69%) had very low influences, respectively. The overall results, obtained through Weighted Linear Combination (WLC) analysis techniques indicate that about 97669.65 Hectares (ha) of land are under very low levels of landslides susceptibility, which accounts for 24.03% of the total study area. Low susceptibility levels had 123105.84 ha (30.28%), moderate landslides susceptibility areas were found to have 140264.79 ha (34.50%), while high and very high susceptibility areas were found to cover about 45423.43 ha (11.17%) and 57.78 ha (0.01%), respectively. Furthermore, 81% overall model accuracy was obtained as computed from the Area Under the Curve (AUC) using Receiver Operating Characteristic (ROC) curve.
引用
收藏
页码:1085 / 1115
页数:31
相关论文
共 50 条
  • [1] GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania
    Michael Makonyo
    Zahor Zahor
    Natural Hazards, 2023, 118 : 1085 - 1115
  • [2] GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping
    Zhou, Suhua
    Chen, Guangqi
    Fang, Ligang
    Nie, Yunwen
    SUSTAINABILITY, 2016, 8 (04)
  • [3] GIS-based study on the susceptibility of shallow landslides: a case study of mass shallow landslides in Sanming, Fujian in 2019
    Yu, Congwei
    Liu, Kan
    Yu, Bin
    Yin, Jie
    NATURAL HAZARDS, 2023, 115 (03) : 2553 - 2575
  • [4] GIS-based study on the susceptibility of shallow landslides: a case study of mass shallow landslides in Sanming, Fujian in 2019
    Congwei Yu
    Kan Liu
    Bin Yu
    Jie Yin
    Natural Hazards, 2023, 115 : 2553 - 2575
  • [5] Identification of groundwater potential recharge zones using GIS-based multi-criteria decision analysis: A case study of semi-arid midlands Manyara fractured aquifer, North-Eastern Tanzania
    Makonyo, Michael
    Msabi, Michael M.
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2021, 23
  • [6] GIS-based landslide susceptibility mapping using analytical hierarchy process: a case study of Astore region, Pakistan
    Afzal, Nouman
    Ahmad, Adeel
    Shirazi, Safdar Ali
    Younes, Isma
    Le Thi Thu Ha
    EQA-INTERNATIONAL JOURNAL OF ENVIRONMENTAL QUALITY, 2022, 48 : 27 - 40
  • [7] GIS-based regional landslide susceptibility mapping: a case study in southern California
    He, Yiping
    Beighley, R. Edward
    EARTH SURFACE PROCESSES AND LANDFORMS, 2008, 33 (03) : 380 - 393
  • [8] GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China
    Sun, Tao
    Chen, Fei
    Zhong, Lianxiang
    Liu, Weiming
    Wang, Yun
    ORE GEOLOGY REVIEWS, 2019, 109 : 26 - 49
  • [9] GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India
    Trung-Hieu Tran
    Nguyen Duc Dam
    Jalal, Fazal E.
    Al-Ansari, Nadhir
    Ho, Lanh Si
    Tran Van Phong
    Iqbal, Mudassir
    Hiep Van Le
    Hanh Bich Thi Nguyen
    Prakash, Indra
    Binh Thai Pham
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2021, 2021
  • [10] Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China
    Wang, Weidong
    He, Zhuolei
    Han, Zheng
    Li, Yange
    Dou, Jie
    Huang, Jianling
    NATURAL HAZARDS, 2020, 103 (03) : 3239 - 3261