Skip to main content

Advertisement

SpringerLink
Log in

Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

The notion of this research is based on the two devastating earthquake events that happened on October 8, 2005, and September 24, 2019, in the regions of Azad Kashmir and Muzaffarabad. This study aims to (i) identification of the susceptible zones where landslides can occur in the future; (ii) preparation of landslide inventory maps using vector data, satellite imagery, Shuttle Radar Topographic Mission (STRM) and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) DEM; (iii) implementation of Analytical Hierarchy Process (AHP) model using weighted overlay analysis (WOA). For this purpose, key factors such as land use, faults, slope, contours, soil, and seismology maps are used to develop a landslide hazard zonation map. The output landslide susceptibility map has four susceptibility levels such as low, medium, high, and very high vulnerable zones. The results indicated that a highly susceptible landslide zone is found in the northwestern part of Muzaffarabad, which is a metropolitan region. Moreover, there are 127 active landslides are identified and collectively about 9% of the study area is very highly susceptible to future landslides. Furthermore, research findings are helpful in tactful thinking for future infrastructure development, and ecological protection in high-susceptible landslide regions in Muzaffarabad. It also helps the Government to make strategies based on any specific zones on a priority basis to reduce the casualties and destruction in future landslide events.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abrams M, Crippen R, Fujisada H (2020) ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). Remote Sens 12(7):1156

    Article  Google Scholar 

  • Ahmed KS, Basharat M, Riaz MT, Sarfraz Y, Shahzad A (2021) Geotechnical investigation and landslide susceptibility assessment along the Neelum road: a case study from Lesser Himalayas, Pakistan. Arab J Geosci 14(11):1–19

    Article  Google Scholar 

  • Ahmad MN, Shao Z, Altan O (2022) Effect of locust invasion and mitigation using remote sensing techniques: a case study of North Sindh Pakistan. Photogramm Eng Remote Sens 88(1):47–53

    Article  Google Scholar 

  • Ali S, Biermanns P, Haider R, Reicherter K (2018) Landslide susceptibility mapping by using GIS along the China–Pakistan economic corridor (Karakoram Highway), Pakistan. Nat Hazards Earth Syst Sci

  • Aslam B, Zafar A, Khalil U (2021) Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential. Soft Comput 25(21):13493–13512

    Article  Google Scholar 

  • Azam SHMN, Tahir W, Abdullah J (2021) Assessment of SRTM, ASTER and IFSAR digital elevation model (DEM) in oil palm plantation river derivation and basin delineation. In: Proceedings of the international conference on civil, offshore and environmental engineering, pp 56–65

  • Bahrami Y, Hassani H, Maghsoudi A (2021) Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran. GeoJournal 86(4):1797–1816

    Article  Google Scholar 

  • Bakiev M, Khasanov K (2021) Comparison of digital elevation models for determining the area and volume of the water reservoir. Int J Geoinform 17(1):37–45

    Article  Google Scholar 

  • Bopche L, Rege PP (2022) Landslide susceptibility mapping: an integrated approach using geographic information value, remote sensing, and weight of evidence method. Geotech Geol Eng 40:1–13

    Article  Google Scholar 

  • Bounab A, Agharroud K, El Kharim Y, El Hamdouni R, Faghloumi L (2022) The importance of investigating causative factors and training data selection for accurate landslide susceptibility assessment: the case of Ain Lahcen Commune (Tetouan, Northern Morocco). Geocarto Int, just-accepted, 1–24

  • Brock J, Schratz P, Petschko H, Muenchow J, Micu M, Brenning A (2020) The performance of landslide susceptibility models critically depends on the quality of digital elevation models. Geomat Nat Haz Risk 11(1):1075–1092

    Article  Google Scholar 

  • Burgan HI, Aksoy H (2022) Daily flow duration curve model for ungauged intermittent subbasins of gauged rivers. J Hydrol 604:127249

    Article  Google Scholar 

  • Carrera-Hernandez JJ (2021) Not all DEMs are equal: An evaluation of six globally available 30 m resolution DEMs with geodetic benchmarks and LiDAR in Mexico. Remote Sens Environ 261:112474

    Article  Google Scholar 

  • Chen Z, Ye F, Fu W, Ke Y, Hong H (2020) The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China. Nat Hazards 101(3):853–877

    Article  Google Scholar 

  • Devara M, Tiwari A, Dwivedi R (2021) Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis. Geomat Nat Hazards Risk 12(1):675–693

    Article  Google Scholar 

  • Du J, Glade T, Woldai T, Chai B, Zeng B (2020) Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Eng Geol 270:105572

    Article  Google Scholar 

  • Fang Z, Wang Y, Peng L, Hong H (2020) Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Comput Geosci 139:104470

    Article  Google Scholar 

  • Farooq S, Akram MS (2021) Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas. Arab J Geosci 14(10):1–16

    Article  Google Scholar 

  • Ghayour L, Neshat A, Paryani S, Shahabi H, Shirzadi A, Chen W, Al-Ansari N, Geertsema M, Pourmehdi Amiri M, Gholamnia M (2021) Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms. Remote Sens 13(7):1349

    Article  Google Scholar 

  • Ha H, Bui QD, Nguyen HD, Pham BT, Lai TD, Luu C (2022) A practical approach to flood hazard, vulnerability, and risk assessing and mapping for Quang Binh province, Vietnam. Environ Dev Sustain 1–30

  • Hakim WL, Rezaie F, Nur AS, Panahi M, Khosravi K, Lee C-W, Lee S (2022) Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea. J Environ Manag 305:114367

    Article  Google Scholar 

  • Hemati M, Hasanlou M, Mahdianpari M, Mohammadimanesh F (2021) A systematic review of landsat data for change detection applications: 50 years of monitoring the earth. Remote Sens 13(15):2869

    Article  Google Scholar 

  • Huang Y, Xu C, Zhang X, Xue C, Wang S (2021) An updated database and spatial distribution of landslides triggered by the Milin, Tibet Mw6.4 Earthquake of 18 November 2017. J Earth Sci 32(5):1069–1078

    Article  Google Scholar 

  • Jamali A (2021) Improving land use land cover mapping of a neural network with three optimizers of multi-verse optimizer, genetic algorithm, and derivative-free function. Egypt J Remote Sens Space Sci 24(3):373–390

    Google Scholar 

  • Kahal AY, Abdelrahman K, Alfaifi HJ, Yahya MMA (2021) Landslide hazard assessment of the Neom promising city, northwestern Saudi Arabia: an integrated approach. J King Saud Univ Sci 33(2):101279

    Article  Google Scholar 

  • Khan MY, Turab SA, Riaz MS, Atekwana EA, Muhammad S, Butt NA, Abbas SM, Zafar WA, Ohenhen LO (2021) Investigation of coseismic liquefaction-induced ground deformation associated with the 2019 Mw 5.8 Mirpur, Pakistan, earthquake using near-surface electrical resistivity tomography and geological data. Near Surf Geophys 19(Near‐Surface Geophysics for Geohazard Assessment):169–182

    Article  Google Scholar 

  • Kumar P, Mital A, Ray PKC, Chattoraj SL (2021) Landslide hazard and risk assessment along nh-108 in parts of Lesser Himalaya, Uttarkashi, using weighted overlay method. In: Geohazards, pp 163–180. Springer

  • Lau PHM, Zawawi AA (2021) Analysis of landslide occurrence using DTM-based weighted overlay: a case study in tropical mountainous forest of Cameron Highlands, Malaysia. Environ Nat Resour J 19(5):358–370

    Google Scholar 

  • Long DV, Cong NC, Cuong NT, Binh NQ, Phuoc VND (2022) An assessment of terrain quality and selection model in developing landslide susceptibility map—a case study in mountainous areas of Quang Ngai Province, Vietnam. In: Modern mechanics and applications, pp 959–970. Springer

  • Mahmoody Vanolya N, Jelokhani-Niaraki M (2021) The use of subjective–objective weights in GIS-based multi-criteria decision analysis for flood hazard assessment: a case study in Mazandaran, Iran. GeoJournal 86(1):379–398

    Article  Google Scholar 

  • Mao Z, Shi S, Li H, Zhong J, Sun J (2022) Landslide susceptibility assessment using triangular fuzzy number-analytic hierarchy processing (TFN-AHP), contributing weight (CW) and random forest weighted frequency ratio (RF weighted FR) at the Pengyang county, Northwest China. Environ Earth Sci 81(3):1–33

    Article  Google Scholar 

  • Meghanadh D, Maurya VK, Tiwari A, Dwivedi R (2022) A multi-criteria landslide susceptibility mapping using deep multi-layer perceptron network: a case study of Srinagar-Rudraprayag region (India). Adv Space Res 69(4):1883–1893

    Article  Google Scholar 

  • Moragues S, Lenzano MG, Lanfri M, Moreiras S, Lannutti E, Lenzano L (2021) Analytic hierarchy process applied to landslide susceptibility mapping of the North Branch of Argentino Lake, Argentina. Nat Hazards 105(1):915–941

    Article  Google Scholar 

  • Mori F, Gena A, Mendicelli A, Naso G, Spina D (2020) Seismic emergency system evaluation: the role of seismic hazard and local effects. Eng Geol 270:105587

    Article  Google Scholar 

  • Osako LS (2021) Updating landslide inventory maps using high resolution digital orthophotos and Digital Surface and Elevation modeling: the case study of Brusque city, Santa Catarina State, Brazil. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3:251–255

    Article  Google Scholar 

  • Pakistan Meteorological Department. (n.d.)

  • Panchal S, Shrivastava AK (2022) Landslide hazard assessment using analytic hierarchy process (AHP): a case study of National Highway 5 in India. Ain Shams Eng J 13(3):101626

    Article  Google Scholar 

  • Papathanassiou G, Valkaniotis S, Ganas A, Stampolidis A, Rapti D, Caputo R (2022) Floodplain evolution and its influence on liquefaction clustering: the case study of March 2021 Thessaly, Greece, seismic sequence. Eng Geol 298:106542

    Article  Google Scholar 

  • Pham QB, Chandra Pal S, Chakrabortty R, Saha A, Janizadeh S, Ahmadi K, Khedher KM, Anh DT, Tiefenbacher JP, Bannari A (2021) Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes. Geocarto Int 1–27

  • Polat A (2021) An innovative, fast method for landslide susceptibility mapping using GIS-based LSAT toolbox. Environ Earth Sci 80(6):1–18

    Article  Google Scholar 

  • Pudasaini SP, Krautblatter M (2021) The landslide velocity. Earth Surf Dyn Discuss 1–34

  • Rabby YW, Li Y, Abedin J, Sabrina S (2022) Impact of land use/land cover change on landslide susceptibility in Rangamati municipality of Rangamati District, Bangladesh. ISPRS Int J Geo Inf 11(2):89

    Article  Google Scholar 

  • Ram P, Gupta V (2022) Landslide hazard, vulnerability, and risk assessment (HVRA), Mussoorie township, lesser himalaya, India. Environ Dev Sustain 24(1):473–501

    Article  Google Scholar 

  • Roccati A, Paliaga G, Luino F, Faccini F, Turconi L (2021) GIS-based landslide susceptibility mapping for land use planning and risk assessment. Land 10(2):162

    Article  Google Scholar 

  • Saleem J, Ahmad SS, Butt A (2020) Hazard risk assessment of landslide-prone sub-Himalayan region by employing geospatial modeling approach. Nat Hazards 102(3):1497–1514

    Article  Google Scholar 

  • Senouci R, Taibi N-E, Teodoro AC, Duarte L, Mansour H, Yahia Meddah R (2021) GIS-based expert knowledge for landslide susceptibility mapping (LSM): case of mostaganem coast district, west of Algeria. Sustainability 13(2):630

    Article  Google Scholar 

  • Sifa SF, Mahmud T, Tarin MA, Haque DME (2020) Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh. Geol Ecol Landsc 4(3):222–235

    Article  Google Scholar 

  • Sim KB, Lee ML, Wong SY (2022) A review of landslide acceptable risk and tolerable risk. Geoenviron Disasters 9(1):1–17

    Article  Google Scholar 

  • Sun D, Xu J, Wen H, Wang D (2021) Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: a comparison between logistic regression and random forest. Eng Geol 281:105972

    Article  Google Scholar 

  • Sur U, Singh P, Meena SR (2020) Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data. Geomat Nat Haz Risk 11(1):2176–2209

    Article  Google Scholar 

  • Tiwari A, Ajmera S (2021) Land suitability assessment for agriculture using analytical hierarchy process and weighted overlay analysis in ArcGIS ModelBuilder. In: Recent trends in civil engineering, pp 735–762. Springer

  • Wang H, Zhang L, Luo H, He J, Cheung RWM (2021a) AI-powered landslide susceptibility assessment in Hong Kong. Eng Geol 288:106103

    Article  Google Scholar 

  • Wang Z, Xu J, Wang Y, He X (2021b) Study of the key technology on the Geo-hazard spatial information sharing platform in Meizoseismal Region of Wenchuan Earthquake Zone. J Ambient Intell Humaniz Comput 12(1):1011–1022

    Article  Google Scholar 

  • Xu S, Zhang M, Ma Y, Liu J, Wang Y, Ma X, Chen J (2021) Multiclassification method of landslide risk assessment in consideration of disaster levels: a case study of Xianyang City, Shaanxi Province. ISPRS Int J Geo Inf 10(10):646

    Article  Google Scholar 

  • Yan Y, Zhuang Q, Zan C, Ren J, Yang L, Wen Y, Zeng S, Zhang Q, Kong L (2021) Using the Google Earth Engine to rapidly monitor impacts of geohazards on ecological quality in highly susceptible areas. Ecol Ind 132:108258

    Article  Google Scholar 

  • Zhang Y, Wu W, Qin Y, Lin Z, Zhang G, Chen R, Song Y, Lang T, Zhou X, Huangfu W (2020) Mapping landslide hazard risk using random forest algorithm in Guixi, Jiangxi, China. ISPRS Int J Geo Inf 9(11):695

    Article  Google Scholar 

Download references

Funding

It is declared that the authors have not received any funding for this work.

Author information

Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Muhammad Nasar Ahmad, Zhenfeng Shao, Rana Waqar Aslam & Israr Ahmad

  2. Natural Resources Development Center of Jiangxi Province, No. 66, Tuanjie Road, Nanchang City, 330025, China

    Ming Liao

  3. Zhuhai Orbita Aerospace Science and Technology Co., Ltd, Orbita Tech Park, 1 Baisha Road, TangJia DongAn, Zhuhai, 519080, Guangdong, China

    Xianyi Li

  4. Guangzhou Urban Planning and Design Survey Research Institute, 10 Jianshe Avenue, Guangzhou, 440100, Guangdong, China

    Yang Song

Authors
  1. Muhammad Nasar Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenfeng Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rana Waqar Aslam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Israr Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xianyi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Nasar Ahmad.

Ethics declarations

Competing interests

The authors have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmad, M.N., Shao, Z., Aslam, R.W. et al. Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan. Stoch Environ Res Risk Assess 36, 4041–4056 (2022). https://doi.org/10.1007/s00477-022-02245-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-022-02245-8

Keywords

Search

Navigation